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Managing upper respiratory tract infections

• Related content
• Peer review
• Freya Davies , associate academic fellow 1 ,
• Nick A Francis , senior clinical research fellow 1 ,
• Jochen W L Cals , assistant professor and general practitioner 2
• 1 A1 Institute of Primary Care and Public Health, Cardiff University, UK
• 2 A2 Department of General Practice, School for Public Health and Primary Care, Maastricht University, Maastricht, The Netherlands

Antibiotics often do as much harm as good

Upper respiratory tract infection (URTI) is one of the most common problems to present to healthcare services, including general practices, walk-in centres, out of hours services, and emergency departments. Of all antibiotic prescriptions, 80% originate in primary care and more than half of these are for respiratory tract infections. 1

Having a rational evidence based approach to antibiotic prescribing is important for two reasons. Firstly, most URTIs are self limiting and will resolve without antibiotics. Antibiotic resistance is a major concern and prescribing antibiotics inappropriately exacerbates the problem. Secondly, the management of URTI uses large amounts of healthcare resources. Prescribing antibiotics medicalises these illnesses and promotes further consultations, whereas providing patients with information and empowering them to manage their own illness can lead to greater patient satisfaction and reduce the burden on the healthcare system.

Appropriate management of URTIs involves identifying the groups of patients who are most likely to benefit from antibiotic treatment while avoiding unnecessary treatment in most patients who will not benefit. You will see in the table ⇓ that patients generally benefit little from antibiotic treatment for URTIs, and the chance of benefiting from the antibiotics is often similar to the chance of having an adverse effect from them. In this article we aim to provide an overview of the evidence available to guide the initial assessment and management of URTI in adults and children older than 3 months. We focus on what can be said and done in the initial (10 minute) consultation.

Useful information for managing upper respiratory tract infections

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Assessment of URTI

When a patient presents with symptoms of an URTI you should take a medical history of their presenting symptoms and duration; symptom severity; interference with daily activities, work, or school; sleep disturbance caused by URTI symptoms; use of over the counter drugs; relevant risk factors; and comorbidities. 2 Patients with substantial comorbidities are more likely to develop complications after URTIs and it is important to consider these risk factors when making management decisions. Box 1 details the conditions that increase patients’ risk and the complications patients may develop that you need to be aware of in your assessment. As well as taking a medical history it is important to elicit the patients’ ideas and concerns about their symptoms and explore their expectations of the consultation (box 2). Getting to the bottom of the reason the patient came for a consultation will help you tailor the information you provide. Perform an appropriate clinical examination to exclude serious illness and reassure the patient that you are taking their illness seriously. Remember that children can present differently from adults and the normal ranges for clinical observations are different for adults and children. See box 3 for advice on how to structure your examination.

Box 1: High risk patients 2

Patients with upper respiratory tract infections in whom immediate antibiotic prescribing or further investigations or management should be considered:

Patients who are systemically very unwell

Patients who are at high risk of serious complications because of pre-existing comorbidity. This includes patients with substantial heart, lung, renal, liver, or neuromuscular disease; immunosuppression; cystic fibrosis; and young children who were born prematurely.

Patients with symptoms or signs suggestive of serious illness and/or complications (particularly pneumonia, mastoiditis, peritonsillar abscess, peritonsillar cellulitis, intraorbital or intracranial complications)

Box 2: Eliciting ideas, concerns, and expectations in a consultation for a sore throat

Did you have any thoughts on what was causing your symptoms? (“Well my kids always have coughs and cold so maybe I’ve picked up a bug from them”)

Is there anything in particular that you were worried about? (“The last time I had a sore throat like this it turned into tonsillitis and I needed a week off work. I can’t afford to have time off now”)

Some patients have already thought about what they were hoping to get from the consultation. Have you got any specific thoughts about what you would like to get out of this consultation? (“Anything that can make it go away quicker would be great. It’s too painful to eat very much.”)

Some people that come to see me are very keen to take antibiotics. What are your thoughts about antibiotics? (“I do always seem to get thrush after I take a course, which is a pain, but they did help with my throat last time. Maybe if I start taking them now it will stop it getting any worse?”)

Box 3: Physical examination in upper respiratory tract infection

Basic observations—temperature, heart rate, respiratory rate, capillary refill in children, oxygen saturations if available

Auscultate the chest, watching for signs of respiratory distress

Examine the ears and throat

Palpate for lymphadenopathy

The table and the following sections will provide more detail on the aetiology, differential diagnoses, red flags, management, and self care advice for the most common URTIs (common cold, acute sore throat, otitis media, rhinosinusitis, and influenza).

Common cold

The common cold is a self limiting viral illness (more than 200 different viruses have been implicated). On average, young children have six to eight colds a year and adults two to four. 3 A practical definition has been proposed 4 describing the common cold as an acute illness with some of the following symptoms: rhinitis (not caused by hay fever or allergies), sore throat (not including streptococcal pharyngitis), fever, and cough (with or without sputum).

Making the diagnosis

Patients will typically complain of a runny or blocked nose, sore throat, and sometimes cough. No specific tests are routinely carried out to diagnose the common cold. The diagnosis is based on the clinical history (as above) together with examination findings to rule out other specific respiratory conditions including streptococcal tonsillitis, sinusitis, bronchitis, pneumonia, asthma, and allergic rhinitis. 4

Acute sore throat

Acute sore throat includes pharyngitis and tonsillitis and is commonly caused by viruses or bacteria. Most cases of sore throats are self limiting—40% of patients not treated with antibiotics have no sore throat after three days and 82% recover after one week. 5

Patients consulting with a sore throat may have associated pain on swallowing; fever; and tender lymphadenopathy. Some countries use throat swabs or rapid diagnostic tests to try and detect streptococcal infection to guide antibiotic treatment. However, up to 20% of the healthy population carry group A streptococci in their throats. Many European countries, including the United Kingdom, recommend using clinical features to try and identify those at greatest risk of having streptococcal infection rather than diagnostic tests. The Centor score (temperature >38  C; absence of cough; tonsillar swelling or exudates; tender anterior cervical lymphadenopathy; one point for each positive sign) has been used for a number of years as a clinical prediction rule to estimate the probability of streptococcal pharyngitis. The National Institute for Health and Care Excellence recommend that immediate antibiotic prescribing is considered for patients scoring three and above. 2

One of the main arguments for identifying and treating streptococcal throat infections is that treatment reduces the risk of acute rheumatic fever. This is now such a rare complication in developed countries (no cases of acute rheumatic fever have been reported in any placebo controlled trials of antibiotics for sore throat since 1961), however, that it is hard to justify prescribing for this reason. Yet clinicians working with high risk groups (developing countries, certain Aboriginal groups, for example) should aim to identify those with streptococcal infection and treat them promptly with penicillin.

Glandular fever causes sore throat together with cervical lymphadenopathy, fatigue, and malaise in teenagers and young adults. The symptoms may be more severe and prolonged than with other acute sore throats. It is caused by Epstein-Barr virus and is self limiting. As such, it does not generally require diagnostic testing, but it can be detected with an Epstein-Barr virus serology blood test to guide prognosis. Advice to avoid contact sports for six weeks should be given as it can cause splenomegaly, with the risk of splenic injury

Acute otitis media

Acute otitis media is most common in young children, and 30% of children under 3 visit their general practitioner (GP) with an episode each year. 6

The diagnosis of acute otitis media can be difficult. Ear pain of a sudden onset is the most important diagnostic feature in the medical history. 7 Other symptoms include fever, irritability, anorexia, nausea, vomiting, and otorrhea (although otitis externa is a more common cause of otorrhea). By definition, acute otitis media can only be present if a middle ear effusion exists, which will cause a conductive hearing loss on the affected side and can cause the tympanic membrane to appear cloudy, bulging, or distinctly red. If the tympanic membrane is of a normal colour, acute otitis media is unlikely. 7 Tympanometry or pneumatic otoscopy are more accurate for assessing a middle ear effusion, but are not commonly carried out in the UK. Fluid caused by a perforation of the tympanic membrane might also be visible in the external auditory canal.

Acute rhinosinusitis

Acute rhinosinusitis is defined as an acute infection of the nasal passages and the paranasal sinuses. It is often caused by a viral URTI and only 0.5 to 2% of cases are estimated to be complicated by a bacterial infection. 8

This is made on the basis of clinical symptoms only. These typically include purulent nasal discharge, unilateral facial pain or pressure, pain when bending forward, pain in the upper teeth or when chewing, and postnasal drip.

Seasonal influenza is an acute viral illness which causes annual epidemics that peak during winter in temperate climates. 9 An estimated 12 000 people in England and Wales die each year from seasonal flu. 10 These deaths are usually among people aged over 65. As a result an annual influenza vaccination (on the basis of the predominant circulating strains) is offered to all people in the UK over the age of 65 as well as to those with chronic medical problems (see high risk groups for influenza in the table) that put them at increased risk of complications.

Diagnosis is usually made on the basis of the clinical history, although laboratory testing of nasal swabs can be performed during outbreaks. Typical symptoms of influenza include a sudden onset of symptoms, a high fever, cough (usually dry), headache, muscle and joint pain, severe malaise (patients are often confined to bed), sore throat, and runny nose. 9 No single symptom is specific enough to differentiate influenza from other respiratory illnesses, 11 and many other viruses can cause an influenza-like illness.

Management of URTI

Patients can present with an URTI for many reasons. They might be looking for reassurance, analgesia, advice on self care, confirmation of the diagnosis, a sick note, or a prescription for antibiotics or other drugs. Effectively eliciting the patient’s expectations and providing a thorough clinical assessment is important. When you have accurately established the patient’s concerns and motivation for consulting, you can tailor the information you provide. Information that patients frequently find helpful includes the likely cause and duration of their symptoms, appropriate self care advice, and advice on when to re-consult. Information leaflets are available to help this process. Research has shown that when consultations are conducted in this way fewer antibiotics are prescribed, without a reduction in patient satisfaction. 12 As the table shows, there are many situations in which an antibiotic prescription is not the most appropriate treatment for the patient.

In practice, the higher the number needed to treat, the less likely it is that the patient you treat will have any benefit from the treatment. You may use this number to help explain to patients why you think antibiotics will not help them. For example, “If I give 16 people with an earache like yours antibiotics, only one person will get better quicker as a result. The other 15 people will not benefit and also have a risk of experiencing side effects.”

Antivirals for influenza

A recent Cochrane review attempted to clarify the role of the antiviral drugs oseltamivir and zanamivir in the treatment of otherwise healthy adults and children with influenza or influenza-like illness. 25 The authors were unable to obtain many clinical study reports from trials conducted by the manufacturers, and concluded that there was a high risk of publication bias. They found evidence for a small improvement in symptoms but were unable to draw conclusions about complications or transmission. Antiviral drugs may be recommended during influenza pandemics. Seek local advice.

Self care advice

All patients presenting with an URTI should be given advice on the analgesia they can take to relieve their symptoms. Both paracetamol and ibuprofen have useful analgesic and antipyretic effects. The use of over the counter cough medicines for children aged under 6 is no longer recommended in the UK owing to the concern about possible side effects and lack of proven benefit.

Patients have often developed their own home remedies for URTI symptoms and discussing these may also be helpful in promoting self care, especially if they are harmless. Honey is a traditional remedy that may provide some relief for acute cough in children. 23

Doctors often recommend that patients increase their fluid intake when they have an URTI. The possible benefits of this include reducing mucus viscosity and replacing fluid losses that are a result of rapid breathing or fever. There is currently no good quality evidence to support this advice, however, or to assess any possible side effects from increased fluid intake—for example, hyponatraemia. 24

Safety netting

Offer the patient advice on the likely duration of their symptoms (table). On the basis of this, advise patients about when they may wish to re-consult if their symptoms are not resolving as expected. Patients should be informed of the “red flag” symptoms to look out for and told that if these develop or they feel their condition is worsening they should seek medical attention. Leaflets can be used as a take home reminder for patients (box 4). In some situations a delayed prescribing strategy may be used (table).

A thorough history and clinical examination will allow you to separate uncomplicated URTIs in healthy patients from those patients with red flag symptoms or in high risk groups. In high risk patients and in cases where the evidence above suggests antibiotics are likely to be useful, a prescription should be considered.

In otherwise healthy patients URTIs are usually self limiting. These patients will benefit most from a consultation that addresses their concerns and offers them ways to effectively manage their symptoms. Prescribing antibiotics in these cases may cause adverse effects with no benefit for the patient, and it increases the risk of the development of antibiotic resistant bacteria.

Although consultations about URTI might initially seem mundane, they can be challenging and require skill to perform well. Developing rapport with the patient and providing reassurance without always needing to resort to a prescription can be very satisfying for doctors. Using an evidence based approach to antibiotic treatment of URTI will ensure the right patients receive the antibiotic therapy they need. It will also reduce the number of unnecessary prescriptions for antibiotics. This will increase self management and patient empowerment, while decreasing the chance of adverse events and helping to combat antibiotic resistance.

Box 4: Sources of patient information

Why no antibiotic? www.patient.co.uk/health/why-no-antibiotic . Provides a simple explanation of why antibiotics are not required. Also links to the Health Protection Agency leaflet on the same topic

When should I worry? www.whenshouldiworry.com . A booklet designed by Cardiff University researchers for doctors to use with parents of children presenting with respiratory tract infections

Originally published as: Student BMJ 2013;21:f2859

Competing interests: None declared.

Provenance and peer review: Commissioned; externally peer reviewed.

• ↵ Royal College of General Practitioners. Target antibiotics toolkit. www.rcgp.org.uk/clinical-and-research/target-antibiotics-toolkit.aspx
• ↵ National Institute for Health and Clinical Excellence. Respiratory tract infections—antibiotic prescribing. NICE, 2008. www.nice.org.uk/nicemedia/pdf/CG69FullGuideline.pdf
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• ↵ O’Neill P, Roberts T, Bradley Stevenson C. Otitis media in children (acute). Clin Evid Online. 2007. www.ncbi.nlm.nih.gov/pmc/articles/PMC2943821/
• ↵ Rothman R, Owens T, Simel DL. Does this child have acute otitis media? JAMA 2003 ; 290 : 1633 -40. OpenUrl CrossRef PubMed Web of Science
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• ↵ World Health Organization. Influenza (seasonal). 2009. www.who.int/mediacentre/factsheets/fs211/en/
• ↵ Royal College of General Practitioners. Preparing for pandemic influenza. 2008. www.rcgp.org.uk/policy/rcgp-policy-areas/pandemic-flu.aspx
• ↵ Michiels B, Thomas I, Van Royen P, Coenen S. Clinical prediction rules combining signs, symptoms and epidemiological context to distinguish influenza from influenza-like illnesses in primary care: a cross sectional study. BMC Fam Pract 2011 ; 12 : 4 . OpenUrl CrossRef PubMed
• ↵ Francis NA, Butler CC, Hood K, Simpson S, Wood F, Nuttall. Effect of using an interactive booklet about childhood respiratory tract infections in primary care consultations on reconsulting and antibiotic prescribing: a cluster randomised controlled trial. BMJ 2009 ; 339 : b2885 . OpenUrl Abstract / FREE Full Text
• Hemilä H, Chalker E, Douglas B. Vitamin C for preventing and treating the common cold. Cochrane Database Syst Rev 2007 ;(3):CD000980. http://onlinelibrary.wiley.com/doi/10.1002/14651858.CD000980.pub3/abstract
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• Singh M, Das RR. Zinc for the common cold. Cochrane Database Syst Rev 2011 ;(2):CD001364. http://onlinelibrary.wiley.com/doi/10.1002/14651858.CD001364.pub3/abstract
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• Venekamp RP, Sanders S, Glasziou PP, Del Mar CB, Rovers MM. Antibiotics for acute otitis media in children. Cochrane Database Syst Rev 2013 ; 1 : CD000219 . OpenUrl PubMed
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• Zalmanovici Trestioreanu A, Yaphe J. Intranasal steroids for acute sinusitis. Cochrane Database Syst Rev 2009 ;(4):CD005149. http://onlinelibrary.wiley.com/doi/10.1002/14651858.CD005149.pub3/abstract
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• National Institute for Health and Clinical Excellence. Oseltamivir, amantadine (review) and zanamivir for the prophylaxis of influenza. 2008. www.nice.org.uk/nicemedia/live/12060/42037/42037.pdf
• ↵ Oduwole O, Meremikwu MM, Oyo-Ita A, Udoh EE. Honey for acute cough in children. Cochrane Database Syst Rev 2012 ; 3 : CD007094 . OpenUrl PubMed
• ↵ Guppy MPB, Mickan SM, Del Mar CB, Thorning S, Rack A. Advising patients to increase fluid intake for treating acute respiratory infections. Cochrane Database Syst Rev 2011 ;(2):CD004419. http://onlinelibrary.wiley.com/doi/10.1002/14651858.CD004419.pub3/abstract
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The treatment of mild upper respiratory tract infections - a position paper with recommendations for best practice

Affiliations.

• 1 School of Psychology, Cardiff University, Cardiff, UK.
• 2 Centre of Allergy, Respiratory and Sleep Medicine, Maingau Clinic of the Red Cross, Frankfurt am Main, Germany.
• 3 Department of Otorhinolaryngology, University Hospital Marburg, Philipps University Marburg, Marburg, Germany.
• 4 Institute of Pneumology, University of Cologne, Cologne, Germany.
• 5 Bethanien Hospital, Clinic of Pneumology and Allergology, Centre for Sleep Medicine and Respiratory Care, Solingen, Germany.
• 6 Spanish Society Clinical Family and Community Pharmacy (SEFAC), Paseo de las Delicias, Madrid, Spain.
• 7 Istituti di Ricovero e Cura a Carattere Scientifico, Ospedale Policlinico San Martino, Genova, Italy.
• 8 Università di Genova, (DIMI), Genova, Italy.
• 9 Hull York Medical School, University of Hull, Hull, UK.
• PMID: 37521107
• PMCID: PMC10379023
• DOI: 10.7573/dic.2023-4-2

Following the waning severity of COVID-19 due to vaccination and the development of immunity, the current variants of SARS-CoV-2 often lead to mild upper respiratory tract infections (MURTIs), suggesting it is an appropriate time to review the pathogenesis and treatment of such illnesses. The present article reviews the diverse causes of MURTIs and the mechanisms leading to symptomatic illness. Different symptoms of MURTIs develop in a staggered manner and require targeted symptomatic treatment. A wide variety of remedies for home treatment is available, including over-the-counter drugs and plant-derived substances. Recent pharmacological research has increased the understanding of molecular effects, and clinical studies have shown the efficacy of certain herbal remedies. However, the use of subjective endpoints in these clinical studies may suggest limited validity of the results. In this position paper, the importance of patient-centric outcomes, including a subjective perception of improved well-being, is emphasized. A best practice approach for the management of MURTIs, in which pharmacists and physicians create an improved multi-professional healthcare setting and provide healthcare education to patients, is proposed. Pharmacists act as first-line consultants and provide patients with remedies, considering the individual patient's preferences towards chemical or plant-derived drugs and providing advice for self-monitoring. Physicians act as second-line consultants if symptoms worsen and subsequently initiate appropriate therapies. In conclusion, general awareness of MURTIs should be increased amongst medical professionals and patients, thus improving their management.

Keywords: COVID-19 pandemic; common cold; coronavirus; cough; holistic health; mild upper respiratory tract infections; pharmacist; rhinitis; rhinovirus; sleep.

Copyright © 2023 Smith A, Kardos P, Pfaar O, Randerath W, Estrada Riolobos G, Braido F, Sadofsky L.

Publication types

• Open access
• Published: 27 April 2024

The relationship between changes in peak expiratory flow and asthma exacerbations in asthmatic children

• Xiongbin Chen 1   na1 ,
• Peng Han 1   na1 ,
• Yan Kong 1 &
• Kunling Shen 1 , 2  

BMC Pediatrics volume  24 , Article number:  284 ( 2024 ) Cite this article

Metrics details

Asthma is one of the most common chronic airway diseases in children. Preventing asthma exacerbation is one of the objectives of all asthma action plans. In patients with poor perception, it is difficult to identify acute asthma exacerbations by clinical asthma score, asthma control test or asthma control questionnaire. The aim of this study is to analyze whether children with asthma have changes in peak expiratory flow(PEF)before an acute asthma exacerbation and to evaluate the relationship between PEF and asthma exacerbation.

Basic information (including sex, age, atopy, etc.) and clinical information of asthmatic children who registered in the Electronic China Children’s Asthma Action Plan (e-CCAAP) from 1 September 2017 to 31 August 2021 were collected. Subjects with 14 consecutive days of PEF measurements were eligible. Subjects in this study were divided into an exacerbation group and a control group. We analyzed the relationship between changes in PEF% pred and the presence of asthma symptoms.

A total of 194 children with asthma who met the inclusion criteria were included, including 144 males (74.2%) and 50 females (25.8%), with a male-to-female ratio of 2.88:1. The mean age of the subjects was 9.51 ± 2.5 years. There were no significant differences in sex, age, allergy history or baseline PEF between the two groups. In children with and without a history of allergy, there was no significant difference between the variation in PEF at 14 days. Patients who only had a reduced in PEF but no symptoms of asthma exacerbation had the greatest reduction in PEF compared to the other groups. The most common cause of acute exacerbations of asthma is upper respiratory tract infection. Among the causes of acute exacerbations of asthma, the variation in PEF caused by air pollution was significantly higher than that of other causes ( P  < 0.05). In acute exacerbations, the decrease in PEF was significantly greater in the exacerbation group than in the control group. In children with asthma symptoms, there was a decrease in PEF approximately 1.34 days before the onset of symptoms.

Children with asthma show a decrease in PEF 1.34 days before the onset of asthma symptoms. We recommend that asthmatic children who show a decrease in PEF should step-up asthma therapy. The most common cause of acute exacerbations of asthma was upper respiratory tract infections, and the variation in PEF caused by air pollution was significantly higher than that caused by other factors.

Peer Review reports

In many countries, childhood asthma is a major public health problem [ 1 ]. As of 2018, there were approximately 350 million people with asthma worldwide (including children, adolescents and adults) [ 2 ]. The Third National Epidemiological Survey of Childhood Asthma in China found that the prevalence of asthma among urban children aged 0–14 years was 3.02% in 2010, compared with 1.09% and 1.97% in 1990 and 2000, respectively [ 3 ]. In China, the incidence of childhood asthma is increasing at a rate of more than 50% per decade. According to the national report in China, the overall prevalence of asthma in adults is 4.2% (95% CI 3.1–5.6), representing 45.7 million people [ 4 ].

A global multicenter study showed high rates of poorly controlled asthma in children (6–7 years), adolescents (13–14 years), and adults (≥ 19 years), with particularly high rates in children [ 5 ]. The Global Initiative for Asthma (GINA) states that the key to asthma management is the prevention of acute exacerbations and that early identification of asthma exacerbations, and timely intervention can reduce the burden of disease [ 6 ]. However, asthma exacerbations usually occur without any signs, and many children with asthma can breathe normally for weeks or months between exacerbations. Individuals and families do not have an accurate perception of symptoms. We need objective parameters to describe the severity of asthma. Peak expiratory flow (PEF) is an objective pulmonary parameter measured by an instrument that gives a true picture of a child’s airways. GINA recommends PEF testing and regular follow-up for children over the age of 5 with asthma prior to diagnosis and initiation of controller therapy [ 6 ].

It had been shown that patients’ perceptions of asthma symptoms or the severity of exacerbations vary and that difficulties with physical sensations and emotional expression were often associated with severe asthma, even fatal acute asthma attacks [ 7 ]. In children, the need for additional objective parameters to describe the status and severity of asthma is reinforced by the poor perception of asthma symptoms and the fact that children often have difficulty expressing themselves. Studies have shown that forced expiratory volume in one second (FEV 1 ) does not change significantly in most school-aged children, regardless of whether they have an acute asthma attack, and FEV 1 is not associated with asthma severity as defined by symptoms [ 8 , 9 , 10 ]. A study in adults suggests that PEF may be a useful method for monitoring trends in asthma exacerbations and quantifying asthma control history [ 11 ]. Several studies have shown the effectiveness of PEF-based asthma education and self-management programs in reducing emergency department visits and hospitalizations due to asthma exacerbations [ 12 , 13 , 14 ]. Compared with FEV1, PEF can be self-tested at home, making it more feasible and easier to comply with. Numerous guidelines suggest that PEF is a valuable, readily available measure that is well suited to monitoring long-term trends in asthma control [ 6 , 15 ]. The PEF test helps to identify the variable nature of airflow limitation (obstruction) in patients, which is a central feature of asthma.

Many international guidelines recommend the provision of a written asthma action plan (WAAP) to guide patients in recognizing and responding to worsening asthma symptoms to reduce acute asthma exacerbations [ 6 , 16 , 17 ]. Almost all written asthma action plans (WAAPs) include long-term monitoring of PEF as an objective indicator of asthma control.

However, previous studies were mostly conducted in professional medical institutions under the guidance of medical professionals. It is difficult to reflect the management of asthma in family conditions. And most of the studies were limited to adults, with few studies in children.

To help healthcare professionals, children and families self-manage asthma and achieve good asthma control, we implemented the China Children’s Asthma Action Plan (CCAAP) and published an expert consensus on the clinical application of the CCAAP (in Chinese with an English abstract) [ 18 ].

This study was based on the CCAAP. We collected the children’s PEF values and combined them with the children’s symptoms to determine their asthma status. The aim of this study is to show the relationship between changes in PEF and asthma exacerbations to help children better manage their asthma.

Inclusion criteria

Registered in the electronic China Children’s Asthma Action Plan (e-CCAAP).

Age ≥ 6 and ≤ 18 years.

Children diagnosed with asthma by physicians [ 19 ].

The PEF measurements were recorded for at least 14 consecutive days.

Exclusion criteria

PEF values with significant deviations.

The information is incorrect.

We checked the children’s medical records to make sure the information was correct. For children who did not have a medical record, we would contact their parents or guardians to verify that the information was correct. If their parents or guardians could not be contacted, the information would be considered incorrect. And We defined significant deviations as PEF values that were significantly lower (e.g., below 10 L/min) or significantly higher than the baseline PEF(e.g., greater than 13,245 L/min).

Asthmatic children who registered in the e-CCAAP from 1 September 2017 to 31 August 2021 and recorded PEF measurements for at least 14 consecutive days were eligible. According to CCAAP, children were required to step up treatment when their PEF%≤80% and/or onset of asthma symptoms.

Patients with asthma exacerbations had to increase the dose of inhaled corticosteroids (ICS) by 2 to 4 times, which depending on the dose before the asthma exacerbation and the severity of the asthma and giving Salbutamol at the same time. If patients had any of the following symptoms, they needed to visit doctors:1) Heavy breathing, suffocation, dyspnea, crying, irritability, etc;2) Initial treatment with inhaled bronchodilators was ineffective or situations getting worsen. And they also can used ICS-LABA(Long-acting β-agonists) composite formulation.It was a case control study. We retrospectively analyzed their acute asthma exacerbations and PEF% pred. Basic information included sex, age, allergy history, and basic medications. History of allergy was defined as at least 1 positive for inhaled allergen (dust mites, mold, cockroaches, pets, spring/fall pollen, other allergens) or at least 1 positive for food allergen (milk, egg, wheat, nuts, seafood, soybeans, peanuts, other allergens).In our study, PEF reduction was defined as PEF% pred ≤ 80%. Subjects were divided into an exacerbation group or a control group according to asthma symptoms and PEF% pred. The characteristics of the control group were PEF% pred > 80% and no onset of asthma symptoms. The participants were asked to take PEF measurements three times in the morning and three times in the evening and to record the optimum value of the measurement. The triggers of asthma exacerbations were recorded in e-CCAAP. The exacerbation group were divided into three subgroups: (1) onset of asthma symptoms after a decrease in PEF (PEF + symptom group). (2) only a decrease in PEF (PEF group). (3) onset of asthma symptoms only (symptom group).

Definition of air pollution and climate change

In this study, the definition of air pollution was Air Quality Index (AQI)>100 [ 20 ]. The AQI is provided by the Ministry of Environmental Protection of China. The AQI is available in real time for most cities on the website and smartphone app. Climate change is mainly about colder weather, which leads to the inhalation of cold air.

Statistical analyses were performed using SPSS Statistics version 22.0. Continuous and categorical variables were presented as the mean ± standard deviation (‾×±sd) or number (percentage). We analyzed the differences in the demographic characteristics between the two groups by using a χ 2 test and two-sample t tests for proportions and continuous data, respectively. Comparisons of nonnormally distributed data were performed using the Mann‒Whitney U rank test. A two-tailed p value of 0.05 was considered to be statistically significant.

Ethics approval and consent to participate

The study was approved by the Ethics Committee of Beijing Children’s Hospital, and written informed consent was waived.

Baseline characteristics

The baseline characteristics of the two groups were summarized in Table  1 . A total of 194 subjects were included in this study. Of the 194 subjects, there were 144 males and 50 females, with a male to female ratio of 2.88:1. The mean age was 9.51 ± 2.5 years. There were 162 subjects in the exacerbation group. Of the 162 subjects, 98 were in the PEF + symptom group, 13 in the PEF group and 51 in the symptom group. There were 32 subjects in the control group. The number of subjects in the PEF + symptom group was significantly higher than that in the other groups ( P <0.05). The mean ages of the four groups were 9.56 ± 2.08, 10.69 ± 2.39, 9.41 ± 3.03, and 9 ± 2.78 years, respectively. The baseline PEF was significantly higher in the PEF group than in the other three groups ( P  = 0.014). With the exception of baseline PEF, there were no statistically significant differences in baseline characteristics between the two groups. Most subjects were from eastern China. (Table  1 ).

Impact of allergy history on PEF

The distribution of food allergens or inhalation allergens in this study was shown in Table  2 . There were no statistically significant differences between the groups for food and inhalation allergens. In children with a history of allergy, the mean PEF variation was less than that in children without a history of allergy, and their PEF decrease was less than that in children without a history of allergy when there was an onset of asthma symptoms, but the difference in PEF changes between these two groups was not statistically significant ( P  = 0.206).

Changes in PEF over 14 days in included subjects

As shown in Fig.  1 , the change in PEF% pred over 14 days in the exacerbation group (PEF + symptom group, PEF group, Symptom group) and the control group. The variation in PEF% pred was significantly greater in the PEF group than in the other groups.

Changes in PEF. Changes in PEF in four groups in 14 days. The PEF group had greater changes. Red arrow indicates onset of asthma symptom

Analysis of triggers in the exacerbation group

As shown in Fig.  2 and Table  3 , in the exacerbation group, the most common trigger for an acute exacerbation of asthma was upper respiratory tract infections. The proportion of acute exacerbations caused by upper respiratory tract infections in the PEF + symptom group, PEF group and symptom group was 44.9%, 61.5% and 49% respectively, which was significantly higher than that caused by other triggers ( P <0.0001).Changes in PEF% pred for acute exacerbations of asthma caused by different triggers were shown in Fig.  3 .

Acute exacerbations due to air pollution had significantly higher variations in PEF% pred than other triggers. Acute exacerbations of exercise-induced asthma had significantly less variating changes in PEF% pred than other triggers.

Triggers in the Exacerbation Group. Upper respiratory tract infection was the most common triggers

Changes in PEF with different triggers. Air pollution has significantly higher variations in PEF% pred than other triggers

Time to PEF changes before exacerbation

We performed a retrospective analysis of children who developed asthma symptoms after the onset of PEF changes. Ninety-eight subjects (50.5%) developed symptoms after a decrease in PEF% pred, significantly more than those who did not develop asthma symptoms after a decrease in PEF% pred (6.7%). We found that most children with asthma had changes in PEF approximately 1–3 days before the onset of asthma symptoms. Statistically, the time from PEF change to symptom onset was 1.34 days [95% CI, 1.19, 1.49].

In our study, we found that children with asthma had a decrease in PEF 1.34 days before the onset of asthma symptoms, which may be an early sign of an acute exacerbation of asthma. A randomized controlled trial in children aged 7–14 years with moderate asthma showed that the PEF threshold was 70% of the optimum for increasing inhaled steroids and 50% of the optimum for starting prednisone therapy based on the PEF action plan. There was a significant decrease in PEF approximately 1 day prior to step-up treatment [ 21 ]. A study in adults showed that asthma exacerbations were characterized by a gradual decrease in PEF over a few days, followed by more rapid changes over 2 to 3 days [ 22 ]. Many countries’ asthma action plans advocated increasing the dose of inhaled corticosteroids (ICS) or initiating “yellow zone” therapy at the early signs of an exacerbation to avoid an acute exacerbation or reduce the severity and to prevent the need for oral steroids or hospitalization. In this study, some of the subjects had a significant decrease in PEF but had no asthma symptoms. This suggested that we may delay treatment if symptoms were used as an early sign of an acute exacerbation of asthma. PEF decreased before the onset of symptoms, and as soon as a decrease in PEF is detected during daily PEF monitoring, we can initiate “yellow zone” therapy. In our study, children in the PEF group had the greatest decrease in PEF% pred, which may be related to the fact that they had a higher PEF at baseline.

This study found that the most common trigger of acute asthma exacerbations in children was upper respiratory tract infections. Infection is the main trigger for acute exacerbations of asthma in children of all ages, followed by exposure to allergens [ 23 ]. In the United States, childhood asthma morbidity decreased during the novel coronavirus epidemic compared with other periods, which may be related to reduce pathogen exposure due to the use of masks [ 24 ]. The study by Anneclaire et al. also found that the likelihood of children’s asthma worsening increased as pollen levels increased [ 25 ]. Some studies found that houses that have been painted in the past 1 year are also a risk factor for acute exacerbations of asthma [ 26 ]. We need individualized action plans to improve the management of asthma, and avoiding infections and allergens are very important measures.

In this study, we found no significant differences in food allergens and inhalation allergens between the exacerbation group and the control group. Although allergen exposure is the second most common trigger. However, a study showed that, blood eosinophils and mold sensitization were significantly associated with asthma severity [ 27 ].The study by Zoratti, E. M. et al. also indicated that severe asthma often co-clusters with highly allergic children [ 28 ].More studies are needed to confirm the relationship between allergens and PEF.

Doctors, teachers and parents need to be involved in improving asthma control in children. Natasha et al. showed that asthmatic students, teachers, and family members were involved in the study together to teach them how to identify asthma symptoms based on the Asthma Action Plan (AAP) and actions for each area. By the end of the study, all students accurately identified symptoms, AAP areas, and action steps [ 29 ]. When we promote our asthma action plan, we can consider a combined hospital-school-home model.

The advantage of this study is the simplicity and economy of long-term PEF monitoring through an electronic platform, with subjects from all over China participating in this study. Subjects can monitor the PEF anytime and anywhere, which greatly improves their compliance. The limitations of this study are as follows. First, as a retrospective study, some recall bias was inevitable. Second, PEF measurements were related to the child’s ability to breathe calmly and regularly, measurements taken at home or at school were highly arbitrary, and even PEF measurements taken under different circumstances can vary greatly. Patients with upper respiratory tract infections may develop tonsillitis, which would cause upper airway obstruction and affect PEF measurement results. This would limit the use of PEF in daily life.

In children with asthma, the PEF% pred decreased 1.34 days before the onset of asthma symptoms. Therefore, we recommend starting “yellow zone” treatment when the PEF % pred decreases during long-term PEF monitoring to prevent acute exacerbations of asthma. The most common trigger for acute exacerbations of asthma is upper respiratory tract infections. Acute exacerbations due to air pollution have significantly higher variations in PEF% pred than other triggers.

Data availability

All data generated or analysed during this study are included in this published article.

Abbreviations

peak expiratory flow

China Children’s Asthma Action Plan

forced expiratory volume in one second

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Acknowledgements

We thank Ju Yin and Baoping Xu, who provided services for data collection in respiratory department of Beijing Children’s Hospital.

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Xiongbin Chen, Peng Han contributed equally to this work.

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Respiratory Department, Beijing Children’s Hospital, Capital Medical University, China National Clinical Research Center of Respiratory Diseases, National Center for Children’s Health, Beijing, China, 100045

Xiongbin Chen, Peng Han, Yan Kong & Kunling Shen

Department of Respiratory, Shenzhen Children′s Hospital, 518038, Shenzhen, China

Kunling Shen

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Xiongbin Chen and Peng Han proposed the study and wrote the first draft. Yan Kong collected and analyzed the data. Kunling Shen guided the design of the study and helped to draft the manuscript. All authors read and approved the final manuscript.

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Chen, X., Han, P., Kong, Y. et al. The relationship between changes in peak expiratory flow and asthma exacerbations in asthmatic children. BMC Pediatr 24 , 284 (2024). https://doi.org/10.1186/s12887-024-04754-7

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Meta-analysis of the human upper respiratory tract microbiome reveals robust taxonomic associations with health and disease

• Nick Quinn-Bohmann   ORCID: orcid.org/0009-0001-5358-7322 1 , 2 ,
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The human upper respiratory tract (URT) microbiome, like the gut microbiome, varies across individuals and between health and disease states. However, study-to-study heterogeneity in reported case–control results has made the identification of consistent and generalizable URT-disease associations difficult.

In order to address this issue, we assembled 26 independent 16S rRNA gene amplicon sequencing data sets from case–control URT studies, with approximately 2–3 studies per respiratory condition and ten distinct conditions covering common chronic and acute respiratory diseases. We leveraged the healthy control data across studies to investigate URT associations with age, sex, and geographic location, in order to isolate these associations from health and disease states.

Conclusions

We found several robust genus-level associations, across multiple independent studies, with either health or disease status. We identified disease associations specific to a particular respiratory condition and associations general to all conditions. Ultimately, we reveal robust associations between the URT microbiome, health, and disease, which hold across multiple studies and can help guide follow-up work on potential URT microbiome diagnostics and therapeutics.

The human respiratory system is a complex structure, divided into the upper respiratory tract (URT) and the lower respiratory tract (LRT), and is primarily responsible for the exchange of oxygen and carbon dioxide with the atmosphere [ 1 ]. The upper respiratory tract, with an approximate surface area of 70 m 2 , is known to harbor a diverse microbial community [ 2 ]. Beginning at birth, colonization by microbes occurs through constant exposure to the surrounding environment via aspiration, inhalation, and direct contact [ 2 , 3 , 4 ]. A quasi-stable community develops over time, typically consisting of genera such as Corynebacterium and Dolosigranulum in young healthy children [ 5 ] and Corynebacterium and Staphylococcus in healthy adults [ 6 ] . The URT, consisting of the nares, nasal passages, mouth, sinuses, pharynx, and larynx, is the section of the respiratory tract most exposed to the environment and harbors the highest bacterial density [ 2 ]. Upsetting the balance of the URT microbiome may lead to opportunistic pathogen invasion and serious respiratory tract-related disease and infection [ 7 , 8 ]. Chronic respiratory diseases represent the largest disease burden worldwide, affecting over half a billion people in 2017 [ 9 ]. Pneumonia, an infection of the lungs, is a leading cause of mortality across the world, responsible for an estimated 3.2 million deaths in 2015 [ 10 ]. The likelihood of being infected by the influenza virus, another common respiratory pathogen that has caused recurrent epidemics over the past century, has been shown to be partially dependent on the composition of the URT microbiome [ 7 , 11 ]. Additional respiratory conditions, such as RSV, rhinosinusitis, and recurrent respiratory allergies, have all been linked with the disruption of the URT microbiome [ 12 , 13 , 14 ].

Maintaining a diverse commensal microbiome can be protective against the invasion of opportunistic pathogens [ 2 , 15 ]. Commensal bacteria can help to saturate metabolic niche space, preventing invasion and engraftment of potential pathogens [ 8 ]. Additionally, commensals have been shown to directly suppress viral infections through the activation of host immune responses [ 16 ]. Early exposure to certain commensal microbes can even lead to long-term immunomodulation, preventing autoimmune diseases and promoting tolerance to allergens [ 17 , 18 ]. Overall, the symbiotic relationship between the URT microbiome and the host appears critical for the maintenance of human health [ 2 , 19 ].

As with the gut microbiome, variability exists in the microbial composition of these URT communities across individuals. In addition to inter-individual heterogeneity and disease status, URT microbiome profiles may be shaped by other covariates known to impact community structure, such as age [ 1 , 7 ], and possibly others such as technical variation (e.g., sequencing methodologies), demographics, geographic location, and sex, although these associations are not well defined. Certain keystone or core taxa are well known to have a generally positive association with health, including the genera Dolosigranulum and Corynebacterium [ 20 , 21 , 22 ]. The sinonasal area is predominantly colonized by Corynebacterium and Staphylococcus [ 23 , 24 ], whereas the throat and tonsil areas are mostly colonized by Streptococcus , Fusobacterium , and Prevotella [ 25 , 26 ]. Certain species in the genera Streptococcus , Haemophilus , and Pseudomonas have been linked to negative health outcomes and disease [ 1 , 20 , 27 , 28 , 29 ]. However, respiratory illnesses are often polymicrobial, caused or facilitated by the presence of multiple organisms [ 30 ]. Identifying consistent signatures of URT health and disease has been hampered by the variability in reported results from individual case–control studies.

Here, we conducted a meta-analysis of the composition of the URT microbiome across health and disease states to identify consistent patterns that persist across independent studies in demographically and geographically divergent cohorts within and across multiple respiratory conditions. Using 16S rRNA amplicon sequencing data collected from the nasopharynx or the oropharynx across cases and controls from 26 independent studies representing 10 respiratory diseases and conditions, we observe robust associations between the relative abundance of specific genera and disease status. The diseases, conditions, or set of conditions included in the meta-analysis are as follows: asthma [ 31 , 32 , 33 ], chronic obstructive pulmonary disease (COPD) [ 34 ], COVID-19 [ 35 , 36 , 37 ], influenza [ 38 , 39 , 40 ], pneumonia [ 41 , 42 , 43 ], respiratory allergies [ 44 , 45 ], rhinosinusitis [ 46 , 47 , 48 ], respiratory syncytial virus (RSV, includes a range of conditions caused by the human respiratory syncytial virus) [ 49 , 50 , 51 ], respiratory tract infection (RTI, defined as a viral or bacterial infection of the upper or lower respiratory tract, including bronchitis) [ 52 , 53 , 54 ], and tonsillitis [ 55 , 56 ]. Knowledge of these consistent within- or across-disease associations may help guide the development of diagnostic tools and therapeutic interventions aimed at prevention or treatment of respiratory conditions.

Assembling case–control studies for a URT meta-analysis

To investigate the associations between the composition of the URT microbiome and disease susceptibility, we analyzed data collected from 26 independent case–control studies including 4706 total samples (study inclusion criteria outlined in the “ Methods ” section). Studies included in this meta-analysis had, at a minimum, publicly available 16S rRNA amplicon sequencing data and associated metadata on disease status, URT sampling site, sequencing method, and 16S rRNA hypervariable region used for amplicon sequencing. Unfortunately, additional metadata, such as age, gender, and other demographic data, were not uniformly available across all studies. Four studies included samples from both the nasopharynx and oropharynx; these samples were analyzed separately. For each study, raw data in FASTQ format were downloaded and processed through the same bioinformatic pipeline, defined in the “ Methods ” section below. All analyses were conducted at the genus level, given the phylogenetic resolution of partial 16S rRNA amplicon sequencing [ 57 ]. Details on each study included in this meta-analysis can be found in Additional file 1 : Table 1.

Alpha- and beta-diversity analyses show community-wide impacts of disease conditions

We compared URT microbiome alpha-diversity (Shannon index and Chao1 index) between disease cases and healthy controls at a per-study level. Prior to calculating diversity metrics, rarefaction to a sampling depth of 2000 reads was conducted. After rarefaction, 4536 samples remained, representing a loss of 170 samples. Due to the large compositional differences observed between the nasopharynx and oropharynx [ 40 ], diversity was investigated separately between these environments (Fig.  1 A, B). Across 20 studies sampling the nasopharynx, 7 showed significant differences in alpha-diversity as measured by the Shannon Index between cases and controls, spanning asthma, influenza, RSV, RTI, and respiratory allergies (two-tailed independent Student’s t -test, p  < 0.05). All but one (Wen et al., Influenza) of these significant relationships showed significantly higher alpha-diversity in healthy vs unhealthy samples (Fig.  1 A). Across 10 studies sampling the oropharynx, four significant differences were observed between healthy and disease groups, for asthma, influenza, pneumonia, and RTI (two-tailed independent Student’s t -test, p  < 0.05). Again, all but one (Wen et al., Influenza) showed significantly higher alpha-diversity in healthy vs unhealthy samples (Fig.  1 B). Similar relationships were observed when examining taxonomic richness (Chao1 index). Among studies sampling the nasopharyngeal microbiome, 10 of 20 showed significant differences between cases and controls, including six that were also significantly enriched in the same direction in the Shannon index (Fig.  1 C, two-tailed independent Student’s t -test, p  < 0.05). For oropharyngeal samples, 8 of 10 studies showed significant enrichment between cases and controls (Fig.  1 D, two-tailed independent Student’s t -test, p  < 0.05). It has not been well established whether or not alpha-diversity of the URT microbiome is associated with disease [ 58 ]. These results indicate that changes in alpha-diversity of the URT microbiome during respiratory disease are disease-specific, not wholly consistent across studies, and lean toward an overall decline in diversity in the disease state.

Alpha-diversity between disease cases and healthy controls for each respiratory condition. Alpha-diversity (Shannon Index or Chao1 index) is shown between cases and controls for each study. Shannon diversity for both samples from the nasopharynx ( N  = 3223) ( A ) and the oropharynx ( N  = 1313) ( B ) was calculated, as well as Chao1 richness for the nasopharynx ( N  = 3223) ( C ) and the oropharynx ( N  = 1313) ( D ). Significant differences between cases and controls were determined by independent Student’s t -test, two-tailed p -value * =  p  < 0.05, ** =  p  < 0.01, *** =  p  < 0.001

We calculated Bray–Curtis distances at the genus level, to investigate beta-diversity patterns across studies (Fig.  2 ). For these analyses, all samples from all studies were pooled after rarefaction, including samples from both URT sampling sites. Analysis by PERMANOVA showed significant differences in beta-diversity between samples collected from two different URT sites, the nasopharynx and the oropharynx (Fig.  2 A, PERMANOVA p  < 0.05). This is consistent with findings that the nasopharyngeal and oropharyngeal microbiomes are compositionally distinct [ 59 ]. Additionally, a significant difference was observed between samples taken from different continents, which pushes against prior assertions that the URT microbiome is generally consistent across geographic regions [ 60 ] (Fig.  2 C, PERMANOVA p  < 0.05). As expected, significant differences were observed in Bray–Curtis dissimilarity in cases relative to controls, as well as between disease conditions (Fig.  2 B, D, PERMANOVA p  < 0.05). Finally, significant differences in beta-diversity were observed between sequencing methods, and 16S rRNA hypervariable region used for amplicon sequencing (Fig.  2 E, F, PERMANOVA p  < 0.05). These results indicated that any further analysis would necessarily require consideration of these confounding variables.

Principal coordinate analysis (PCoA) plots of genus-level Bray–Curtis distances along the first two principal coordinatess across all samples. Within subplots, each point represents a single sample ( N  = 4536). Beta-diversity was significantly associated with disease status ( A ), URT sampling site ( B ), geographic region ( C ), disease type ( D ), sequencing method ( E ), and 16S rRNA hypervariable region used for amplicon sequencing ( F ). Significant differences in beta-diversity were observed for all six parameters, as determined by PERMANOVA, p  < 0.001 in all cases

Covariates are significantly associated with URT microbiome composition

Next, we aimed to examine the influence of geographic regions on taxonomic composition in healthy URT samples. Using metadata on geographic regions available for all studies, multiple regression was run for each genus to estimate the effect of geographic region (Europe, N. America, S. America, Africa, Asia, or Oceania) on centered log-ratio transformed relative abundance data, correcting for URT sampling site, sequencing method, and hypervariable region. Ninety-eight genera showed significant association with at least one geographic region (Fig.  3 , multiple regression, FDR-corrected p -value < 0.05). FDR-corrected p -values and mean relative abundances of each taxon per geographic region can be found in the supplementary material (Additional file 1 : Table 2).

Significant differences in centered log-ratio (CLR) relative abundance of prevalent taxa between geographic regions and ages across healthy control samples. Heatmaps show significant taxonomic associations with geographic location and age in healthy controls. In both, mean CLR-transformed relative abundance is shown via color encoding, with red indicating higher CLR abundance and blue indicating lower CLR abundance. A Taxa displaying significant associations with geographic location in healthy controls are shown in each column ( N  = 2387). Each row represents one study, with the URT sampling site annotated (NP = nasopharynx, OP = oropharynx). Geographic region per study is shown via the color bar to the left of the heatmap. Significance was determined by multiple regression, correcting for URT sampling site, sequencing method, and 16S hypervariable region, with FDR-corrected two-tailed p -value < 0.05. B Taxa significantly associated with age are shown, for samples with available metadata for age ( N  = 554). Significance was determined by ANCOVA, treating age as a continuous variable, correcting for geographic region, URT sampling site, sequencing method, and 16S hypervariable region, with FDR-corrected two-tailed p -value < 0.05

To investigate how relative abundances of URT genera vary with age in healthy populations, ANCOVA analyses controlling for URT sampling site, geographic region, sequencing method, and 16S rRNA hypervariable region and treating age as a continuous variable were conducted. Overall, 45 genera were significantly associated with age (ANCOVA, FDR-corrected p -value < 0.05), based on ANCOVA containing a squared term for age to uncover potential non-linear relationships. Samples were grouped into age quantiles, in order to visualize mean CLR-transformed relative abundance across age groups for genera that showed significant associations (Fig.  3 ). FDR-corrected p -values associated with age and age^2, as well as mean relative abundances of each taxon per age quantile can be found in the supplementary material (Additional file 1 : Table 3). Using a multiple regression framework similar to the age analysis (i.e., with the same set of covariates), with sex as a categorical independent variable, no genera were found to be significantly associated with sex.

Within-study Random Forest Classifiers show how predictive URT microbiome profiles are in distinguishing cases from controls across disease types

Random forest classifiers were constructed on a per-study basis using genus-level URT relative abundance data, with fivefold cross-validation. The capacity of these classifiers to correctly discriminate cases from controls was assessed by calculating the area under the receiver-operating characteristic (AUROC, Fig.  4 ) from the results of cross-validation testing. Generally, moderate classification accuracy was observed, with an average per-study AUROC of 0.71. Higher AUROC values were observed for some disease conditions, such as influenza and pneumonia. Others showed less capacity to discriminate cases from controls, such as asthma and RTI. No strong correlation was observed between study sample count ( N ) and AUROC (Pearson correlation r  =  − 0.059, p  = 0.75), nor between the URT sampling site and AUROC (two-tailed Student’s independent t -test, t  =  − 0.76, p  = 0.45). These results indicate that URT composition contains information that can be leveraged to predict case versus control status, but that the predictive capacity can vary substantially across diseases.

Area under the receiver-operating characteristic (AUROC) for classifying case versus control status from the URT microbiome profile across studies. AUROC values are shown for each study and sampling site ( N  = 30 data sets, 4706 samples), based on random-forest classifiers constructed using fivefold cross-validation for data from each study, separately. Values less than 0.5 are not shown. Sample count for each study is shown (range = 12–1021). Per-study disease type is shown via color encoding. Shaded background indicates the URT sampling site of each study (nasopharynx = pink; oropharynx = blue)

URT microbiomes show distinct taxonomic associations across studies and disease states

We next investigated whether we could identify robust taxonomic patterns of URT microbiome disruption across disease conditions. We conducted logistic regression on a per-study basis, in order to avoid cross-study comparisons due to sparsity in available covariates, with disease status as the dependent variable, iterating through separate regressions for each genus (significant genera defined as those with FDR-corrected p  < 0.05). Studies spanning 8 disease types showed significant enrichment in at least one taxon (Fig.  5 ). COPD, COVID-19, and asthma were the three respiratory conditions that showed no significant taxonomic enrichments in health or disease (all FDR-corrected p  > 0.05). Several consistent enrichments, where a taxon showed significant enrichment in the same direction in at least two studies within a disease, were observed (Fig.  5 ; designated by black boxes drawn around cells in the heatmap). For instance, Pseudomonas was consistently enriched in cases of influenza, while Veillonella was consistently enriched in cases of influenza, pneumonia, and RSV. Overall consistent cross-disease associations with health or disease status were defined as those genera that showed significant enrichments in the same direction in at least three more studies across all diseases than in the opposing direction ( N same_direction  −  N opposite_direction  ≥ 3). Following this heuristic , Corynebacterium , Veillonella , Fusobacterium , Rothia , and Gemella were all associated with health, although Corynebacterium , and Veillonella each showed enrichment in cases in one study. Pseudomonas and Acinetobacter were consistently associated with disease (Fig.  5 ). Influenza and pneumonia showed the largest number of significant enrichments among all the disease conditions analyzed. Streptococcus had the highest mean relative abundance of taxa with significant associations, at 17.2% ± 0.3%, followed by Corynebacterium , Staphylococcus , Dolosigranulum , Haemophilis , and Prevotella , all with mean relative abundances over 5% (Fig.  5 ). Effect sizes and FDR-corrected p values were recorded for each genus-disease pair (Additional file 1 : Table 4).

Within-study case vs. control logistic regression results at the genus-level. A Per-study taxonomic enrichment in cases is denoted in red, and enrichment in controls is denoted in blue ( N  = 30 data sets, 4706 samples). Blank/gray spaces indicate no significant association. Only taxa with at least one significant association are shown. Significant associations are defined as having FDR-corrected two-tailed p -value < 0.05. Black boxes are shown around consistent enrichments within a disease, in which taxa are enriched in the same direction in at least two studies within a disease. Overall disease associations are shown in the last heatmap row, in which enrichment in the same direction in three or more studies than in the opposite direction ( N same_direction  −  N opposite_direction  ≥ 3) are considered across-disease significant. B Mean relative abundance across all samples of each taxon shown in A . C Prevalence across all samples for each taxon shown in A

The results of this meta-analysis were consistent with prior findings regarding the composition of the URT microbiome in health and disease [ 1 ] and revealed novel compositional patterns within and across diseases and between healthy individuals across age and geography. They also underscore the importance of recognizing different types of dysbioses in the URT microbiome that can potentially contribute to disease.

URT microbiome samples showed a trend toward lower alpha-diversity in disease cases, as opposed to healthy controls, in at least one study representing asthma, RTI, influenza, respiratory allergies, RSV, and pneumonia (Fig.  2 A, B ). Previous studies have reported similar signatures in cases of bacterial or viral infection [ 61 , 62 ]. Influenza was the sole respiratory condition in which one study showed significantly higher alpha-diversity in disease cases, aligning with previous findings that alpha-diversity patterns vary depending on the disease context [ 43 ]. However, this finding will need further validation, as prior reports have found no association between URT alpha-diversity and susceptibility to influenza infection, and another study in this analysis showed an association in the opposite direction from what we report (likely due to methodological differences across analyses) [ 7 , 63 ].

Bray–Curtis dissimilarity between URT communities was associated with multiple covariates: case–control status, sampling site (nasopharynx or oropharynx), disease type, geographic region, sequencing method, and 16S rRNA hypervariable region used for amplicon sequencing (Fig.  2 ). Concordantly, prior work has shown significant beta-diversity differences between health and disease states [ 61 ] and separation between nasopharyngeal and oropharyngeal samples, with the oropharynx harboring a more diverse microbial population than the nasopharynx [ 40 ] (Fig.  2 ). The significant beta-diversity differences reported here between samples from distinct geographic regions were novel. Prior work has asserted a lack of geographic signal in the URT microbiome [ 60 ]. However, it is intuitive that variation in the surrounding environment could give rise to variation in URT composition (Fig.  2 ). Technical differences in sequencing methodology were significantly associated with beta-diversity, as one might expect (Fig.  2 ). These results underscore the need to account for relevant covariates when looking for associations between URT composition and diseases that are independent of these potentially confounding factors.

We next looked into how the covariates age, sex, and geographic location shaped the taxonomic composition of the URT microbiome in healthy individuals across studies, in order to identify and isolate these signals from health and disease associations, and further indicate which covariates should be considered in future analyses (Fig.  4 ). Relative abundances of several taxa ( N  = 98) were observed to show significant associations with geography. Corynebacterium , a known health-associated taxon, showed higher mean relative abundance in samples from North America (12.0%), South America (15.2%), and Oceania (12.9%) than in samples from Africa (5.0%), Asia (4.2%), or Europe (8.6%). Conversely, Streptococcus showed much higher mean relative abundance in samples collected in Africa (35%) than in any other geographic region. Other taxa that show significant association with geographic region include Gemella, Pseudomonas, Rothia , and Veillonella , all of which show significant associations with health or disease via case–control analysis. Due to these significant differences in taxonomic composition, it is imperative to account for geographic location in the construction of diagnostic or therapeutic tools. Two keystone taxa, Dolosigranulum and Moraxella , were enriched in children as compared to adults, which was previously reported (Fig.  4 ) [ 5 ]. Additionally, we saw an increase in the health-associated taxon Veillonella in adults, when compared to children (Fig.  4 ). Due to the breadth of associations observed with age, and the purported inhibition of pathogenic invasion by some of these age-associated genera [ 2 ], we suggest that age should be included as a covariate when analyzing URT microbiome data, whenever possible. However, when age metadata are unavailable, we hope that the list of taxa provided here can be used to identify associations that may be driven by variation in age, rather than by disease. Sex showed no associations.

We ran case–control logistic regression analyses separately within each study and URT sampling site, to avoid pooling data across samples with very different demographic, biological, and methodological characteristics, similar to the approach taken in a prior meta-analysis of human gut microbiomes [ 64 ]. Robust taxonomic enrichments associated with case–control status were observed within 11 out of the 26 studies included in the meta-analysis (Fig.  5 ), including two studies that contained both nasopharyngeal and oropharyngeal samples. Studies from 7 of the 10 respiratory conditions included showed significant enrichment of at least one genus. Asthma, COPD, and COVID-19 were the three diseases that showed no significant URT genus-level associations, although previous URT studies have shown a microbial association with these diseases, such as with Rothia in COVID-19 patients [ 65 , 66 , 67 ].

Several consistent signatures were observed across studies within a disease. For instance, Veillonella was significantly enriched in controls for at least two independent studies within both pneumonia and RSV, and across OP and NP samples in the same study for influenza (Fig.  5 ). Two studies included in the meta-analysis, one in influenza and one in RSV, similarly report Veillonella enrichment in cases as compared to controls [ 49 , 62 ]. Conversely, Pseudomonas was significantly enriched in cases across two independent studies for influenza. This association was also reported in two influenza studies included in the meta-analysis [ 39 , 40 ]. Prevotella showed six significant enrichments across studies, but interestingly showed very inconsistent associations, with enrichment in controls in four studies and enrichment in cases in two. Here we see an example of putative dysbiosis taking many forms, and the health or disease associations of many taxa showing strong context-specificity. Across diseases, significant signatures were observed for several keystone taxa that were enriched in healthy individuals [ 2 ], like Corynebacterium, Veillonella , Fusobacterium , Rothia , and Gemella . Of these, Corynebacterium has been previously identified as a core taxon, putatively associated with health [ 1 ]. Additionally, these are largely abundant/prevalent taxa, with mean relative abundance above 5% for Corynebacterium , specifically (Fig.  5 ) . Conversely, a few genera known to harbor opportunistic pathogen species, including Pseudomonas and Acinetobacter , showed multiple associations with diseases. Acinetobacter baumannii and Pseudomonas aeruginosa are both known to cause disease in humans [ 27 , 28 , 29 , 68 ]. Understanding which taxa are strongly related to health or disease, and in which contexts, will further aid the development of effective microbial diagnostics and therapeutics.

There were several limitations to our study that are important to highlight. First, there were differences in amplicon sequencing methodologies across the 26 studies included in this analysis, which introduced substantial technical biases. For example, not all studies had paired-end reads available, so we elected to use only forward reads for all studies to mitigate potential bias. Using longer, merged reads for some studies and not others would impact the efficiency of taxonomic annotation across studies (i.e., even for studies with the same variable region sequenced). Furthermore, there are often a large number of paired-end reads that fail to merge, which can lead to a substantial drop in sequencing depth in a given sample, which is another layer of bias. Additionally, samples across studies showed differences in sequencing depth. To account for this, we elected to rarefy the data to normalize sampling depth across samples. While other options exist, the current consensus in the field is that rarefaction is still optimal for comparing point estimates of alpha- and beta-diversity across samples [ 69 ].

First, while we controlled for these technical variables in our statistical testing whenever possible, incomplete metadata on these differences across studies can skew the final results. Second, many studies were missing pertinent demographic metadata, such as sex or age, which limited our statistical power by preventing us from correcting for these covariates in regressions that pooled data across all studies. It was not possible to determine whether geographic region-related trends were consistent across age groups, due to age metadata not being available for a majority of samples. Third, some studies have nearly 100-fold more samples than others, which can skew regression results if samples were pooled across studies that differed substantially in cohort size. For these reasons, the case versus control genus enrichment analyses were conducted on a per-study basis, to avoid introducing these myriad biases into the regressions. Significant case–control hits from within-study regressions that were consistent across studies provided strong support for disease-specific associations that are independent of the aforementioned limitations.

Overall, these findings point to different flavors of dysbiosis that distinguish different disease states in the URT. In some cases, the disease state is characterized by a loss of putatively beneficial commensals, such as Veillonella in influenza, pneumonia, and RSV, and in other cases, it is characterized by the gain of putatively pathogenic taxa such as Pseudomonas in influenza, which mirrors what has been found across diseases in the human gut microbiome [ 64 ]. Future work should leverage these results to help guide the development of diagnostics and therapeutics for the URT.

Systematic review of relevant studies

A systematic review was conducted using two main search engines (PubMed and Embase) to retrieve all relevant publications describing microbiome sequencing in the human upper respiratory tract. A PRISMA flow chart (Additional file 2 : Fig. S1) shows how the publications were screened, identified as relevant, and finally selected based on inclusion and exclusion criteria. Briefly, a total of 153,586 reports were identified using relevant keywords such as “microbiome,” “16S rRNA,” “URT,” “oropharynx,” “nasopharynx,” and “larynx.” Of these, 37,083 were classified as conference abstracts, conference papers, short surveys, and book chapters and therefore were excluded from the analysis. Additional exclusion criteria included 16S rRNA studies from non-human URT, which filtered out 115,883 manuscripts, leaving only 620 manuscripts. Of these 620 manuscripts, a very strict and manual pre-selection was conducted to eliminate those with irrelevant topics or disease conditions, such as studies that involved interventions or those without healthy patient controls, as well as studies with unavailable sequencing data, incomplete metadata, or duplicate manuscripts that referred to the same clinical study. This pre-selection step reduced the number of manuscripts by approximately 90%, leaving only 68 manuscripts. The final selection step was conducted manually to ensure the public availability of well-curated metadata and corresponding raw sequencing data files. This step also excluded studies from overrepresented disease conditions, so that no more than 3 studies were selected per disease condition. At the end, a total of 26 peer-reviewed publications survived all inclusion and exclusion criteria, yielding a total of 10 URT-related conditions (asthma, chronic obstructive pulmonary disease, COVID-19, influenza, pneumonia, respiratory allergies, rhinosinusitis, RSV, respiratory tract infection, tonsillitis) with 1–3 studies per condition representing a total of 4,706 samples.

16S rRNA amplicon sequencing URT cohorts

All phylogenetic and read count data used in this study consisted of 16S rRNA gene amplicon sequencing data, with multiple hypervariable regions sequenced across studies, spanning the V1 to V7 regions. A full list of the 26 data sets analyzed in this study, along with links to SRA accession numbers and accompanying metadata, can be found in Additional file 1 : Table 1. The studies contained between 12 and 1021 subjects and varied in age from birth to 97 years old (in studies where age metadata was available), with more representation of young individuals. Studies were conducted in all six inhabited continents, with more representation from Europe and North America. 16S rRNA amplicon sequencing data consisting of FASTQ files, along with associated metadata, were downloaded from the NCBI SRA. While some studies included paired-end sequencing reads, only forward reads were used to maintain better analytical consistency across all studies and to avoid biases in the efficiency of taxonomic assignment between studies. Following data collection, all FASTQ data were imported into QIIME2 version 2022.8.3 [ 70 ] for further processing and analysis. Data were imported through the construction of a single-end Phred33v2 FASTQ manifest for each dataset. Following import, quality control and filtering in the QIIME2 DADA2 (v1.12.1) [ 71 ] plug-in removed chimeric sequences, trimmed left ends of all sequences by 10 bp to remove primers, truncated sequences uniformly at 200 bp, and identified amplicon sequence variants (ASVs). In total 623,507,314 reads were filtered, with 134,649,099 removed for poor quality or chimerism.

Data preprocessing and taxonomic classification

The Silva high-quality rRNA gene database version 138 was used to assign taxonomy to ASVs [ 72 ]. The full-length 16S rRNA classifier was used due to heterogeneity in the hypervariable region used for sequencing between studies. Mean classification at the genus level was 86.0% (Additional file 1 : Table 5; Additional file 2 : Fig. S2). At the species level, classification was unsuccessful, with a mean classification of 13.9%. As a result, all subsequent analyses were conducted at the genus level by binning ASV counts together based on their genus-level annotations. All subsequent data analysis was managed using pandas (v1.4.4) in Python (v3.8.13).

Alpha-diversity analyses

To investigate alpha-diversity, QIIME2 artifacts containing sequences for each study were merged into a single dataframe. Prior to calculation, algorithmic filtering removed any taxa with fewer than two reads per study, and any taxa present in less than 5% of samples across a study. This merged data frame was converted into a QIIME2 artifact and rarefied using the qiime feature-table rarefy function to a sampling depth of 2000. Alpha-diversity was calculated in QIIME2 via the alpha function within the diversity plugin. Shannon entropy and Chao1 index were used to estimate alpha-diversity for all samples included in the meta-analysis. Shannon entropy and Chao1 index for cases and controls within each disease were plotted and significant differences across groups were tested using two-tailed independent Student’s t -test ( p  < 0.05) in SciPy (v1.8.1).

Beta-diversity analyses

To estimate beta-diversity, the filtered and rarefied genus count table constructed previously was used to construct a Bray–Curtis dissimilarity matrix using the beta function in the QIIME2 diversity plugin. Subsequently, principal coordinate analysis (PCoA) was used to analyze and visualize overall beta-diversity in scikit-bio version 0.5.7. Significant differences in beta-diversity were observed along multiple axes, including case vs. control status, disease type, geographic location, URT sampling site, sequencing method, and 16S rRNA hypervariable region as determined by PERMANOVA, using the adonis function within the diversity plugin for QIIME2.

URT compositional patterns across geographic regions

A genus-level abundance matrix was constructed using only healthy control samples, and taxa with fewer than two reads per study or those present in fewer than 5% of samples across a study were removed. To examine the association between geographic location and centered log-ratio (CLR) transformed relative abundance of common taxa, multiple regression was used to determine significant enrichments of taxa in each geographic region while correcting for URT sampling site, sequencing method, and 16S rRNA hypervariable region using the formula “clr ~ region + v_region + sequencing + URT” in statsmodels (v0.13.5) [ 73 ]. For the purpose of these analyses, the continents in which studies took place were used as the geographic regions, as too many countries were represented to have appropriate statistical power at smaller geographic scales. As sex and age metadata were not available for 61.5% of the studies, these covariates were not accounted for in this analysis. Multiple comparison correction for p -values was done using the Benjamini–Hochberg method for adjusting the false discovery rate (FDR) [ 74 ], using statsmodels (v0.14.1). Per-study mean CLR-transformed relative abundance of taxa identified to be significantly enriched in at least one geographic region (multiple regression, FDR-corrected p  < 0.05) were added to a clustered heatmap, with color encoding the average CLR-transformed relative abundances in each context. Columns containing average CLR-transformed relative abundances were clustered via an agglomerative clustering algorithm using clustermap in seaborn (v0.12.2).

URT microbiome-age associations

Associations between age and CLR-transformed relative abundances was analyzed via ANCOVA in statsmodels. Using 10 studies for which age metadata was available, ANCOVA was conducted using the following formula “clr ~ age + age 2  + variable_region + sequencing + URT_site + region” that was used to determine significant associations with age, accounting for URT sampling site, geographic region, sequencing method, and 16S rRNA hypervariable region. The square term for age was included to determine if non-linear relationships existed between CLR and age. The p -values were corrected for multiple comparisons via the Benjamini–Hochberg FDR correction as previously described. Samples were split into quantiles by age for visualization. Significantly associated taxa (FDR-corrected p  < 0.05) were added to a heatmap with color encoding the average CLR-transformed relative abundances.

URT microbiome associations with sex

Associations between sex and genus-level CLR abundances were determined via multiple regression. Using the 10 studies for which sex metadata was available, multiple regression were conducted using the following formula: “clr ~ sex + variable_region + sequencing + URT_site + region” in statsmodels. The resulting p -values were corrected for multiple comparisons via the Benjamini–Hochberg FDR correction. After correction, no taxa showed a significant association with sex.

Supervised classification of cases and control

Random forest classifiers were constructed for each study to classify cases and controls within each study using scikit-learn (v0.24.1) [ 74 , 75 ]. Classifiers were constructed with fivefold cross-validation, using the scikit-learn StratifiedKFold function to shuffle data. The RandomForestClassifier function within scikit-learn was used to construct classifiers with n_estimators = 100. Area under the curve of the receiver-operating characteristic was calculated using the results of cross-validation testing, using the cross_val_predict and roc_auc_score functions in scikit-learn .

URT microbiome-disease associations

To investigate the association between genera in the URT microbiome and disease, sample read counts were normalized using a CLR transformation, as above. Logistic regressions used case–control status as the dependent variable and CLR-transformed abundance as the independent variable, following the formula “case_control_status ~ clr” in statsmodels. Regressions were run separately within each study and sampling site. By running separate analyses within each study and sampling site, key confounders like geographic location, sampling site, 16S rRNA hypervariable region, and sequencing method were constant within a given regression analysis. Mean relative abundance of each taxon within a given study and sampling site found to be significant was calculated for visualizations. P -values were FDR-corrected as described above. Significance was assigned to any association with an FDR-corrected p -value less than 0.05. Results were plotted in a binary heatmap, with significant health-associated genera designated as blue and disease-associated genera designated as red. Heatmaps were constructed using seaborn.

Availability of data and materials

All data generated or analyzed during this study are included in this published article, its supplementary information files, and publicly available repositories. Additional supplementary data can be found in Additional file 1 : Tables 1–5. All original data are available on the NCBI SRA under accession codes provided in Additional file 1 : Table 1, with the exception of one study for which data is not publicly available. All intermediate data files for this analysis are available at Zenodo under DOI: 10.5281/zenodo.10962515. Analysis code can be found at the following GitHub repository: https://github.com/Gibbons-Lab/2023_URTmetaanalysis .

Abbreviations

• Upper respiratory tract

Nasopharynx

Respiratory tract infection

Respiratory syncytial virus

Chronic obstructive pulmonary disease

Coronavirus disease 2019

Amplicon sequence variant

False detection rates

Centered log-ratio

Area under the receiver-operating characteristic

Principal coordinate analysis

Analysis of covariance

National Center for Biotechnology Information Sequence Read Archive

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Acknowledgements

 Thanks to members of the Gibbons Lab for helpful comments on this work.

This work was funded by a research grant from Reckitt Health US LLC, by a Washington Research Foundation Distinguished Investigator Award, and by startup funds from the Institute for Systems Biology.

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N.Q.B., S.M.G., and C.D. conceptualized the study. N.Q.B. ran the analyses, interpreted the results, and authored the first draft of the manuscript. S.M.G., C.D., and J.F.C. provided resources for the work and supervised the work. J.F.C. conducted the study selection for inclusion in the analysis. J.S. and R.S. provided support with the interpretation of results. All authors read and approved the final manuscript.

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Supplementary Information

Additional file 1: table s1..

Studies Inclusion. Table S2. Taxonomic Classification Percentage. Table S3. Enrichment associated with geographic region in healthy controls. Table S4. Enrichment associated with age in healthy controls. Table S5. Results from case v. control logistic regression.

Additional file 2: Fig. S1.

Prisma Flowchart for Study Inclusion, An original search returned 153,586 studies. Filtering out conference abstracts, conference papers, short surveys and book chapters left 116,503 peer-reviewed publications. Additional screening removed 115,883 publications by screening for keywords “16S rRNA” and “human” and “upper respiratory” or “nasopharynx” or “oropharynx” or “larynx”, leaving 620 publications. Another phase of screening removed 552 publications for irrelevance (e.g., intervention studies or studies that lacked healthy controls), lack of sequencing data, unavailable, incomplete data/metadata, and duplicate studies reporting on the same cohort, leaving 68 publications. Of these, 42 were excluded due to overrepresentation of disease conditions in the final cohort, or problems with accessing the raw data and metadata. In the end, 26 publications remained, with 1-3 studies per disease. Fig. S2. Mean classification percentage for each study at each taxonomic level. Classification remained at or above 60% for all studies through the genus level. At the species level, a significant drop in classification percentage was observed.

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Quinn-Bohmann, N., Freixas-Coutin, J.A., Seo, J. et al. Meta-analysis of the human upper respiratory tract microbiome reveals robust taxonomic associations with health and disease. BMC Biol 22 , 93 (2024). https://doi.org/10.1186/s12915-024-01887-0

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An update on respiratory syncytial virus

• Antonio Piralla 1   na1 ,
• Zhengrong Chen 2   na1 &
• Hassan Zaraket 3   na1  

BMC Infectious Diseases volume  23 , Article number:  734 ( 2023 ) Cite this article

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Respiratory syncytial virus (RSV) is a leading cause of acute respiratory infections resulting in a significant burden worldwide, particularly in children and older adults. This collection calls for original research papers that advance our understanding of the epidemiology, evolution, diagnosis, clinical management, and prevention of RSV infections.

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Respiratory syncytial virus (RSV) is a leading cause of acute respiratory tract infection, including lower respiratory tract infection (LRTI). Before the coronavirus disease 2019 (COVID-19) pandemic RSV represented the fourth cause of overall disability-adjusted life-years at all ages [ 1 ]. The burden of RSV infection is highest in children aged < 5 years (global incidence 17.0 (95% uncertainty intervals (UI) 10.6–26.2) per 1000 people), older adults aged > 70 years (global incidence 6.3 (95% UI 4.9–7.8) per 1000 people) and adults with underlying comorbidities [ 2 ]. As well as being well-established as a pediatric pathogen, RSV infections have been increasingly reported in adults. In particular, the number of RSV cases in older adults in high-income countries has been estimated as high as 10.9 million, resulting in 0.8 million hospitalizations and as many as 74,000 deaths [ 3 ].

RSV annual epidemiology is also impacted by the alternation of the subtypes A and B and, within them, by genetic variations of strains or lineages [ 4 ]. Indeed, several studies demonstrated that RSV variability is higher than previously thought and impacted the number of RSV hospitalizations and clinical severity in pre-pandemic seasons. Subsequently, RSV genetic evolution could have been impacted by the COVID-19 pandemic [ 5 ]. The drop in infections could have caused a genetic bottleneck resulting in the extinctions of pre-pandemic lineages and the emergence of a reduced number of RSV strains, and/or local variations [ 4 ]. RSV, subtypes A and B, classification has been recently revised by several groups to unify criteria and nomenclature following the availability of a larger number of full-genome sequences, together with those of the G gene that has been traditionally used for genotype designation [ 5 ].

Before the COVID-19 pandemic, the detection of RSV infections followed a predictable seasonal pattern each year. During the first year of the pandemic, respiratory viruses, including RSV, caused an unusually low number of infections and related hospitalizations in the first phase of the COVID-19 pandemic, due to the implementation of non-pharmaceutical interventions [ 6 ]. The subsequent reduction of pandemic restrictions has caused the reappearance of RSV in summer-early autumn 2021 [ 7 ]. To explain the atypical inter-seasonal resurgence of respiratory infections around the world, the Pediatric Infectious Disease Group proposed the concept of an “immunity debt” [ 8 ]. In the following epidemic season, autumn 2022, the RSV epidemiology was again impacted by SARS-CoV-2 circulation, but also by the return of the influenza virus. The three viruses co-circulated together abundantly causing a heavy burden on healthcare services [ 9 ]. This occurrence defined as “a tridemic” was unexpected, given the previously demonstrated interference between SARS-CoV-2 and influenza virus, and RSV and influenza virus [ 9 ]. Indeed, viral interference is a complex phenomenon driven by viral properties and by host immunity. At the population level, viral competition for the same host can shape the circulation of seasonal and pandemic respiratory viruses [ 9 ].

Currently, treatment of RSV infections relies on supportive care including supplemental oxygen, rehydration, and mechanical ventilation when critical. Antiviral treatment with aerosolized ribavirin is limited to severe infections in immunocompromised patients [ 10 ]. Until recently, only palivizumab, a multiple-dose monoclonal antibody (mAb) has been available for immunoprophylaxis against severe RSV-related lower respiratory tract illness (LRTI) in premature and other high-risk infants [ 10 ]. More recently, nirsevimab, a longer-lasting, single-dose mAb for the general infant population (preterm and term infants) targeting the RSV fusion glycoprotein (F) was approved. Nirsevimab provides protection for a whole season and has an efficacy of 74.5% against medically attended RSV-LRTI and 62% against hospitalizations due to severe RSV-LRTI [ 11 ].

On the vaccine front, after decades of troubled RSV vaccine development, four randomized clinical trials in older adults and pregnant women were recently published revealing a breakthrough in providing a high level of protection against RSV [ 12 , 13 , 14 , 15 ]. The successful development of these vaccines was mainly enabled by structure-based design and research demonstrating that the F protein in its perfusion state (PreF) elicits high levels of potent neutralizing antibodies [ 14 ]. In a randomized clinical trial of 24,966 adults aged 60 and above, an adjuvanted stabilized PreF recombinant protein-based vaccine was associated with a 94% efficacy against severe RSV-related LRTI and 72% efficacy against RSV acute respiratory infection [ 15 ]. In another trial with 34,284 adults > 60 years old Walsh et al. also showed that a recombinant PreF protein-based vaccine resulted in similar high efficacies of 67% and 86% against RSV-associated LRTI with at least two or three signs or symptoms, respectively [ 13 ]. Falsey et al. demonstrated in a trial with 5782 adults aged 65 and above that an adenovirus-based PreF vaccine candidate led to 70–80% efficacy depending on the disease definition [ 14 ]. Finally, a bivalent PreF protein-based maternal vaccine offered 82% protection against medically attended RSV-associated LRTI in infants within 90 days after birth and 69% at 6 months after birth for severe RSV in their infants [ 12 ]. These studies were the basis of the FDA’s approvals of two vaccines (Arexvy and Abrysvo TM ) for adults aged 60 and older and for pregnant women (Abrysvo TM ) to protect infants from birth up to 6 months of age [ 16 ].

The recent advances and progress toward the prevention of RSV have re-energized the field and highlighted the need for continued research to better understand the disease as well as the epidemiology and evolution of RSV. Studies assessing the impact of the newly approved immunotherapeutic and vaccines on RSV burden and genetic diversity are critically needed. This collection calls for original research papers that aim to improve our understanding of RSV epidemiology, evolution, diagnosis, clinical management and prevention.

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Acknowledgements

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Antonio Piralla, Zhengrong Chen, and Hassan Zaraket equally contributed to this manuscript.

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Microbiology and Virology Department, Fondazione IRCCS Policlinico San Matteo, via Taramelli 5, Pavia, 27100, Italy

Antonio Piralla

Department of Respiratory Disease, Children’s Hospital of Soochow University, NO. 92, Zhongnan Street, Suzhou, PR of China

Zhengrong Chen

Department of Experimental Pathology, Immunology, and Microbiology, Faculty of Medicine, American University of Beirut, Beirut, Lebanon

Hassan Zaraket

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Hassan Zaraket is an employee and holds stocks of Hoffman La Roche; however, this work was not performed as part of his employment. Antonio Piralla and Zhengrong Chen have nothing to declare.

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Piralla, A., Chen, Z. & Zaraket, H. An update on respiratory syncytial virus. BMC Infect Dis 23 , 734 (2023). https://doi.org/10.1186/s12879-023-08730-x

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The characteristics of microbiome in the upper respiratory tract of COVID-19 patients

• Xilong Zhang 1 , 2   na1 ,
• Nadira Nurxat 1   na1 ,
• Jueraiti Aili 2 ,
• Yakupu Yasen 2 ,
• Qichen Wang 1 &
• Qian Liu 1  

BMC Microbiology volume  24 , Article number:  138 ( 2024 ) Cite this article

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Co-infection with other pathogens in coronavirus disease 2019 (COVID-19) patients exacerbates disease severity and impacts patient prognosis. Clarifying the exact pathogens co-infected with severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) is premise of the precise treatment for COVID-19 patients.

Sputum samples were collected from 17 patients in the COVID-19 positive group and 18 patients in the COVID-19 negative group. DNA extraction was performed to obtain the total DNA. Sequencing analysis using 16S and ITS rRNA gene was carried out to analyze the composition of bacterial and fungal communities. Meanwhile, all the samples were inoculated for culture.

We did not observe significant differences in bacterial composition between the COVID-19 positive and negative groups. However, a significantly higher abundance of Candida albicans was observed in the upper respiratory tract samples from the COVID-19 positive group compared to the COVID-19 negative group. Moreover, the Candida albicans strains isolated from COVID-19 positive group exhibited impaired secretion of aspartyl proteinases.

COVID-19 positive patients demonstrate a notable increase in the abundance of Candida albicans , along with a decrease in the levels of aspartyl proteinases, indicating the alteration of microbiota composition of upper respiratory tract.

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Introduction

Severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), as a novel member of enveloped RNA β-coronavirus, triggers coronavirus disease 2019 (COVID-19). The main symptom for COVID-19 includes severe SARS-CoV-2 associated pneumonia [ 1 ]. Since its emergence in 2019, COVID-19 has caused a global pandemic due to the rapid transmission [ 2 ]. The high mortality rate of COVID-19 poses a significant threat to human health and public health security. According to the statistics from the World Health Organization (WHO), as of July 12th, 2023, there have been a total of 760 million COVID-19 confirmed cases and 6.95 million deaths worldwide. Fortunately, great efforts have been made in the development of therapeutic drugs [ 3 ], preventive vaccines [ 4 ], and the discovery of other measures beneficial for disease recovery. However, the co-infection of SARS-CoV-2 with other microorganisms enhances the severity and mortality of COVID-19 [ 5 , 6 , 7 ]. Deciphering the main alteration in the composition of microorganism in COVID-19 patients is crucial for effective patient management and treatment of SARS-CoV-2.

The co-infecting microorganisms in COVID-19 patients included bacteria [ 7 ], fungi [ 6 ], and virus [ 8 ]. Recent clinical and in silico studies revealed that viral co-infections in COVID-19 primarily involve respiratory viruses such as enterovirus/rhinovirus (hRV), human metapneumovirus (hMPV), and Respiratory Syncytial Virus (RSV) [ 9 ]. The most frequently identified co-infected bacterial pathogens include Streptococcus pneumoniae , Staphylococcus aureus, Klebsiella pneumoniae , Acinetobacter baumannii , Mycoplasma pneumoniae , Legionella pneumophila , and Chlamydia pneumoniae [ 10 ]. Fungal originated co-infections including pulmonary aspergillosis and candidiasis were reported to aggravate the severity of SARS-CoV-2 infection [ 11 , 12 ]. COVID-19-associated pulmonary aspergillosis (CAPA) affects approximately 15% of critically ill patients diagnosed with COVID-19 [ 13 ]. Invasive candidiasis is rare but associated with considerable mortality in critically ill patients [ 14 ]. Candida albicans ( C. albicans ) was identified as the most prevalent pathogen, accounting for 70.7% of cases, in hospitalized COVID-19 patients with oropharyngeal candidiasis (OPC) [ 15 ]. However, whether the SARS-CoV-2 infection affects the microbiome structure of the respiratory tract is still unknown. Moreover, the promising pathogens need to be clarified to prevent and treat SARS-CoV-2 infection effectively.

By comparing the structure of bacteria and fungi composition of patients with respiratory tract infections in patients with or without COVID-19 infection using 16S ribosomal RNA (16S rRNA) and Internal Transcribed Spacer (ITS) sequencing, we observed that there are no significant differences in bacterial composition between the two groups. Instead, there is a significantly increased abundance of C. albicans from respiratory tract samples in COVID-19 patients. C. albicans isolated from COVID-19 infection group showed impaired levels of secreted aspartyl proteinases (Sap), which may help C. albicans colonization by evading immune surveillance.

The clinical analysis of the recruitments

In total, 35 sputum samples were collected from Ren Ji Hospital, Shanghai Jiao Tong University School of Medicine in January 2023. The clinical data and laboratory test results for 35 patients are presented in Table  1 . Among the COVID-19 negative group, there were 18 patients with an average age of 75 years, and 15 (83%) of them were male. In the COVID-19 positive group, there were 17 patients with an average age of 78 years, and 14 (82%) of them were male. There were no statistically significant differences in age (Z = -0.777, P  = 0.437), gender (Z = 0.077, P  = 0.939), lymphocytes (19.35, Z = -1.242, P  = 0.225), neutrophils (80.10, Z = -1.398, P  = 0.164), and monocytes (7.35, Z = -1.934, P  = 0.055) between the two groups. Of note, the content of procalcitonin (PCT) was significantly higher in the COVID-19 positive group (0.34 ng/mL) compared with the negative group (0.08 ng/mL) (Z = -2.311, P  = 0.020).

Bacterial composition is similar in the respiratory tract of patients with or without COVID-19 infection

The increased contents of PCT suggested a potential bacterial infections in the COVID-19 positive group [ 16 ], the bacterial microbiome was first compared between individuals with positive and negative COVID-19 respiratory tract infections utilizing 16S rRNA sequencing. At the phylum level, no significant distinctions in microbial composition were observed between the two groups as Firmicutes is the dominant phylum in both groups. Interestingly, there was a reduction in the relative abundance of Firmicutes (36.86%) in the COVID-19 positive group compared to the negative group (53.27%) (Fig.  1 A). Further analysis at the genus level demonstrated that Streptococcus was the prevailing genus in both groups (Fig.  1 B), with a prevalence of 46.67% in the negative group and 32.24% in the positive group. There are no differences in the alpha diversity, as determined by the observed species and Shannon indicators (Fig.  1 C), and beta diversity through principal component analysis (PCA) (Fig.  1 D) between the two groups. In order to validate these findings, a culture-based methodology was utilized, confirming Streptococcus as the prevailing genus in both groups (Fig.  1 E). Collectively, our results indicate that there are no substantial disparities in the bacterial composition of the upper respiratory tract among individuals post COVID-19 infection.

Respiratory tract bacterial composition comparisons between COVID-19 infected and non-infected patients (A) Bar chart showing the relative abundance of the top 10 phylum of in the COVID-19 negative and positive groups. (B) Bar chart showing the relative abundance of the top 10 genus of Firmicutes in the COVID-19 negative and positive groups. (C) Alpha diversity analysis. (D) Beta diversity analysis. (E) Relative abundance of bacterial isolates at the culture level. The statistical significance was measured by unpaired, two-tailed Student’s t test. ns, not significant

The abundance of Candida exhibited a significant increase in the upper respiratory tract of the COVID-19 positive patients

Fungal infections affect the severity of COVID-19 patients [ 17 ], therefore, a detailed analysis of fungal composition was conducted through ITS sequencing. At the phylum level, the microbial structure of the COVID-19 positive group was predominantly characterized by the presence of Ascomycota (Fig. 2A), with Saccharomycetales at the order level and Saccharomycetes at the class level (LDA SCORE [log 10] > 3) (Figure S1 ). Conversely, the COVID-19 negative group exhibited a predominance of Fusarium , Meyerozyma , and Malassezia restricta at the genus level (|LDA SCORE [log 10]| > 2) (Figure S1 ). Furthermore, analysis of the top 5 dominant genus showed that Candida species is the prevailing fungal genus in both groups (Fig.  2 B), and Candida species showed a significantly higher abundance in the COVID-19 positive group (Fig. 2B). The analysis of observed species and Shannon indices indicated a declining trend of Alpha diversity of the upper respiratory tract in the COVID-19 positive group (Fig.  2 C). Beta diversity analysis demonstrated that the first principal component significantly contributed to the intergroup dissimilarities (Fig.  2 D). Finally, culture-based isolation analysis showed that C. albicans is the predominant fungi in both groups (Fig.  2 E). Moreover, the separation rate of C. albicans in the COVID-19 positive group (80/411, 19.46%) surpasses that of the COVID-19 negative group (49/432, 11.34%), which highlights a higher prevalence of C. albicans in individuals with COVID-19 infection. Taken together, our data suggested a substantial elevation of Candida in the upper respiratory tract in the COVID-19 positive group, with C. albicans being the predominant species in the sputum samples of both groups.

Increased Candida abundance in the upper respiratory tract of COVID-19 positive patients (A) Bar chart showing the relative abundance of the top 5 phylum in the COVID-19 negative and positive groups. (B) Bar chart showing the relative abundance of the top 5 genus in the COVID-19 negative and positive groups. (C) Alpha diversity analysis. (D) Beta diversity analysis. (E) Relative abundance of fungal isolates at the culture level. The statistical significance was measured by Mann-Whitney U test. ns, not significant. *, P  < 0.05

Metabolic pathways in bacterial and fungal colonization patterns in respiratory infections

Based on our comprehensive analysis of 16S rRNA and ITS data, we conducted a meticulous KEGG pathway analysis to investigate the potential metabolic impact exerted by both bacterial and fungal communities between the two groups. Notably, the colonization of specific bacterial species within sputum samples was found to significantly influence the modulation of host cellular metabolic pathways (Fig.  3 A&B). However, there are no statistically significant differences observed between the two groups pertaining to these metabolically relevant pathways (Fig.  3 C&D). This suggests that the establishment of pathogenic bacteria can affect the metabolic pathways of the host, irrespective of the presence of a COVID-19 infection.

Metabolic pathways of bacterial and fungal colonization in respiratory infections (A) KEGG pathway analysis of bacteria. (B) KEGG pathway analysis of fungi. (C) Comparison of bacterial-related metabolic and disease levels between the two groups. (D) Comparison of fungal-related metabolic and disease levels between the two groups. The statistical significance was measured by unpaired, two-tailed Student’s t test. ns, not significant

Comparative expression of secreted aspartyl proteinases and phospholipases in Candida albicans isolated from COVID-19 positive and negative groups

C. albicans possesses multiple virulence factors, including hyphal formation, surface recognition molecules, phenotypic switching, secretion of extracellular hydrolytic enzymes, adhesion, and tissue penetration ability [ 18 ]. Among these factors, the secretion of hydrolytic enzymes plays a crucial role in invading host tissues, with most strains producing high levels of secreted aspartyl proteinases (Sap), phospholipases (Plb), and lipases (Lip) [ 19 ]. As shown in Table  2 , in the COVID-19 negative group, 35 strains of C. albicans showed positive expression for secreted aspartyl proteinases (97.22%), while 8 strains showed positive expression for phospholipases (25.80%). In the COVID-19 positive group, 30 strains exhibited positive expression for secreted aspartyl proteinases (88.24%), and 16 strains showed positive expression for phospholipases (45.71%). We observed that the expression of Sap was significantly lower in the strains isolated from the upper respiratory tract in the COVID-19 positive group compared to those from the COVID-19 negative group ( P  < 0.0001).

In this study, we found that there was no significant difference in the bacterial composition of the upper respiratory tract between COVID-19 positive and negative individuals. However, COVID-19 positive patients showed a significant increase in the abundance of C. albicans , which displayed a concurrent decrease in the level of secreted aspartyl proteinases compared with the strains isolated from COVID-19 negative patients, suggesting alterations in the microbial composition of the upper respiratory tract.

Changes in the respiratory microbiota have been associated with disease severity [ 20 ]. In our study, we did not observe significant differences in bacterial composition using 16S rRNA sequencing. It has been reported that the upper respiratory tract microbiota of COVID-19 positive patients is dominated by Gram-positive Staphylococcus and Corynebacterium species [ 21 ]. Another study indicates that, compared to COVID-negative individuals, the upper respiratory tract microbiota of COVID-positive patients predominantly consists of Streptococcus and Veillonella [ 22 ]. The alterations in microbiota of patients with COVID-19 may be attributed to differences in sampling location (upper or lower respiratory tract), sampling methods, patient severity, disease stage, antibiotic usage, length of ICU stay, and other confounding factors [ 23 ]. It is possible that the similar bacterial composition is due to the limitations in sample size in our study.

However, a significant increase in the relative abundance of C. albicans in the upper respiratory tract of COVID-19 positive patients was observed by ITS sequencing and culture-based analysis in our study (Fig.  2 ). The significant threat in the treatment of COVID-19 co-infection with C. albicans has been well described. Furthermore, studies have demonstrated a link between C. albicans and the development of Long COVID or Post-acute sequelae of COVID-19 (PASC) [ 24 , 25 , 26 ]. C. albicans is a commensal fungus that asymptomatically colonizes the skin and mucosa of 60% of healthy individuals. However, C. albicans has the capacity to transition from a commensal to an invasive state, and this transition is facilitated by host factors such as: (i) disruption of the normal mucosal flora balance; (ii) compromised barrier functions; and (iii) immunosuppression, particularly decreased cellular immune responses [ 27 ]. During the course of COVID-19 infection, SARS-CoV-2 exhibits a propensity to target T cells, B cells, and NK cells, resulting in immune system impairment [ 28 ]. This immune dysregulation creates a favorable environment for the proliferation of opportunistic pathogens. It has been reported that the incidence of invasive pulmonary aspergillosis, caused by Aspergillus species, in COVID-19 patients ranges from 19.6–33.3% [ 17 ]. C. albicans is an opportunistic fungi that can cause infections in individuals with compromised immune systems [ 29 ]. Due to the attenuated immune response of COVID-19 patients, particularly the reduced upregulation of monocyte CD80 expression and the suppressed release of key cytokines such as IL-6, TNF, IL-1a, and IL-1b, this may lead to a decreased ability to clear C. albicans in these patients [ 30 ].

We observed that the main species of C. albicans isolated from the two groups, exhibited differences in secreting extracellular hydrolytic enzymes (Table  2 ). The main steps of C. albicans infection involve adhesion and hyphal formation. The secreted aspartyl protease family (SAP family) contributes to adhesion, while phospholipase (PLB) facilitates the hydrolysis of phospholipids [ 31 ]. Heightened expression of Sap correlates with hyphal formation and enhances adhesion and invasion capabilities [ 32 ]. The hydrolytic enzyme production is controlled by a diverse group of genes known as the Saps gene family, consisting of at least 10 members [ 33 ]. Sap1 and Sap3 are induced by phenotypic switching, Sap4, Sap5 and Sap6 are expressed upon hyphal formation, and Sap1-Sap6 as well as Sap9-Sap10 are involved in adhesion to host cells. Eight Sap (Sap1-Sap6, Sap9 and Sap10) are important for pathogenicity attributes [ 34 ]. Furthermore, Sap have been found to potently induce the production of pro-inflammatory cytokines in monocytes [ 35 ]. Recent studies have demonstrated that Sap activate NLRP3 inflammasomes, resulting in the triggering of inflammatory immune responses and the recruitment of neutrophils [ 36 ]. Moreover, studies have also pointed out that the secreted aspartyl proteases Sap1, Sap2, and Sap3 from C. albicans exhibit the ability to hydrolyze and thereby disrupt the functions of human complement components C3b, C4b, and C5. This suggests that these secreted aspartyl proteases may play a role in enhancing C. albicans ’ resistance to immune system attacks [ 34 ].

We observed a noteworthy decrease in the levels of secreted aspartyl proteases in the COVID-19 positive group when compared to the COVID-19 negative group. Regrettably, the specific aspartyl protease responsible for this alteration remains unidentified. We postulate that C. albicans may employ a reduction in aspartyl protease levels as a tactic to evade the immune system’s assault. Nonetheless, there exists the possibility that the reduced colonization of aspartyl protease-producing C. albicans in COVID-19 patients could stem from impairments in their immune system [ 37 ]. Whether other virulence factors, such as biofilm formation or immune evasion molecules contribute to the colonization of C. albicans remains a subject worthy of further investigation [ 27 , 38 ].

Our findings revealed that COVID-19 positive patients showed significantly higher levels of procalcitonin (PCT) compared to the negative group (Table  1 ). PCT is a biomarker for bacterial infections [ 39 ]. However, the bacterial structure was not affected by SARS-CoV-2 infection by 16S rRNA sequencing. Although the abundance of C. albicans is increased significantly in the COVID-19 positive group, the connection between PCT with C. albicans or SARS-CoV-2 infection remains to be determined. It is possible that the treatment or medication history of the enrollments may affect the biomarkers for the acute infection. To determine the connection between PCT levels and C. albicans or SARS-CoV-2 infection, additional population with SARS-CoV-2 infection transition to negative should be recruited. By examining a more diverse population and accounting for variables such as treatment history, we can better understand the potential links between these factors.

There are several limitations in our study. Firstly, we had a low number of samples from both groups, which may limit the generalizability of our findings. Additionally, information regarding comorbidity was missing, which could impact our comprehensive understanding of host-microbe interactions. Furthermore, the 16S rRNA and ITS sequencing data we utilized only pertained to microbial pathways, thus necessitating the use of RNA-seq for further exploration of host pathways. Regarding the 16S rRNA sequencing, we specifically amplified the V1-V3 region, which is more favorable for Staphylococcus and may result in missing information about other microbes. To address these limitations, future studies should consider increasing the sample size, improving the collection of comorbidity information, and incorporating more comprehensive host genome sequencing techniques such as RNA-seq, whole genome sequencing (WGS), and targeted sequencing. Additionally, the identification of Sap through Western Blotting (WB) should be included to obtain more comprehensive and accurate results.

Participant enrollment and samples collection

From January 9th to 11th, 2023, a total of 94 sputum samples were collected from patients. After preliminary screening based on clinical diagnosis, we identified 51 patients with respiratory tract infections. Subsequently, through nucleic acid testing for SARS-CoV-2, we further screened 35 patients and divided them into two groups based on their test results: 17 patients who tested positive for the virus were included in the positive group, while 18 patients who tested negative constituted the negative group.

The human samples collection was approved by the ethics committee of Ren Ji Hospital, School of Medicine, Shanghai Jiao Tong University. In addition, the written informed consents were received from all individuals.

DNA extraction and PCR amplification

Microbial DNA was extracted from sputum samples using the E.Z.N.A.® Soil DNA Kit (Omega Bio-tek, Norcross, GA, U.S.) following the manufacturer’s protocols. For bacterial analysis, the V1-V3 region of the 16S ribosomal RNA gene was amplified by PCR using specific primers for this region (8F:5’- AGAGTTTGATCCTGGCTCAG-3’ and 533R: 5’- TTACCGCGGCTGCTGGCAC-3’). Similarly, for fungal analysis, the ITS1-ITS2 region of the ITS ribosomal RNA gene was amplified by PCR using specific primers (ITS1F: 5’- CTTGGTCATTTAGAGGAAGTAA-3’ and ITS2R: 5’- GCTGCGTTCTTCATCGATGC-3’). The amplified DNA products were then pooled and used to construct an Illumina Pair-End library following Illumina’s genomic DNA library preparation procedure. The amplicon library was subsequently subjected to paired-end sequencing (2*250) on an Illumina MiSeq platform (Shanghai BIOZERON Co., Ltd) according to standard protocols. To ensure data accessibility and availability, the raw sequence reads obtained from the sequencing process were deposited into the NCBI Sequence Read Archive (SRA) database under the designated Accession Number: PRJNA1013128 and PRJNA1013618.

Data analysis

Figure S2A and S2B showed the Shannon-Wiener index for 16S rRNA and ITS sequencing, which indicates that the sequencing data volume is sufficiently large to reflect the majority of microbial information in the sample. The OTU (Operational Taxonomic Units) were clustered using UPARSE (version 7.1, http://drive5.com/uparse/ ) with a 97% similarity cutoff. Chimeric sequences were identified and removed using UCHIME [ 40 ]. Rarefaction analysis, based on Mothur v.1.21.1 literature, was conducted to assess diversity indices such as Chao, ACE, and Shannon diversity indices [ 41 ]. Beta diversity analysis was performed using UniFrac literature to compare the results of principal component analysis (PCA) [ 42 ]. The community ecology package R-forge was utilized, and the Vegan 2.0 package was used to generate a PCA figure. For the identification of biomarkers for highly dimensional colonic bacteria, LEfSe (linear discriminant analysis effect size) analysis was conducted following specific literature [ 43 ]. The Kruskal-Wallis sum-rank test was employed to examine changes and dissimilarities among classes, followed by LDA (Linear Discriminant Analysis) analysis to determine the effect size of each distinctively abundant taxa, as mentioned in relevant literature [ 44 ]. Phylogenetic Investigation of Communities by Reconstruction of Unobserved States (PICRUSt) ( http://picrust.github.io/picrust/tutorials/genome_prediction.html ) program based on the Kyoto Encyclopedia of Genes and Genomes (KEGG) database was used to predict the functional alteration of microbiota in different samples.

Isolation and identification of microorganisms

All the sputum samples collected from the recruitment process were mixed with 5 ml of sterile saline. After vortexing, 100 µl of each sample was cultured on sheep blood agar at 37 °C for 24 h to isolate bacteria. At least 24 colonies were selected and subjected to species identification using MALDI-TOF-MS (Bruker Daltonics, Bremen, Germany). For species identification, a small amount of bacteria was spotted onto a steel target plate and then treated with 1 µl of 10% formic acid (Sigma F0507). This mixture was dried for 5 min at 75 °C. Next, 1 µl of MALDI matrix saturated solution (consisting of a-cyano-4-hydroxycinnamic acid from Sigma 70,990 in 50% acetonitrile / 2.5% trifluoroacetic acid) was added to the bacteria. The plate was then analyzed using the MALDI-TOF MS system. The spectrum was obtained in linear positive-ion mode within the range of 2000 to 20,000 Da. Each spot was measured manually at five different positions, with 1000 laser shots at 25 Hz in groups of 40 shots. The acquired spectra were analyzed using MALDI Bruker Biotyper 3.0 software and library (Bruker Daltonics).

Reagent formulation

Bovine serum albumin plate: A 10% solution of bovine serum albumin was prepared by dissolving 20.0 g of glucose, 0.2 g of yeast extract powder, 1.0 g of KH2PO4, and 20.0 g of agar in 1 L of distilled water. The pH was adjusted to 5.6 ± 0.2. The bovine serum albumin solution was added to a final concentration of 1%. The plates were poured after thorough mixing.

Egg yolk agar medium: A medium was prepared by dissolving 40.0 g of glucose, 10.0 g of peptone, 63.6 g of NaCl, 0.6 g of CaCl2, and 15.0 g of agar in 1 L of distilled water. The pH was adjusted to 5.6 ± 0.2. was After autoclaving, the medium was cooled to 50 °C, and mixed with 100mL of egg yolk emulsion, and the plates were poured after thorough mixing.

Detection of secretory hydrolase activity

The suspension for pathogens were adjusted with a turbidity of 0.5 McFarland in PBS buffer. After diluted at a ratio of 1:200, the pathogen was spotted on the agar plates. The plates were incubated at 37 °C incubator after drying. The diameter of the colonies and the diameter of the halo were measured after incubating for 72 h. The Pz value was calculated using the formula: Pz = Colony diameter / (Colony diameter + Halo diameter). A higher Pz value indicates less secretion of hydrolases. Pz ≤ 0.59 is considered high expression of hydrolases; Pz values between 0.6 and 0.79 indicate moderate expression of hydrolases, Pz values between 0.8 and 1 indicate low-level expression of hydrolases, and Pz = 1 indicates no expression of hydrolases.

Statistical analyses

Statistical analysis was conducted using IBM SPSS 27 and GraphPad Prism 9. For quantitative data such as age, PCT, IL-6, independent-sample t-tests, Mann-Whitney tests, and Kruskal-Wallis tests were employed for statistical analysis. For categorical data such as gender, Chi-Square tests were used. A significance level of P  < 0.05 was considered statistically significant.

Data availability

All data generated or analyzed during this study are included in this published article. The datasets generated and analyzed during the current study are available in the NCBI repository, the IDs of 16S rRNA and ITS sequencing are PRJNA1013128 and PRJNA1013618.

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The study was supported by the National Natural Science Foundation of China (grant 82072235).

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Department of Laboratory Medicine, Ren Ji Hospital, Shanghai Jiao Tong University School of Medicine, 160 Pujian Road, Shanghai, 200127, China

Xilong Zhang, Nadira Nurxat, Qichen Wang & Qian Liu

College of Health Science and Technology, Shanghai Jiao Tong University School of Medicine, Shanghai, 200025, China

Xilong Zhang, Jueraiti Aili & Yakupu Yasen

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X.Z. and N.N. collected the samples and performed sequencing. J.A., Y.Y. and Q.W. were involved in analysis and interpretation of the results. X.Z. wrote the first draft of the manuscript, and all authors edited, reviewed, and approved the final version of the manuscript. Q.L. was involved in the conception and design and, as such, had full access to all the data in the study.

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Zhang, X., Nurxat, N., Aili, J. et al. The characteristics of microbiome in the upper respiratory tract of COVID-19 patients. BMC Microbiol 24 , 138 (2024). https://doi.org/10.1186/s12866-024-03281-w

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Respiratory Tract Infections and Laboratory Diagnostic Methods: A Review with A Focus on Syndromic Panel-Based Assays

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Respiratory tract infections (RTIs) are the focus of developments in public health, given their widespread distribution and the high morbidity and mortality rates reported worldwide. The clinical spectrum ranges from asymptomatic or mild infection to severe or fatal disease. Rapidity is required in diagnostics to provide adequate and prompt management of patients. The current algorithm for the laboratory diagnosis of RTIs relies on multiple approaches including gold-standard conventional methods, among which the traditional culture is the most used, and innovative ones such as molecular methods, mostly used to detect viruses and atypical bacteria. The implementation of molecular methods with syndromic panels has the potential to be a powerful decision-making tool for patient management despite requiring appropriate use of the test in different patient populations. Their use radically reduces time-to-results and increases the detection of clinically relevant pathogens compared to conventional methods. Moreover, if implemented wisely and interpreted cautiously, syndromic panels can improve antimicrobial use and patient outcomes, and optimize laboratory workflow. In this review, a narrative overview of the main etiological, clinical, and epidemiological features of RTI is reported, focusing on the laboratory diagnosis and the potentialities of syndromic panels.

1. Introduction

Respiratory tract infections (RTIs) are the focus of developments in public health, given their widespread distribution and the high morbidity and mortality rates reported worldwide [ 1 ]. The RTIs are defined as diseases of infectious etiology involving the respiratory system [ 2 ]. The clinical spectrum ranges from asymptomatic or mild infection to severe or fatal disease, and the severity is the result of the interaction between three factors: the causative agent, the environmental conditions, and the host [ 1 ]. These infections typically occur as acute disease with a rapid clinical onset ranging from hours to days after the infection and including a variety of symptoms such as fever, cough, sore throat, coryza, shortness of breath, wheezing, and/or difficulty in breathing [ 1 ]. The epidemiology of RTIs is continually evolving following rapid sociodemographic changes and certainly climate change [ 3 , 4 ]. In addition to being the deadliest infectious diseases worldwide, especially among children and elderly, RTIs are the most frequent reason for consultation or admission to health-care facilities and primary care, and they are reported to have a significant impact on the increasing requests for medical examinations at both medical offices and emergency departments, on antimicrobial prescriptions, and on hospitalizations [ 1 , 5 ]. In addition, new epidemiological data highlight the considerable impact of RTIs on the quality and the expectancy of life, as well as the severe threat to populations and global public health [ 4 ]. The epidemiological study of RTIs must keep up with the rapid changes in sociodemographic and climate dynamics and needs continuous updating in order to provide important tools for health policies of control and prevention. A prompt and rapid laboratory diagnosis of RTIs is required to support and to guide clinical decisions in favor of appropriate patient management, while also avoiding the inappropriate use of antimicrobials. As a matter of fact, the delay in identifying the causative agent of RTIs could lead to the emergence and spread of antimicrobial-resistant pathogens due to the misuse of broad-spectrum empirical therapy, thus resulting in poor clinical outcomes, increased mortality rates and length of hospital stay [ 6 , 7 , 8 ].

Important technological advances have been made over the years to provide new tools for the detection of both bacterial and viral respiratory infections, resulting in the development of accurate, fast, and easy-to-use diagnostic methods [ 9 ]. In particular, molecular methods are now widely available in diagnostic laboratories. These molecular-based techniques allow sensitive and highly specific detection of both bacterial and viral nucleic acids directly in the clinical specimens and in the cell culture supernatants, without requiring the long incubation period needed for bacterial or viral isolation [ 9 ]. In addition, molecular methods involve less technical expertise than culture and are useful for the detection of “difficult to grow” bacteria and of viruses that do not proliferate in standard cell cultures [ 9 ].

In this context, the introduction of syndromic panels broke new ground in the field of diagnostic microbiology, since they provide a highly powerful tool capable of detecting a broad array of pathogens that, collectively, could cause a single clinical syndrome; this was achieved by meeting the needs of accuracy and of the shortening of time-to-result [ 9 , 10 ]. In this review, a narrative overview of the main etiological, clinical, and epidemiological features of RTIs is reported, with a focus on the laboratory diagnosis and the potentialities of syndromic panels.

2. The Epidemiology of RTIs

RTIs are the deadliest diseases of infectious etiology, and the fourth leading cause of mortality worldwide, with 2,603,913 deaths globally reported in 2019 [ 4 , 11 ].

At present, for the COVID-19 pandemic alone, over 567 million confirmed cases and over 6.3 million deaths have been reported globally [ 4 , 11 ].

In addition, this type of infection is recognized for its significant contribution to loss of life expectancy (LE), with high rates of disability-adjusted life years (DALYs) estimated each year [ 4 , 11 ]. The disease burden of RTIs shows an uneven distribution at both a demographical and geographical level and differs widely by age, gender, and among countries and regions [ 4 ]. The negative impact of RTIs on life quality is particularly significant for infants, children, and the elderly, among whom the highest mortality and morbidity rates are also reported, especially in low- and middle-income countries [ 4 , 11 , 12 ]. Both the pediatric and the elderly populations are shown to be the most vulnerable to RTIs worldwide in terms of mortality and loss of LE. Concerning the pediatric population, the highest mortality and DALY rates are reported among children of less than 1 year [ 11 , 12 ], while among the elderly population, the people over 70 account for the greatest number of deaths and loss of LE. Such disparity in terms of demographic distribution is also observed with regard to the geographic spread of RTIs, largely affected by the degree of socioeconomic development. Low-, and the middle-income countries and territories [ 13 ] are more susceptible to RTIs, accounting for the highest mortality and DALY rates [ 4 , 11 , 12 ]. As concerns high-income countries, where high aging indexes are accounted for, a large number of aged people are at greater risk of infection and hospitalization, resulting in an increasing trend in morbidity, mortality, and loss of LE due to RTIs [ 4 , 11 , 12 ]. It is worth noting that in high-income countries, many deaths associated with RTIs occur in aged care facilities and in nursing homes; this suggests a high rate of transmission of RTIs in such settings, with reported significant mortality rates and loss of LE for the elderly [ 4 ]. Similarly, the pediatric population in high-income countries is at high risk of RTIs due to their attendance at daycare services and schools, which are ideal environments for the transmission of this type of infectious disease.

3. The Human Respiratory Tract and the Classification of RTIs

The human respiratory tract is divided into two contiguous spatial environments: the upper tract consisting of the tonsils, nasopharynx, oral cavity, oropharynx, and larynx, and the lower tract which includes the trachea, bronchi, and lungs. Therefore, RTIs are classified as upper respiratory infections (URIs) and lower respiratory infections (LRIs), based on the respiratory tract involved [ 14 ]. In this review, the respiratory infections caused by mycobacteria will not be discussed, since mycobacterial infections are not included in the routine laboratory diagnostic workflow and in syndromic panels.

3.1. Upper Respiratory Tract Infections (URIs)

URIs involve the mucous membranes lining the upper respiratory tract from the nostrils and the mouth to the vocal cords in the larynx, also including the paranasal sinuses and the middle ear [ 14 ]. According to the International Statistical Classification of Diseases [ 15 ], a URI can occur as acute nasopharyngitis (AN), acute sinusitis (AS), acute pharyngitis (AP), acute tonsillitis (AT), acute laryngitis (AL), and laryngotracheitis or laryngotracheobronchitis (LTB) ( Figure 1 ). The majority of URIs have a viral etiology; however, some of these infections are triggered by bacteria.

Classification of the URIs with the associated most relevant causative agents.

3.1.1. Acute Nasopharyngitis (AN)

AN is also known as rhinopharyngitis, acute coryza, or, most commonly, a cold. A cold is inflammation of the nasal and the pharyngeal mucosa mainly caused by infection with rhinovirus (RV) [ 15 , 16 ]. AN is a seasonal infectious disease, particularly spread during the autumn and the winter months, and 90% of cases are due to a viral causative agent. A long stay in indoor crowded environments during the cold season increases the probability of contagion; moreover, most of the respiratory viruses thrive in the low humidity of winter [ 16 ]. In addition to RV, Coronavirus (Co-V), Adenovirus (ADV), Influenza (FLU) virus and the Parainfluenza virus (PIV) can cause AN [ 15 , 16 ]. Patients with such infectious diseases complain of cough, pharyngeal pain, a running nose, and a stuffy nose as local symptoms, and increasing fever, general fatigue, and headache as general symptoms [ 15 ]. Most of the cases are self-limited and resolve in 7 to 10 days without treatment, although some symptoms last up to three weeks [ 15 , 16 ].

3.1.2. Acute Sinusitis (AS)

AS of infectious etiology occurs as mucosal inflammation of one or more of the paranasal sinuses (maxillary, ethmoid, frontal, and sphenoid) [ 15 ].

Similar to AN, the symptoms of infectious AS include nasal congestion and discharge, facial pain over the sinuses, dysosmia, and cough with a mild improvement after 5 to 7 days [ 15 ]. The clinical outcome could become worse, with purulent nasal discharge at the middle meatus, olfactory cleavage, maxillary tooth pain, and unilateral maxillary sinus tenderness reported [ 15 , 16 ]. When a worsening of symptoms arises, bacterial etiology is suspected and usually involves Streptococcus pneumoniae , Haemophilus influenzae , or Moraxella catarrhalis [ 16 ], whereas Staphylococcus aureus , Gram-negative bacilli, Streptococcus spp., and anaerobic bacteria are associated more frequently with subacute, chronic, or healthcare-associated sinusitis [ 17 ].

3.1.3. Acute Pharyngitis (AP)

AP is defined as inflammation and/or irritation of the mucous membrane of the oropharynx and represents one of the major reasons for outpatient and primary care visit, as well as one of the most common infectious illnesses encountered by general practitioners [ 16 ]. Infectious AP especially occurs during the colder months, with peaks of incidence in late winter and early spring, especially among school-aged children and adolescents given the high spread rate of this infectious disease in daycare and schools; adults can be also affected by infectious AP but at lower rates [ 15 , 16 , 18 , 19 , 20 , 21 ]. Although AP can be caused by many different types of pathogens, most cases have a viral origin [ 15 , 16 , 19 , 20 , 21 ]; in particular, RV and ADV are reported as the primary viral causes of AP, followed by FLU A and FLU B, PIV, Co-V, human metapneumovirus (h-MPV), respiratory syncytial virus (RSV), coxsackievirus, and human bocavirus (h-BocaV). However, cases associated with herpes simplex viruses 1 and 2 (HSV1, 2), Epstein-Barr virus (EBV), human cytomegalovirus (h-CMV), and to the human immunodeficiency virus (HIV) type 1 are also described [ 18 , 21 , 22 ]. Concerning bacterial etiology, Group B and C β-hemolytic Streptococcus spp., Chlamydia pneumoniae , Mycoplasma pneumoniae , Candida spp., mixed anaerobes, Arcanobacterium haemolyticum , Fusobacterium necrophorum , Neisseria gonorrhoeae , and Corynebacterium diphteriae are frequently identified as causative agents of AP, but many of the cases are due to Streptococcus pyogenes as the leading exponent [ 18 , 21 , 23 ].

The clinical spectrum of AP includes a broad range of signs and symptoms, which tend to vary depending on the causative agent. Usually, the typical symptoms of AP include discomfort of the throat, throat pain, and swallowing pain, often accompanied with pharyngeal erythema, hyperaemic palatine tonsils, and swelling of the lymphoid follicles of posterior wall of the pharynx [ 15 , 21 ]. If viral in etiology, AP often manifests with coughing, rhinorrhea, conjunctivitis, headache, and rash. When Epstein–Barr virus-associated AP (e.g., infectious mononucleosis) occurs, patients may complain of fever, tonsillar hypertrophy, myalgia, general fatigue, and anterior and posterior lymphadenopathy. Regarding the bacterial origin of the AP, Group A β-hemolytic streptococcal (GAS) pharyngitis is the most prevalent and arises with an acute clinical onset including fever, tonsillar exudates, edematous uvula, and palatine petechiae [ 16 , 21 ]. Viral AP is self-limited, with symptoms lasting from 5 to 7 days, and the clinical course usually resolves without any complication [ 16 ]. If not diagnosed and adequately treated, AP can result in serious complications, especially with regard to bacterial cases: untreated GAS pharyngitis can lead to severe sequelae such as peritonsillar abscess, parapharyngeal and retropharyngeal abscess, painful cervical lymphadenitis, sinusitis, otitis media, mastoiditis, sepsis, meningitis, rheumatic fever, poststreptococcal sequelae (i.e., glomerulonephritis), and scarlet fever [ 16 , 19 ].

3.1.4. Acute Tonsillitis (AT)

AT often occurs when an infectious process of the mucosal oropharynx also involves the palatine tonsils, which are bundles of lymphatic tissue located between the palatoglossal arch anteriorly and the palatopharyngeal arch posteriorly [ 24 ]. Even though infectious AT usually spreads in winter and early spring, the disease tends to be quite recurrent throughout the year [ 24 ].

As well as infectious AP, the etiology of the AT can be either viral or bacterial. Viral AT is quite common and the main causative agents are the same as those of a cold, namely RV, RSV, ADV, and Co-V. On the other hand, although bacterial AT can be caused by different aerobic and/or anaerobic pathogens, most of the cases are due to Streptococcus pyogenes , Staphylococcus aureus , Streptococcus pneumoniae , and Haemophilus influenzae [ 24 , 25 ]. Infectious AT usually occurs with swollen tonsils, with associated odynophagia and dysphagia, sore throat, difficulty swallowing and, occasionally, purulent plugs in the tonsillar crypts, high fever, headache, and general fatigue [ 15 , 24 ]. In most viral-origin cases, the prognosis is favorable, and the infectious process resolves spontaneously without requiring hospital admission and/or antimicrobial treatment [ 25 ]. Patients with infectious AT commonly recover within a few days without any complications or long-term problems [ 24 ]. However, although AT is generally associated with good clinical outcomes, complications can arise when the infection extends to the peritonsillar space, with the subsequent formation of peritonsillar abscesses, especially in cases of a bacterial origin and/or delayed or inadequate antimicrobial therapy [ 15 , 24 ].

3.1.5. Acute Laryngitis (AL) and Laryngotracheobronchitis (LTB)

AL is defined as inflammation of the larynx, resulting in erythema and oedema of the laryngeal mucosa with consequent huskiness or loss of the voice, harsh breathing, dysphonia, and/or a painful dry cough [ 15 , 26 , 27 ]. Such a clinical feature is one of the most common infectious diseases encountered by primary care physicians, especially among school-aged children, adolescents, and adults, with the same seasonal trend observed for URI [ 27 ]. Laryngitis typically occurs with an acute onset because of the spread of viral URIs involving the adjacent structures of the upper respiratory airways, either by directly infecting the laryngeal tissues or by stimulating excessive secretions that lead to inflammation [ 26 , 27 ]. All the major respiratory viruses are etiologically associated with AL; in particular, PIV, RV, FLU, and ADV are the most reported [ 26 , 27 ]. On the contrary, bacterial etiology of AL is rare but cannot be ruled out. In particular, M. catarrhalis and H. influenzae are the most recovered bacteria in patients with AL, thus suggesting their potential involvement in the pathogenesis of such infectious disease [ 27 ]. Before the vaccination era, C. diphtheriae was the main bacterial pathogen involved in laryngeal infectious disease. Nowadays, acute laryngitis secondary to diphtheria is rare; however, such cases can occur in unvaccinated populations [ 26 , 27 ]. Other bacterial pathogens identified in patients complaining of symptoms of AL include Group A and G β-hemolytic Streptococcus spp., methicillin-resistant Staphylococcus aureus (MRSA), C. pneumoniae , M. pneumoniae , and Bordetella pertussis . These two latter pathogens are thought to be especially involved in the pathogenesis of chronic laryngitis in adults [ 27 ]. The disease is usually mild and self-limited, and symptoms resolve in an average of 3 days.

Given the crossroad position of the larynx, located between the upper and the lower respiratory system, any infectious disease affecting this anatomical site can easily spread to the surrounding organs, and to the proximal tract of the tracheobronchial tree, also involving its distal portion [ 26 , 28 ]. This condition is referred to as laryngotracheitis or laryngotracheobronchitis (LTB). LTB, more commonly referred to as croup, results from a mucosal inflammation of the subglottic area due to a viral infection of the neighboring anatomical structure [ 26 , 28 , 29 ]. Such acute disease is an age-specific clinical syndrome since it exclusively affects children between 6 months and 3 years old [ 28 , 29 ]. This pediatric age group is the most prone to the edematous consequences associated to the infection, resulting in the obstruction of the upper respiratory airways, leading to a barking cough, hoarseness, and inspiratory stridor [ 28 , 29 ].

Regarding the etiology, PIV type 1 is the most common viral cause of croup, followed by PIV type 2. Other viruses such as RSV, ADV, and measles (at the onset of measles disease, when mucositis occurs) are a few of the other agents associated with viral croup [ 26 , 29 ]. LTB presents with an acute onset and usually resolves within 2 days in most children [ 29 ]. Mucosal damage and the obstruction of the upper airways due to croup are predisposing factors for other infectious diseases such as the bacterial epiglottitis and tracheitis that, unlike the viral processes, occur with a rapid progressive course, high fever, a toxic appearance, and drooling [ 26 , 28 , 29 ].

Epiglottitis is inflammation of the epiglottis and supraglottic structures characterized by marked swelling of the epiglottic mucosa, and is associated with a high risk of acute and complete airway obstruction, especially in young children [ 26 , 28 , 29 ]. Before the vaccine introduction, the main causative agent of epiglottitis was H. influenzae serotype 1, although H. influenzae serotypes A and F and non-typeable strains, Streptococcus pyogenes and Staphylococcus aureus , were also reported in sporadic cases [ 26 , 28 , 29 ].

Bacterial tracheitis is an invasive and exudative bacterial infection of the soft tissues of the trachea, resulting in a strikingly rapid onset and progression of the illness, with high fever and a toxic appearance. The main causative agents are to be searched among the inhabitants of the oropharyngeal microbial population such as Staphylococcus aureus , and Streptococcus pyogenes , or Streptococcus pneumoniae , also followed by Gram-negative enteric bacteria such as Escherichia coli , Klebsiella pneumoniae , and Pseudomonas aeruginosa [ 26 , 28 , 29 ]. The onset of both bacterial epiglottitis and tracheitis mimics that of common and usually benign croup; however, their clinical features could lead to potential life-threatening outcomes [ 29 ].

3.2. Lower Respiratory Tract Infections (LRIs)

LRIs are acute infectious illnesses involving the bronchi, bronchioles, alveoli, and lungs. The term LRIs is a broad definition that refers to a variety of infectious inflammatory diseases of the lower respiratory airways, among which acute bronchitis (AB), acute bronchiolitis (ABR) and pneumonia are major matters of concern ( Figure 2 ).

Classification of LRIs with the associated most relevant causative agents.

3.2.1. Acute Bronchitis (AB)

AB is defined as brief, self-limited inflammation in response to an infectious process that involves mucosa lining the large and mid-sized airways, mainly resulting in acute cough with or without sputum production [ 15 , 30 , 31 ]. Although it is a recurrent year-round clinical syndrome, AB mostly occurs during the cold. This infectious disease is primarily caused by a viral infection, with variable rates of prevalence according to the epidemiology of the viral pathogen involved [ 30 , 31 , 32 , 33 ]. The main viruses identified as leading viral causes of AB include FLU A and B, PIV, RSV, and h-MPV, as well as common upper respiratory viruses, such as RV, Co-V, and ADV [ 30 , 31 ]. In particular, FLU A and B viruses are responsible for winter outbreaks of AB in both children and adults because of their high rates of transmission during the cold months and their efficiency in infecting and damaging the bronchiolar epithelial cells [ 30 , 31 ]. Approximately 10% or fewer of the AB cases are referred to atypical bacteria, especially C. pneumoniae , M. pneumoniae , and Bordetella pertussis [ 30 , 31 ]. These latter two are associated with more severe cases of AB with long periods of incubation. Although bacterial species are rarely associated with AB, there is wide evidence of their key role in the pathogenesis of acute exacerbations of chronic bronchitis (AECB), a different clinical syndrome caused by multiple factors such as environmental exposure, infections, inflammation, and genetic predisposition [ 34 ]. S. pneumoniae , H. influenzae , and M. catarrhalis represent the main colonizing bacteria of the lower airways in AECB, with local findings of P. aeruginosa, Stenotrophomonas maltophilia , and Enterobacteriaceae in patients with a high degree of functional pulmonary impairment [ 34 ].

The clinical course and the severity of the symptoms associated with AB vary according to the causative agent; in mild cases, the illness lasts from 7 to 10 days, whereas more severe cases persist for up to 3 weeks [ 30 , 31 ].

3.2.2. Acute Bronchiolitis (ABR)

ABR occurs as infection-induced inflammation of the respiratory epithelium lining the bronchioles, resulting in the obstruction of these smaller airways and consequent wheezing commonly associated with fever, cough, rhinorrhea, dyspnea, and tachypnea [ 15 , 26 , 29 ]. This clinical syndrome is age-specific, since it typically affects children younger than 2 years, with an incidence peak occurring between 2 and 6 months of age [ 26 , 29 ]. With regard to epidemiology, ABR has a yearly seasonal pattern that varies according to the geography, the climate, and the causative agent [ 29 ]. The recognized causative agents are only viruses, with RSV identified as the major causative pathogen [ 33 , 35 ]. RSV represents the principal agent in two thirds of the cases of bronchiolitis, with high rates encountered in hospitalized patients: RSV-associated diseases have caused an estimated 1.8 million hospital admissions and 40,000 deaths among children [ 26 , 29 , 33 , 35 ]. In addition, RSV is the leading cause of hospitalization for ABR in the first year of life [ 29 , 33 , 35 ]. Other viruses may play a role in the pathogenesis of ABR, including h-MPV, RV, FLU, PIV serotypes 1–3, ADV, h-BocaV, and Co-V (in particular, NL63, HKU1, 229E, and OC43 species) and they are usually involved as coinfecting agents [ 29 , 33 , 35 ]. An acute course of ABR usually lasts from 3 to 7 days. A minority of children complain of severe symptoms such as hypoxemia, apnea, or respiratory failure and require admission to intensive care. In most cases, the clinical conditions of the hospitalized children with ABR tend to improve within 3 to 4 days with a median 2-week recovery period [ 26 , 29 ].

3.2.3. Pneumonia

Pneumonia is an acute infection of the pulmonary parenchyma causing mild to severe illness in people of all ages [ 36 ].

Among all the infectious diseases affecting the respiratory system, pneumonia has the greatest impact on public health since it remains a leading cause of hospitalization and death worldwide. In particular, higher rates of mortality due to pneumonia are reported in children, among whom the disease accounts 14% of all deaths of children under five years old, and 22% of all deaths in children aged 1 to 5 [ 36 ]. Pneumonia affects children and families worldwide, but the mortality rates are highest in South Asia and Sub-Saharan Africa [ 36 ].

Two types of pneumonia are recognized based on both their clinical presentation and their etiology. The most frequent is typical pneumonia caused by pyogenic bacteria (typically S. pneumoniae ) and currently named bacterial pneumonia; this presents with the typical symptoms including hyperpyrexia (>38.5 °C), a productive cough and general malaise. The other type is interstitial pneumonia, mainly caused by viruses and atypical bacteria (i.e., RSV, Legionella ) and presenting with poor symptoms such as a dry and irritating cough and mild fever (no more than 38 °C). Chest imaging of typical pneumonia reveals the obstruction of alveoli by purulent material, limiting the space; it often involves a pulmonary lobe, and a ground-glass picture in cases of interstitial pneumonia, due to viruses evolving until typical alveolar obstruction in the case of legionnaires’ diseases by Legionella pneumophila [ 37 ].

The most common categories of pneumonia include community-acquired pneumonia (CAP) and hospital-acquired pneumonia (HAP). CAP is due to an infection acquired outside of the hospital setting, while HAP occurs among intubated patients after at least 48 h of hospitalization [ 38 ]. Moreover, HAP includes two minor subcategories known as ventilator-associated pneumonia (VAP) and healthcare-associated pneumonia (HCAP) [ 38 ]. VAP involves patients receiving mechanical ventilation and symptoms with a 48–72 h incubation time-period after endotracheal intubation [ 38 ]. HCAP frequently spreads in lower-acuity health care settings such as nursing homes and dialysis centers [ 38 ]. Hemorrhagic alveolitis pneumonia due to Pneumocystis jirovecii is also reported in immunocompromised patients including those with HIV infection [ 36 ].

A wide variety of agents, including bacteria, viruses, and fungi, can avoid or overwhelm the immune defenses of both the upper respiratory and the lower respiratory tract ( Table 1 ), thus colonizing the parenchyma of the lungs and triggering the infectious process. If bacterial in etiology, the pathogenesis mainly involves the lung parenchyma and the alveoli, resulting in the clinical spectrum of typical pneumonia. On the contrary, when the infectious process affects the extra-parenchymal pulmonary interstitial tissue, interstitial pneumonia occurs and it is usually due to viruses (i.e., h-CMV, FLU A, and RSV), and rarely to bacteria such as Legionella spp., M. pneumoniae , and C. pneumoniae.

The main etiological agents of pneumonia.

Abbreviations: * SARS-CoV: Severe Acute Respiratory Syndrome Coronavirus. ** MERS: Middle East Respiratory Syndrome.

Regarding the bacterial etiology, S. pneumoniae is certainly the leading causative pathogen, accounting for more than 25% of community-acquired pneumonia cases worldwide and the most common cause of bacterial pneumonia in children [ 36 , 38 , 39 ]. Pneumococcal pneumonia is the most common CAP [ 40 ]. S. aureus is frequently isolated from patients with HAP, HCAP, and VAP with the major rates accounted for in intensive care units [ 38 , 40 ]. In particular, the impairment of host defenses in hospitalized patients represents a predisposing factor to the colonization of the oropharynx by S. aureus , thus contributing to the development of a S. aureus -associated pneumonia [ 40 ]. In certain cases, pneumonia due to S. aureus results from a complication of the widespread dissemination of staphylococcal microorganisms through the bloodstream [ 40 ]. The Gram-negative bacteria may also be involved in the pathogenesis of pneumonia, especially, K. pneumoniae , P. aeruginosa , and H. influenzae [ 38 , 40 ]. This latter, in particular, H. influenzae type b (Hib), is reported as the second most common cause of bacterial pneumonia [ 36 ]. It is worth noting that Gram-negative-bacteria-associated pneumonia normally occurs in the context of hospitalization, a stay in a chronic care facility, the presence of co-morbidities, compromised host defenses, and recent antibiotic therapy [ 38 , 39 , 40 ]. Moreover, these predisposing factors contribute to the development of infectious processes carried by multidrug-resistant bacteria such as methicillin-resistant S. aureus (MRSA) and extended-spectrum β-lactamase (ESBL)-producing Enterobacteriaceae [ 38 ]. The range of bacteria able to cause pneumonia also includes the anaerobic and the aerobic inhabitants of the microbial population of the oropharynx [ 40 ]. Such microorganisms may potentially lead to pneumonia as a consequence of the aspiration of oropharyngeal secretions into the tracheobronchial tree [ 40 ]. Patients who are bedridden with impaired consciousness or those with difficulty swallowing are at major risk of developing pneumonia due to such opportunistic pathogens [ 38 , 40 ].

The list of causative bacterial agents of pneumonia also includes obligate intracellular bacteria such as Legionella pneumophila , C. pneumoniae , and M. pneumoniae which are mainly responsible for epidemic and sporadic cases [ 38 , 40 ]. Viruses are also a common cause of pneumonia, especially in hospital settings, in immunocompromised patients and in the elderly. RSV has always been reported as the main viral cause of pneumonia [ 36 ], followed by FLU A and ADV [ 40 ], until the emergence of SARS-CoV-1, Middle East Respiratory Syndrome (MERS), and the novel SARS-CoV-2 in 2019; the latter is the etiological agent of the present Co-V disease (COVID 19), declared a pandemic by the WHO in March 2020. Before this date, viruses as a cause of frank pneumonia were diagnosed relatively infrequently, except in children. However, the emergence of SARS-CoV-2 certainly contributed to increased rates of viral pneumonia cases, since researchers have established its key role in the pathogenesis of interstitial pneumonia. The rate of interstitial pneumonia was significantly higher during the COVID-19 period (7.1%) compared with that found in the pre pandemic periods (5.15%) ( p < 0.001) [ 41 ].

It should be noted that the finding of a virus does not mean its involvement as a cause of pneumonia, since the disease could also occur as a result of viral infection and secondary bacterial coinfection [ 38 ]. The clinical severity of pneumonia is partially attributed to the etiological agent involved: the milder cases are commonly associated with S. pneumoniae , M. pneumoniae , C. pneumoniae , influenza virus, and ADV, whereas the most severe presentations usually involve S. aureus , L. pneumophila , and H. influenzae [ 38 ].

Distinguishing between bacterial pneumonia and viral pneumonia is of great importance, especially to avoid unnecessary antibiotic treatment. A diagnosis can be difficult to make with limited technical resources [ 8 ], and a combination of laboratory methods is mandatory to achieve the correct diagnosis and appropriate patient management with the administration of prompt targeted therapy; this is of great importance considering that typical pneumonia could evolve into sepsis and meningitis, both correlated with high mortality, and interstitial pneumonia could cause rapid onset respiratory failure and death [ 37 ].

4. Laboratory Diagnosis

Early and accurate diagnosis of an RTI is crucial for the adequate management of the patient in terms of the appropriate antiviral or antibacterial therapy, effective infection control measures, and the reduction of the hospital stay’s length [ 42 ]. Moreover, the laboratory diagnosis must include both microbiological and virological methods to be significantly informative in terms of outbreak management, epidemiological surveillance, antimicrobial susceptibility, and strain typing [ 43 ]. Despite the key role of the clinical laboratory, the microbiological/virological diagnosis of RTIs is still challenging given the complexity of such infections [ 44 ]. The quality and the diversity of the respiratory specimens, the difficult accessibility to certain anatomical respiratory structures, potential interferences due to the oropharyngeal microbial population, the wide variety of the respiratory pathogens, and the complex pathophysiology of the RTIs are a few of the considerable challenges to the differential diagnosis of these pathogens [ 42 , 44 ].

The diagnosis of RTIs primarily involves preliminary examination of the associated symptoms and signs, in order to define the key clinical question necessary to allow the clinical microbiologist to establish an adequate diagnostic workflow to be undertaken, starting from the selection of the appropriate respiratory specimen [ 43 ]. The collection, the transport, the storage, and the processing of the respiratory specimen is crucial for the reliability of the diagnostic results; therefore, physicians and laboratory workers should meticulously follow the reference guidelines to ensure the proper management of the sample [ 9 , 17 , 43 ].

The diagnostic workflow of RTIs historically relies on many tools to determine the microbial and viral etiology of these infections, such as microscopic examination, conventional culture, traditional cell cultures, antigen detection, and serology [ 8 , 42 , 43 ]. The implementation of new analytical approaches such as molecular methods [ 9 ] allows researchers to broadly maximize the direct detection of respiratory pathogens, especially those hardly detectable and for which the conventional culture is not a feasible identification method [ 43 ]. In addition, clinical microbiologists are currently experiencing new significant innovation in the field of molecular diagnostic approaches, such as syndromic panels [ 45 ]. In particular, respiratory syndromic panel-based assays allow the simultaneous detection and identification of multiple pathogens associated with the most severe respiratory syndromes [ 45 ].

The spectrum of available diagnostic methods for viral and microbial diagnosis is wide, and the knowledge of their associated advantages, limitations, and time-to-results is crucial to better interpret the results and to appropriately integrate the findings into their clinical management [ 9 ].

4.1. Specimen Collection

The detection of respiratory pathogens largely depends on several preanalytical variables and, certainly, on the type and the quality of the respiratory specimen. In particular, proper specimen management significantly impacts the laboratory diagnosis and the therapeutic decisions, the antibiotic stewardship, the hospital and laboratory costs, the patient care, the clinical outcomes, and the length of hospitalization; moreover, it drives the efficiency of the laboratory [ 17 ]. The timing of collection is the first essential condition to ensure accurate microbiological diagnosis and interpretability of the results [ 43 , 46 ]. According to the guidelines, specimens should be collected as early as possible in the acute stage of an infection, preferably prior to the administration of antimicrobial or antiviral drugs [ 17 , 43 , 46 ]. The respiratory specimens should be collected within 3 days of symptom onset and no later than 7 days, since the viral titer and the amount of bacteria tend to markedly diminish after 72 h from clinical onset [ 47 ].

The mode of transportation and the storage of the sample are crucial to preserve both the microbial and the viral characteristics of the specimen [ 9 , 43 ]. The samples should be delivered as quickly as possible to the laboratory. If the respiratory sample cannot be transported to the laboratory or processed within 1–2 h, the guidelines recommend its storage at −80 °C to −20 °C in order to preserve microbial community composition. Whenever this is not possible, the samples should be stored at 4 °C to 8 °C and processed the same day or the following day. It could also be possible to collect the specimens in specific collection tubes containing a preservation transport medium: if these collection tools are available, the sample could be stored for 24 h at room temperature or at 4 °C [ 48 , 49 ]. It is worth noting that the specimens for virus detection should be transported in suitable transport medium tubes [ 32 ] on wet ice at 2 °C to 8 °C and frozen at −80 °C if testing is delayed by >48 h [ 9 , 17 ]. On the basis of the suspected etiology, either bacterial or viral, the diagnosis of respiratory tract infections requires a specific type of specimen and collection method, as well as specific transport and storage conditions to optimize the diagnostic yield [ 9 , 17 ].

Although various respiratory specimens can be used for identifying the microbial and viral etiology of an RTI [ 43 , 46 ], only a few types are easily obtainable and recommended in terms of diagnostic utility [ 17 , 43 ].

With regard to URIs, their diagnosis is mostly based on the evaluation of the symptoms and the signs reported by the patient [ 8 , 17 , 43 ]. Although the diagnosis of a URI is mostly clinical, the guidelines recommend local microbiological sampling whenever a clinical impairment of the infection occurs or when the patient reports signs and symptoms attributable to AP [ 8 , 17 ]. When the laboratory diagnosis of a URI is required, the sampling tools recommended are nasopharyngeal washes, nasopharyngeal aspirates, nasopharyngeal swabs, oropharyngeal swabs, and combined nasopharyngeal and oropharyngeal swabs [ 17 , 50 ]. The nasopharyngeal aspirate and the nasopharyngeal wash are the specimens of choice for the detection of respiratory viruses, since large numbers of respiratory epithelial cells are aspirated during the collection process [ 17 , 43 , 50 ]. However, the collection of nasopharyngeal aspirates or the nasal washes is hardly feasible for widespread use in clinical practice, since it requires specific suction devices and skilled operators to obtain the specimens [ 43 ]. On the contrary, the collection of nasopharyngeal or oropharyngeal swabs is easier and painless and can also be performed outside the hospital setting. A range of commercial swabs are now available, including rayon-tipped swabs, polyester-tipped swabs (Dacron), and polyurethane sponges with wooden, plastic, or wire shafts [ 43 ].

When a viral URI is suspected, the clinical samples are usually collected using a Dacron swab and placed in a viral transport medium which contains antibiotics, a buffered salt solution, a proteinaceous substance (such as albumin, gelatin, or serum), and a pH indicator [ 9 ]. On the other hand, when a bacterial URI is suspected, Dacron or rayon swabs should not be the tool of choice for oropharyngeal sampling, since they hold small volumes of the sample (0.05 mL), with microbes harnessed within their fibers, thus affecting specimen collection in terms of quality and microbial quantity [ 17 ]. The flocked nylon swab is the most valuable tool for respiratory specimen collection, especially for the bacterial diagnosis of a URI, since it allows more efficient release of respiratory epithelial cells and oropharyngeal secretions [ 17 , 43 ]. In particular, the flocked nylon swab makes it easier to obtain bacteria and/or fungi on the solid media and allows a more homogeneous inoculum of the specimen on the agar plate [ 17 ].

The range of specimens available from the lower respiratory tract includes spontaneous, or less appropriately, induced sputum; bronchoscopy specimens; endotracheal aspirates; and, quite rarely, transthoracic lung aspiration. Given the expertise and technical skills required and the equipment needed, the collection of specimens other than sputum from the lower respiratory tract may be limited to clinically severe cases including hospitalized patients and life-threatening cases [ 47 ]. The collection of lower respiratory specimens is challenging given the “background noise” due to the commensal microbiota of the oropharynx, which could contaminate the specimen during the sampling, thus interfering with the interpretation of the results. For this reason, specimens from the lower respiratory tract require particular care during collection [ 9 , 17 ], and invasive techniques represent efficient and mostly sterile alternatives for pathogen identification. In terms of sterile techniques, bronchoalveolar lavages (BAL) is the most used [ 8 ].

4.2. Microscopy

Since lower respiratory specimens are likely to be contaminated during collection, microscopy represents a useful tool for assessing the quality of a sample before the culture, in order to overcome potential misinterpretations of the results [ 43 , 46 ]. Moreover, microscopy provides early and concise information about the infection, such as the presence of large numbers of polymorphonuclear (PMN) cells as markers of the inflammatory response, or the presence of bacteria with characteristic morphology [ 43 , 46 ]. The results of microscopic examination may provide early indication of the culture results and give guidance about treatment [ 43 , 46 ].

With regard to microbial URIs, microscopy following the Gram staining of upper respiratory specimens is useful for the detection of PMN cells and some characteristic bacteria such as C. diphtheriae and B. pertussis , especially in nasopharyngeal aspirate. Generally, Gram staining is not recommended as a reliable tool for the detection of other bacteria (such as streptococci causing pharyngotonsillitis or N. meningitidis in healthy carriers) since these cannot be distinguished from the nonpathogenic colonizers of the normal microbial population of the upper respiratory system [ 50 ]. Other staining methods such as Loeffler’s Methylene blue for C. diphtheriae can be used when specific clinical suspicion is reported to the laboratory [ 50 ]. When P . jirovecii -associated pneumonia is suspected, the gold-standard staining techniques recommended are direct or indirect immunofluorescence assays, which are proven to be highly sensitive and specific for different life stages, depending on the antibody used [ 51 ].

Gram staining and microscopic examination of the sample from a patient complaining of LRI is highly recommended for evaluating the suitability of the specimen. The quality of a lower respiratory specimen is especially evaluated by assessing the number of squamous epithelial cells (SECs) and PMN cells in a Gram-stained smear of the specimen [ 43 , 46 ]. In particular, the presence of a low number of SECs and a high number of PMN cells per low-power field are indicative of a high-quality specimen; on the contrary, specimens with relatively low numbers of PMN cells and high numbers of SECs are likely to represent oropharyngeal contamination and are recommended to be rejected for conventional culture [ 43 , 46 ]. For example, a number of SECs/100× objective microscopic field > 10 shows that the sputum sample contains saliva and is unsuitable; similarly, the presence of >1% SECs indicates contamination from the commensal microbiota of the upper respiratory tract and the sample is considered unacceptable. The specimens of the lower respiratory tract are also examined for inflammatory cells, the presence of bacteria and their characteristics, such as how they Gram stain, their shape, their layout, their number, and their intracellular or extracellular position, and the prevalence of a single microbial population [ 50 ]. The stained smears obtained from patients with aspiration pneumonia are characterized by many polymorphonuclear neutrophils and mixed intracellular respiratory flora (commonly streptococci and anaerobes), and should be discriminated from the contaminating respiratory microbiota. The presence of intracellular microbes in alveolar macrophages detected in BAL has high sensitivity and specificity for the diagnosis of VAP. On the basis of the Gram staining, bacteria sharing similar features to the most common respiratory bacterial pathogens should be considered in the interpretation of results, and their presence should be notified to clinicians to guide them for potential empirical therapy. On the contrary, if bacteria are insufficient in quantity or do not show Gram-staining characteristics attributable to a potential pathogen, they should be reported as normal respiratory flora [ 50 ].

Microscopy has also been a very important tool in the field of viral RTIs. In particular, electron microscopy has played a key role, even in recent times, in identifying novel viral strains causing epidemics such as, in the early 2000s, the first human Co-V-associated with the Severe Acute Respiratory Syndrome (SARS) [ 42 , 52 , 53 ]. However, despite its several advantages, the use of electron microscopy in the diagnosis of viral respiratory infections has some limitations: it is laborious, time-consuming, and requires considerable technical skill for accurate analysis, as well as strict control of experimental conditions and a high concentration of viral particles (>10 5 mL), with a turnaround time ranging from 3 to 16 h (including specimen preparation) [ 42 , 54 , 55 ]. For these reasons, electron microscopy directly applied to clinical samples is not recommended as a routine diagnostic method for respiratory infections, but rather, for the identification of viruses causing a cytopathic effect after virus cultivation [ 9 ].

4.3. Culture

Bacterial culture remains, at present, the gold-standard method for the isolation and detection of respiratory pathogens of the higher and lower respiratory tract, including atypical bacteria. However, it is considered a labor-intensive method that requires considerable technical expertise and long time-to-result. In addition, the reliability of such a method is not always guaranteed since it widely depends on the quality of the specimen, which suffers from the contamination that potentially occurs during sampling. Moreover, the culture results could be misinterpreted, especially when specimens are collected after starting antibiotic therapy. The growth of bacterial colonies is followed by the identification of the same ones using biochemical tests or, more recently, using matrix-assisted laser desorption/ionization time-of-flight (MALDI-TOF) mass spectrometry and antimicrobial susceptibility testing (AST) via several manual or automated methods, with a turnaround time of 48–96 h [ 42 ]. For these reasons, culture-based identification of a pathogen cannot be considered adequate for allowing a prompt diagnosis and targeted antibiotic therapy, which is required for optimal patient management [ 42 ].

With regard to URIs, pharyngeal samples are routinely cultured for Streptococcus pyogenes on 5% sheep blood agar or Group A Streptococcus selective blood agar (which is easier to visualize because it inhibits accompanying flora but delays the appearance of colonies), and the plates are checked for β-hemolytic colonies. Several other pathogens may cause pharyngotonsillitis or may colonize the upper respiratory tract without causing disease, and their isolation may be important in patients with ear, nose, and throat disorders [ 50 ]. Nasopharyngeal specimens are useful for the diagnosis of infection by B. pertussis , C. diphtheriae , and Chlamydophila spp., and moreover, for the detection of N. meningitidis , S. aureus , and S. pyogenes carriages. Such samples are usually inoculated on sheep blood agar or chocolate agar; then, they are aerobically incubated at 37 °C, in 5% CO 2 , for 48 h. When infection with B. pertussis or B. parapertussis is suspected, the samples should be inoculated on Regan-Lowe charcoal agar with 10% horse blood and cephalexin, and aerobically incubated under moist conditions at 35 °C, ranging from 5 to 7 days [ 50 ]. The specimens potentially containing N. meningitidis should be inoculated in Thayer–Martin or another selective medium that supports the growth of such microorganism while inhibiting the proliferation of the microbial population’s normal inhabitants of the upper respiratory airway (5% CO 2 at 35 °C for 72 h) [ 50 ].

A selective medium such as Canada colistin-nalidixic acid, or a selective and differential medium such as BBL CHROMagar S. aureus (BD Diagnostics, Sparks, MD), BBL CHROMagar MRSA (BD Diagnostics, Sparks, MD), or mannitol salt agar is helpful in differentiating S. aureus or MRSA (methicillin-resistant S. aureus ) from other bacteria [ 50 ].

Regarding LRIs, a qualitative or quantitative (or semiquantitative) culture can be performed. For the qualitative culture of common bacteria, either sputum, BAS, or BAL samples are inoculated on sheep blood agar and MacConkey’s agar (35 °C, 5% CO 2 , 24–48 h); BAL samples can also be cultured under anaerobic conditions on Brucella blood agar, laked blood with kanamycin and vancomycin, and Canada colistin–nalidixic acid [ 50 ]. For uncommon bacteria, selective media should be used: Hemophilus spp. (chocolate agar, 35 °C, 5% CO 2 , 24–48 h), Legionella spp. (buffered charcoal yeast extract with and without antimicrobial agents such as vancomycin, polymyxin B, and anisomycin; aerobic incubation, 35 °C, humidity, 5–10 days), Chlamydophila spp. (prompt transport in antibiotic, e.g., gentamycin- and nystatin-containing media for 24–48 h at 4 °C, or for longer periods at −70 °C, and inoculation in shell vials using McCoy cells for C. trachomatis and C. psittaci , and Hep-2 cells for C. pneumoniae ), Burkholderia cepacia ( B. cepacia selective agar and oxidative-fermentative-polymyxin B-bacitracin-lactose agar), M. pneumoniae (albumin- and penicillin-containing transport medium for up to 24–48 h at 4 °C, or for longer periods at −70 °C and inoculation on mycoplasma–glucose agar, methylene blue–glucose biphasic agar, or SP-4 agar for up to 3 weeks), S. aureus (mannitol salt agar), and Nocardia spp. (incubation for up to 3 weeks at 35 °C using selective BCYE agar). If the sample is suitable for anaerobic culture, specific media such as Schaedler agar and Bacteroides bile esculin agar can be used [ 50 ].

However, Chlamydophila and Mycoplasma species are quite rarely cultured in clinical microbiology laboratories for diagnostic purposes as they require weeks of growth, no easy methods are available, and they result in delayed diagnosis and increased risk of developing severe pneumonia [ 8 ]; for these reasons, molecular assays for their detection are preferred.

Quantitative cultures are needed for the diagnosis of VAP, aspiration pneumonia, and pneumonia in immunosuppressed patients or in those with cystic fibrosis. For the BAS specimen, the identification of ≥10 6 CFU in the original specimen/mL is associated with an active infection; on the contrary, lower counts represent possible cross-contamination. For BAL samples, the recovery of <10 4 bacteria/mL is most likely to represent contamination, while >10 5 bacteria/mL is indicative of an active infection. The detection of 10 4 to 10 5 bacteria/mL constitutes a “gray zone” [ 50 ].

For the detection of the main respiratory viruses (such as ADV, FLU A/B, RSV, and human PIV), the observation and identification of the cytopathic effect in cell culture is considered the gold-standard method [ 9 ]. Contextually to RTIs, cell culture is recommended for specific groups of patients, such as immunocompromised patients, children younger than 5 years who complain of respiratory symptoms, and severely ill pediatric patients [ 9 ]. Cell culture involves the inoculation of several cell lines with a clinical specimen in an attempt to provide a suitable host for whichever virus might be present on it [ 9 ]. The number and the types of cell culture wells are selected based upon the type of clinical specimen, the specimen source, and the supposed causative viral agents [ 9 ]. Viral culture wells are then incubated for days to weeks depending on the specimen source and the suspected virus(es) [ 9 ]. Cell monolayers are daily screened via microscopic examination to evaluate the potential occurrence of a viral growth [ 9 ]. The microscopic examination is performed by placing the plate on the stage of a standard light microscope and viewing the cells through the glass wall of the well with the low-power (10×) objective [ 9 ]. The finding of degenerative changes in monolayer cells provides evidence of viral presence [ 9 ]. The spectrum of morphological changes ranges from the swelling, shrinking, and rounding of cells to clustering, syncytium formation, and, in some cases, complete destruction of the monolayer. These modifications are collectively called the cytopathogenic or cytopathic effect (CPE) of the virus [ 9 ].

Even though the traditional cell culture method is advantageous for growing a wide variety of viruses, including novel or unknown viruses, and it is the only reference laboratory method able to demonstrate viral infectivity, it needs days and often weeks to provide results; thus, it affects patient management and results in poor clinical outcomes [ 9 , 39 , 42 ].

Over the years, different modified cell culture methods that reduced the turnaround time to 24 h were proposed; even though rapidly modified cell culture methods such as shell vial culture showed similar sensitivity for PIV 1-3 (87% vs. 83%) and influenza A/B (78% vs. 75%), and significantly higher sensitivity for RSV (73% vs. 42%) [ 56 ], many clinically relevant viruses are difficult to grow in culture (such as RV and Co-V) and may produce inconclusive results [ 42 ]. Moreover, the use and the maintenance of several different cell lines requires technical expertise and makes this method labor-intensive and feasible only in a few specialized centers. Therefore, as compared to molecular assays, the traditional or modified cell culture methods are laborious, exhibit higher false-negative rates, and have longer turnaround times, making viral culture less clinically relevant [ 42 , 57 , 58 ].

4.4. Antigen Detection Assays

Rapid immunoassays are relatively inexpensive, easy to perform, and can deliver test results in less than 30 min; they are commonly named Rapid Diagnostic Test (RDTs). For these reasons, they are invaluable in outpatient clinics, primary care, emergency, and low-resource settings [ 42 , 57 ]. Immunochromatographic assays are considered the most versatile and popular method among the different immunoassays [ 42 ].

Currently, for virus detection, commercially available RDTs are mostly limited to FLU A and B virus, and RSV. Despite several studies have demonstrated that RDTs showed overall poor sensitivity for FLU and RSV (44–95%), they have a higher median specificity (90 to 95%) compared to cell culture [ 42 , 57 ], and the sensitivity of RSV immunoassays is relatively higher for children (81%) than adults (29%) [ 59 , 60 ]. During the COVID-19 pandemic, several specific RDTs for the detection of SARS-CoV-2 have been developed and used as point-of-care tests, but their use is specifically limited to the search for this agent in nasal/pharyngeal swabs [ 61 ].

With regard to bacteria, such assays allow the prompt detection of the pathogen using respiratory, blood, or urine specimens (mainly for S. pyogenes , S. pneumoniae , M. pneumoniae , C. pneumoniae , and Legionella ). The reported sensitivity in detecting group A Streptococcus is 60% to 95% but can be as low as 31% for some assays. Immunochromatographic assays for the detection of the Legionella sp. antigen in urine provide a rapid result within 15 min; however, they allow the detection of serogroup 1 only. The urine detection of the polysaccharidic antigen C, present on all pneumococcal serotypes, showed high sensitivity with documented invasive pneumococcal infection; nevertheless, the capability of this method to discriminate between children with true pneumococcal diseases and carriages of rhinopharyngeal diseases is still debated [ 50 ].

Depending on their sensitivity and specificity, the use of such assays requires confirmatory assays for a conclusive diagnosis, especially when a negative result is obtained during a respiratory infections season.

4.5. Serology

The serologic measurement of specific antibody responses has limited application for the etiologic diagnosis of RTIs, because diagnostic results are only available retrospectively. Efforts have been made to diagnose infections caused by slowly growing or difficult-to-grow microorganisms using serology. This particularly holds for M. pneumoniae , C. pneumoniae , and Legionella infections and viruses. It should be remembered that the most reliable serologic evidence of an ongoing infection is based on a fourfold increase in the titer of IgG (or IgG plus IgM) antibodies during the evolution of the disease episode based on two serum samples collected with an interval of 7 to 10 days or longer, and/or the appearance of IgM antibodies during the evolution of the disease. IgM tests are usually less sensitive and specific than fourfold changes in antibody titers between paired specimens separated by several weeks [ 62 ].

Serological tests have been historically performed for the detection of “difficult to isolate” respiratory pathogens, relying either on the detection of IgM in the acute phase of the disease or the demonstration of seroconversion [ 43 ].

With regard to viral RTIs, serology allows the identification of antibodies against most of the respiratory pathogens, such as RSV, ADV, FLU A and B, and PIV 1-3 virus, and can detect mixed infections; however, the specific antibodies typically appear about 2 weeks after the initial infection [ 42 , 63 ]. On the other hand, it has been reported that serological assays are significantly less sensitive for the detection of PIV and ADV when compared to molecular methods [ 42 , 64 ]. In general, serum samples for the diagnosis for respiratory infections should be carefully considered; the results of diagnostic assays could be difficult to interpret because of the presence of an immune response to previous exposure to the same agent [ 50 ]. In addition, serology is not indicated for immunosuppressed individuals, neonates, or infants because of their impaired immune responses [ 50 ].

The serum samples should be collected at least twice during the course of the infection: in the acute phase (as soon as possible after the onset of disease and no later than 1 week) and during convalescence (at least 2 weeks after the clinical manifestation of symptoms). Comparison of the antibody patterns in these two states allows the demonstration of a diagnostically significant active virus, and seroconversion is defined when a fourfold increase in antibody titer occurs [ 42 , 50 ]. In some cases, serologic testing is considered the reference method, such as for Epstein-Barr virus in pharyngitis infection; furthermore, it is also used to check on the effectiveness of vaccinations for specific agents, if available (i.e., FLU and SARS-CoV-2) [ 50 ].

As concerns bacterial RTIs, serological testing is crucial for the identification of atypical bacterial agents such as M. pneumoniae, C. pneumoniae, Legionella spp., and B. pertussis.

In cases of a suspected M. pneumoniae -associated RTI, the enzyme immune assay (EIA) is recommended as the reference method to specifically detect IgM or IgG antibodies directed against M. pneumoniae [ 65 ].

When a M. pneumoniae -associated RTI occurs, the specific IgM appear approximately 7 days after the clinical onset, with the peak titers occurring between 4 and 6 weeks after [ 65 ]. Since IgM antibodies can persist for 2 months up to 1 year after infection in children, this serological method has been shown to be particularly useful for diagnosis in the pediatric population [ 65 ]. As concerns the C. pneumoniae -associated RTI, the gold-standard serological method is the microimmunofluorescence (MIF) test, which measures both IgG and IgM antibodies. In particular, the MIF test involves indirect immunofluorescence of the elementary bodies of C. pneumoniae , demonstrating high sensitivity if performed with expertise and with properly collected paired sera [ 66 ]. The serological diagnosis of L. pneumophila can rely on microagglutination, the immunofluorescence assay (IFA), and the enzyme-linked immunosorbent assay (ELISA). These latter two are reported to be excellent techniques in determining the seroprevalence of past and recent infection in a population [ 67 ]. The IFA is recommended as the reference method for the diagnosis of L. pneumophila -associated RTI, with 75% to 80% sensitivity and >99% specificity when the L. pneumophila serotype 1 antigen is used [ 50 ]. For the serological diagnosis of B. pertussis , the ELISA is the recommended diagnostic method, allowing the detection and the measurement of antibodies directed against the pertussis toxin [ 68 ].

However, in this case, the clinical utility of serologic tests is further limited since they require both acute and the convalescent sera to monitor seroconversion and to identify a fourfold increase in antibody titer [ 42 , 69 ]. Different tests showed a range of sensitivity from 14% to 77%, and of specificity from 49% to 97%, compared to PCR [ 42 ]. Serology should always be used in combination with confirmatory tests such as those based on direct methods of diagnosis: the isolation and/or acid nucleic detection of specific pathogenic agents.

4.6. Nucleic Acid Amplification Tests

Since the early 2000s, several nucleic acid amplification tests for the detection of respiratory pathogens have been commercially available. These tests differ in complexity (i.e., PCR, nucleic acid sequence-based amplification (NASBA), transcription-mediated amplification (TMA), strand displacement amplification (SDA), loop-mediated isothermal amplification (LAMP), rolling circle amplification (RCA), and others) and pathogen coverage; moreover, their accuracy is not only dependent on their specific assay chemistry, but is also critically affected by the type, quantity, and quality of the specimens collected [ 42 ].

PCR-based methods for virus detection have been proven to be very sensitive, usually exceeding the sensitivity scores of cell culture techniques. However, false-positive or false-negative results can be a problem if certain measures in handling for the prevention of the viral genetic material are not meticulously followed. Most respiratory viruses have an RNA genome that is particularly vulnerable to degradation by RNAses, which are present in all biologic samples. RNAse-free vials, solutions, and buffers should be used by specialized personnel in designated areas of the laboratory. In addition, if it takes too long for an NPA sample to be transported from the clinic to the laboratory, or if the sample remains on ice for too many hours instead of being frozen immediately, the sensitivity of the method can be unexpectedly low. Further, biologic fluids often contain substances that can inhibit PCR amplification (e.g., mucus). In this case, dilution of the sample or treatment with a suitable agent such as dimethyl sulfoxide may facilitate detection of the virus [ 50 ].

Species-specific PCR assays have been developed for numerous bacterial pathogens, with greater accuracy and sensitivity of identification compared to conventional culture-based diagnostics. Despite the fact that nucleic acid persists in specimens after the beginning of therapy and that it may be detected in smaller and noninvasive specimens, this approach requires a prediction to be made as to which is the most likely pathogen, as in the case of selective culture media. Moreover, due to the need for isolation of the microorganism for antibiotic susceptibility testing, cultures have been replaced by molecular methods only in cases in which the pathogens are of predictable susceptibility or the genetics of resistance are well defined, as with MRSA [ 50 ]. Assays for the detection of S. pyogenes DNA are reported to show a sensitivity of >90%, and by many authors, they are considered sensitive and specific enough to obviate confirmatory culture. Similarly, molecular assays for the detection of S. aureus DNA in nasal swabs are as sensitive as the culture but provide faster results [ 50 ].

When a prompt diagnosis is urgently required, PCR assays are considered the new gold-standard diagnostic method, as for the detection of B. pertussis in rhinopharyngeal samples, or SARS-CoV-2 in nasopharyngeal aspirates [ 70 ]. In these cases, PCR assays are significantly more sensitive and specific compared to a culture. In certain cases, such as vaccination, recent contact with an infected individual, sample collection during the paroxysmal stage of the illness, or the administration of antibiotic therapy, the culture is often negative while PCR is positive. Similarly, PCR for the detection of M. pneumoniae on rhinopharyngeal aspirates or swabs, or throat swabs, is the most sensitive and specific method, as well as for C. pneumoniae , although a positive result may indicate carriage only [ 50 ].

It is worth noting that the use of molecular methods for the detection of viral and microbial causative agents of RTIs must be considered only for specific groups of patients complaining of severe clinical respiratory syndromes, such as immunocompromised patients and the pediatric population; it is not recommended for asymptomatic patients or cases of mild infection [ 9 ].

5. Multiplex Panel Assays

Increasingly advanced molecular diagnostic technologies have the potential to transform and revolutionize microbiological diagnoses in clinical microbiology laboratories, making them faster and more robust [ 71 ]. Since 2011, after the first respiratory syndromic panel was cleared by the US Food and Drug Administration (FDA), in less than 10 years, different commercial syndromic panels with different approaches have been introduced; these have expanded the detection of agents that cause infection of the upper and lower respiratory tract (URT/LRT), blood (BL), and gastrointestinal tract (GI), as well as acute meningitis and encephalitis (ME) [ 72 ]. The ability to simultaneously detect and identify the most frequent causes of infectious diseases directly from clinical specimens is useful for patient care, hospital infection-control practices, and epidemiologic studies [ 73 ]. Respiratory panels comprise various assays that differ in their number and type of pathogens, their qualitative or semi quantitative approach, their manufacture (in-house versus commercial), and their technique (some are point-of-care diagnostic tests). They screen pathogens that infect the upper and/or lower respiratory tract and vary widely in their clinical manifestations [ 74 ]. However, for respiratory infections, there is no single generic specimen; nasopharyngeal swabs, sputum, and bronchoalveolar lavage samples are not equivalent. All of these syndromic panels have been constructed according to specimen type [ 74 ]. Moreover, the COVID-19 pandemics further highlighted their utility [ 75 ], imposing an adaptation of the tests on the new emergency.

Table 2 reports a list of the most relevant FDA-approved syndromic panels for the diagnosis of respiratory illnesses both for the URT and LRT.

The most relevant FDA-approved syndromic panels for the diagnosis of respiratory illnesses for both the upper and the lower respiratory tracts.

Many studies [ 81 , 82 , 83 , 85 , 86 , 87 , 88 , 89 ] have been conducted to evaluate the accuracy of different syndromic panels in specific samples and various patient populations; however, finding enough clinical cases to test could take a long time and more research is needed [ 79 ]. As reported, the performances in terms of sensitivity and specificity of these panels are very similar [ 79 ], and the greatest number of reported discrepancies between these multiplex panels and reference methods is for ADV and FLU B [ 79 , 80 ]. The formulation of respiratory panels (RPs) not only allows the detection of a broad range of targets, some of which are not detectable otherwise, but also to teaches us about the prevalence and clinical significance of them, such as the demonstration of RV ubiquity and of h-MPV involvement in severe disease [ 72 ]. Moreover, this can increase the number of infections that otherwise go undiagnosed because they are not suspected. A recent study demonstrated a 75% higher recovery rate of unexpected M. pneumoniae infection using multiple PCR [ 45 ]. These results highlight important considerations and limitations of syndromic testing for respiratory tract infections. Among the most important, it should be emphasized that the quantitative values, reported in addition to the qualitative values, suggest caution in interpreting the results to avoid overestimating their significance. In addition, the clinical significance of the detection of multiple agents (a coinfection rate of about 10% was reported) with multiplex panels remains unclear. Many potential clinically relevant microorganisms may be normal flora of URT, particularly if revealed in a lower abundance; as a matter of fact, LRT samples should be evaluated by performing quantitative cultures (i.e., for BAL a concentration higher than 10 4 CFU in the sample is considered significant). It was reported that for molecular panels, a cutoff of 10 3.5 genomes/mL is appropriate to consider the detected microorganism as clinically relevant [ 82 ]. In any case, analysis of the results should be performed in the context of clinical manifestations, and physicians should interpret both the multiplex PCR result and the final culture results together when establishing antimicrobial therapy plans. Furthermore, it is important to consider inconsistencies with resistance gene detection, especially in cases of co-infections or when the sample is obtained from an anatomical site with low prevalence of resistant pathogens [ 79 ]. For example, the CTX-M-type extended-spectrum beta-lactamases gene was reported for any member of the families Enterobacteriaceae , Acinetobacter spp., or P. aeruginosa , and for this reason, when a resistance phenomenon is common to different bacteria, the conventional culture and the phenotypic AST are required to confirm the indication of the resistance marker [ 82 ].

The clinical and economic impacts of multiplex respiratory testing have also been evaluated in several studies, concluding that, despite their high cost, multiplex panels offering custom orders can limit unnecessary testing, minimizing patient costs [ 79 ]. Different authors demonstrated an improvement in the clinical outcomes of patients after the introduction of RP to the diagnostic workflow caused mainly by the early administration of a targeted antibiotic therapy, and in the rapid adjustment and de-escalation of empirical therapy, also resulting in a short duration of treatment [ 45 , 72 , 75 , 85 ]. It was estimated that the multiplex panel results would have allowed for earlier antibiotic adjustment in 70.7% of patients, including de-escalation or discontinuation in 48.2%; this would have resulted in an average of 6.2 antibiotic days saved per patient [ 85 ]. In addition to the optimization of antimicrobial use, the application of these tests can reduce hospital admissions and the lengths of stays, as well as the number of chest radiographs and other investigations, as demonstrated by different authors [ 45 ]. This is especially true in the COVID-19 era when the potential use of RPs in a setting closer to the patient could be of particular impact in reducing bed moves by 1 day prior to their definitive care area, although the proposed ideal location for RP point-of-care use is the emergency department [ 85 ].

The FilmArray ® system (BioFireDiagnostics) can identify, in a semi-quantitative mode, both virus- and bacteria-associated pneumonia, as well as determining seven resistance markers (e.g., methicillin- and carbapenem-resistance genes) in 1 h. The extraction, purification of the nucleic acids from the respiratory sample, and nested multiplex PCR are performed in the same cartridge. A dedicated software program automatically analyzes the endpoint melting curve data and reports the detected pathogen [ 7 , 76 , 80 , 82 , 83 , 84 ].

The Verigene ® Respiratory Pathogens Flex Nucleic Acid Test (Nanosphere, Inc.) is performed using the Verigene System, which is a molecular diagnostics workstation consisting of two modules: the Verigene Processor SP and the Verigene Reader. Three automated steps are carried out in the Processor SP: (i) specimen extraction—magnetic bead-based RNA/DNA extraction; (ii) target amplifications; and (iii) hybridization in a microarray format. The Reader can detect, with high efficiency, the target bound in gold–silver aggregates [ 10 , 79 ].

Diagnostic tests with the QIAstat-Dx Respiratory SARS-CoV-2 Panel are performed using the QIAstat-Dx Analyzer 1.0. Samples are collected and loaded manually into the QIAstat-Dx Respiratory SARS-CoV-2 Panel Cartridge, and the extraction, amplification, and detection of nucleic acids in the samples are performed automatically by the QIAstat-Dx Analyzer 1.0. The mixture of the sample and PCR reagents is dispensed into the QIAstat-Dx Respiratory SARS-CoV-2 Panel Cartridge PCR chambers, which contain lyophilized, assay-specific primers and probes. The QIAstat-Dx Analyzer 1.0 creates the optimal temperature profiles to carry out effective multiplex real-time RT-PCR and performs real-time fluorescence measurements to generate amplification curves. The integrated software interprets the resulting data and process controls and delivers a test report [ 76 , 77 ].

The BioCode ® MDx-3000 (Applied BioCode, Inc.) is an automated system that integrates PCR amplification, target capture, signal generation and optical detection for multiple respiratory viruses and bacteria. Nucleic acids from NPS are extracted using the BioMérieux NucliSENS ® easyMAG ® or Roche MagNA Pure 96 automated systems. Once the PCR plate is set up and sealed, all other operations are automated using the MDx-3000. Amplified PCR products labeled with biotin are captured at a defined temperature by target-specific probes that are covalently coupled to designated Barcoded Magnetic Beads (BMBs). High-affinity binding between biotin and streptavidin ensures that captured PCR products with the biotin moiety are labeled with phycoerythrin in close proximity to the BMBs. Optical detection is performed for each reaction well of the capture plate, an optically clear, flat-bottom microtiter plate. Each reaction well is imaged at a specific emission wavelength for its fluorescent signal and under a bright field to identify the barcode patterns (decoding) [ 78 ].

The ePlex RP2 Panel (GenMark Diagnostics, Inc.) is an automated qualitative nucleic acid multiplex in vitro diagnostic test for the simultaneous detection and identification of multiple respiratory viral (16 targets) and bacterial (2 targets) nucleic acids. This test is performed using an ePlex instrument that automates all aspects of nucleic acid testing, including extraction, amplification, and detection, combining electrowetting and GenMark’s eSensor ® technology in a single-use cartridge. eSensor technology is based on the principles of competitive DNA hybridization and electrochemical detection, which is highly specific and is not based on fluorescent or optical detection [ 10 , 79 ].

The eSensor Respiratory Viral Panel (RVP) (Clinical MicroSensors, Inc.) is a qualitative nucleic acid multiplex test intended for use on the eSensor XT-8 system for the simultaneous detection and identification of multiple respiratory viral nucleic acids. The eSensor XT-8 consumable has a plurality of electrode locations that are coated with analyte-specific capture probe oligonucleotide for multiplex amplicon detection. The eSensor XT-8 System accepts the consumable and completes the hybridization and detection of each electrode using an assay-specific protocol [ 80 ].

The Luminex NxTAG ® Respiratory Pathogen Panel–(RPP)–CE-IVD is a qualitative nucleic acid multiplex test that provides simultaneous detection and identification of 18 viruses and 3 atypical bacteria associated with RTIs. The NxTAG Respiratory Pathogen Panel is a ready-to-use system requiring very little hands-on time and is performed in a closed PCR vessel, reducing the chances of contamination. Nucleic acid is simply added directly to pre-plated lyophilized reagents for RT-PCR and bead hybridization. The results are read on the MAGPIX ® instrument; then, the data are analyzed with the RPP assay-specific Software Accessory Package using SYNCT™ software [ 10 , 79 , 80 ].

6. Conclusions

This review focuses on the technologies used at present for the laboratory diagnosis of infectious respiratory diseases, showing that no single approach, whether it is molecular detection, antigen identification, or virus/bacteria isolation, meets the needs of all diagnostic microbiology/virology laboratories in all clinical situations involving all types of bacteria/viruses. Clinical microbiologists and virologists are challenged to use the available technology that best fits the particular situation and yields the most useful results, and should produce clinical reports that are able to guide physicians toward the right interpretation of the results for the best management of the patient.

Tomorrow, as more sophisticated, yet simpler-to-use, broad-range molecular platforms become available for clinical diagnostics, bacteria cultivation and/or virus isolation in cell culture may once again become mainly a research tool. Therefore, culture- and the non-culture-based methods should be performed in parallel to optimize the differential diagnosis of viral and microbial diseases, in order to obtain useful, cost-effective, and labor-saving microbial and/or viral testing results. In determining appropriate testing algorithms for the laboratory, laboratorians must consider a wide range of factors, including the patient population (i.e., age, immune status, and comorbidities), the clinical manifestations, the physician’s diagnosis, the changing epidemiology, and time of year (i.e., many viral infections tend to be seasonal).

Among the advantages and disadvantages, the cost of the molecular assays compared to that of conventional assays should be taken into account. Considering the cost per assay, syndromic panels are expensive at about EUR 100–200 per sample, allowing the detection of 14 to 27 agents per run, according to the assay. On the contrary, the culture-based assays, including MALDI-ToF identification and AST, cost about 30 Euros per sample, only allowing the detection of viable agents or cultivable agents (viruses are not yet cultivable and fastidious microorganisms are not included).

The current algorithms for the diagnosis of RTIs include multiple approaches, among which molecular methods and conventional culture are the most used for laboratory diagnosis of such infectious diseases. Molecular methods are the most used for the detection of viral agents and many atypical bacteria, and their use should be routinely applied in clinical laboratories to samples from patients in the emergency department. The conventional culture remains the gold-standard for the detection of bacteria but suffers from several shortcomings. In particular, culture-based methods show lower sensitivity than molecular methods, particularly with regard to the detection of “difficult-to-grow” microbes, thus underestimating viable microorganisms in the sample to be tested. Moreover, a conventional culture is time-consuming since it requires an average of 48 to 72 h for time-to-results.

Specimen-processing guidelines vary from laboratory to laboratory, resulting in the lack of a common line in the interpretation of growth bacterial patterns, with different modes of reporting the results. On the other hand, the gold-standard cell culture for the viral diagnosis of RTIs also shows several disadvantages: the need for technical expertise in evaluating the cell culture monolayers, the long incubation period required for some viruses to produce CPE, the inability of some viruses to proliferate in traditional cell cultures, and the expense involved in purchasing and maintaining cell cultures are all factors to consider when evaluating such as diagnostic workflow.

The implementation of syndromic panels in the respiratory infection diagnostic algorithm has the potential to be a powerful decision-making tool for patient management, especially in emergency departments, despite requiring the appropriate use of the test in different patient populations. It is mandatory that their use is limited to symptomatic subjects, immunocompromised patients, children less than 5 years old, and the elderly, and that their use is avoided in asymptomatic subjects or mild infections.

In conclusion, the use of syndromic panels for the detection of respiratory pathogens is associated with a radically reduced time-to-results and, in parallel, to increased detection of clinically relevant pathogens compared to the standard methods. Syndromic panels, if implemented wisely and interpreted cautiously, can improve antimicrobial use and patient outcomes through improved clinical decision, optimized laboratory workflow, and enhanced antimicrobial and laboratory stewardship. As the implementation of new syndromic diagnostic platforms in clinical diagnosis continues to grow, it will be essential to share experiences regarding implementation and optimization strategies. Further research is therefore needed to understand the relationship between the number of viruses/bacteria and its clinical relevance in different patient populations, as well as the true clinical significance of the simultaneous finding of multiple pathogens.

Funding Statement

This study was supported by the Ministry of University and Scientific Research Grant FIL, University of Parma, Parma, Italy, and the grant “Fondo di finanziamento per le attività base di ricerca (FFABR)” from the Italian Ministry of University and Research (Ministero dell’Università e della Ricerca, MUR).

Author Contributions

Conceptualization, A.C.; methodology, A.C.; data curation and writing—original draft preparation, A.C., M.B., B.F. and S.M.; writing—review and editing, A.C., M.B., B.F., S.M., F.D.C. and C.C.; funding acquisition, A.C. All authors have read and agreed to the published version of the manuscript.

Institutional Review Board Statement

Not applicable.

Informed Consent Statement

Data availability statement, conflicts of interest.

The authors declare no conflict of interest.

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