Changes in the proteomics of exhaled breath condensate under the influence of inhaled hydrogen in patients with post-COVID syndrome.

Aim. To study the effect of inhalation therapy with an active hydrogen (AH) on the protein composition of exhaled breath condensate (EBC) in patients with post-COVID syndrome (PCS). Material and methods. This randomized controlled parallel prospective study included 60 patients after coronavirus disease 2019 (COVID-19) with PCS during the recovery period and clinical manifestations of chronic fatigue syndrome who received standard therapy according to the protocol for managing patients with chronic fatigue syndrome (CFS). The patients were divided into 2 groups: group 1 (main) - 30 people who received standard therapy and AH inhalations (SUISONIA, Japan) for 10 days, and group 2 (control) - 30 medical workers who received only standard therapy. Patients in both groups were comparable in sex and mean age. All participants in the study were sampled with EBC on days 1 and 10. Samples were subjected to tryptic digestion and high-performance liquid chromatography combined with tandem mass spectrometry analysis using a nanoflow chromatograph (Dionex 3000) in tandem with a high-resolution time-of-flight mass spectrometer (timsTOF Pro). Results. A total of 478 proteins and 1350 peptides were identified using high resolution mass spectrometry. The number of proteins in samples after AH therapy, on average, is 12% more than before treatment. An analysis of the distribution of proteins in different groups of patients showed that only half of these proteins (112) are common for all groups of samples and are detected in EBC before, after, and regardless of hydrogen therapy. In addition to the qualitative difference in the EBC protein compositions in different groups, quantitative changes in the concentration of 36 proteins (mainly structural and protective) were also revealed, which together made it possible to reliably distinguish between subgroups before and after treatment. It is worth noting that among these proteins there are participants of blood coagulation (alpha-1-antitrypsin), chemokine- and cytokine-mediated inflammation, and a number of signaling pathways (cytoplasmic actin 2), response to oxidative stress (thioredoxin), glycolysis (glyceraldehyde-3- phosphate dehydrogenase), etc. Conclusion. The use of hydrogen therapy can contribute to the switching of a number of physiological processes, which may affect the success of recovery in PCS patients. In particular, the obtained results indicate the activation of aerobic synthesis of adenosine triphosphate in mitochondria by hydrogen therapy, which correlates well with the decrease in the blood lactate level detected by laboratory studies. At the same time, this therapy can inhibit pro-inflammatory activity, negatively affecting the coagulation and signaling pathways of integrins and apoptosis, and, in addition, activate protective pathways, tricarboxylic acid cycle, FAS signaling, and purine metabolism, which may be essential for effective recovery after COVID-19.Copyright © 2023 Vserossiiskoe Obshchestvo Kardiologov. All rights reserved.

Quantitatively Visualizing Airborne Disease Transmission Risks of Different Exhalation Activities through CO2 Imaging.

Aerosol transmission has played a leading role in COVID-19 pandemic. However, there is still a poor understanding about how it is transmitted. This work was designed to study the exhaled breath flow dynamics and transmission risks under different exhaling modes. Using an infrared photography device, exhaled flow characteristics of different breathing activities, such as deep breathing, dry coughing, and laughing, together with the roles of mouth and nose were characterized by imaging CO2 flow morphologies. Both mouth and nose played an important role in the disease transmission though in the downward direction for the nose. In contrast to the trajectory commonly modeled, the exhaled airflows appeared with turbulent entrainments and obvious irregular movements, particularly the exhalations involving mouth were directed horizontal and had a higher propagation capacity and transmission risk. While the cumulative risk was high for deep breathing, those transient ones from dry coughing, yawning, and laughing were also shown to be significant. Various protective measures including masks, canteen table shields, and wearable devices were visually demonstrated to be effective for altering the exhaled flow directions. This work is useful to understanding the risk of aerosol infection and guiding the formulation of its prevention and control strategies. Experimental data also provide important information for refining model boundary conditions.

Evaluation of lung inflammation using Exhaled breath condensate in patients with post-COVID syndrome

Introduction: The term "post-COVID syndrome" encompasses a wide range of clinical conditions following SARSCOV2 infection. Whether post-COVID syndrome can be associated with a prolonged inflammatory and immune response is still unknown. Exhaled Breath Condensate (EBC) pH has been recognized as a robust marker of lung inflammation in various diseases (Kharitonov et al. Chest 2006;130(5):1541-46). However, evidences on the role of EBC pH in diagnosing lung inflammation in post-COVID syndrome are still lacking. Aims and objectives: We aimed to investigate EBC pH in patients suffering from post-COVID syndrome. Method(s): We enrolled 10 patients hospitalized with acute respiratory failure and COVID-19 pneumonia. We performed a complete follow up after 3 months (T1) and 6 months (T2) from discharge. Each visit included routine blood tests, arterial blood gas analysis, 6 minute walking test and body plethysmography. Finally, bronchial and alveolar EBC pH were collected at the end of each visit. Result(s): Alveolar EBC pH was significantly lower at T1 compared with T2 samples (p= 0.0007). Moreover, in T1 analysis, we found a less acid pH in bronchial EBC compared to the alveolar one (p=0.003). Alveolar and bronchial EBC did not differ at T2, as well as bronchial EBC from T1 to T2. Serum inflammatory biomarkers did not differ from T1 to T2 analysis. Finally, alveolar EBC was directly correlated with Neutrophil-Lymphocyte ratio (R=0.71, p=0.02). Conclusion(s): Alveolar EBC pH is a useful non-invasive tool to characterize and monitor lung inflammation in patients with post-COVID syndrome. Furthermore, no other serum biomarker seems to be sensitive enough to identify residual phlogosis after COVID-19 disease.

SARS-CoV-2 load in exhaled end-tidal breath fine aerosol condensates among acutely symptomatic COVID-19 cases

SARS-CoV-2 infectious virions have been reported in exhaled breath, but their source remains elusive: breath sampling systems used to date do not separate breath aerosols by size, fail to prevent salivary/fomite contamination, or aerosol size evolution before sample capture. We hypothesised that sampling end-tidal, oral exhaled breath condensate (EBC), after separating large droplets by inertial impaction 4cm from the lips, would quantify viral loads in distal lung-derived fine aerosols (FA). We used a collector (PBM-HALE ) that captures mechanically aerosolised viruses to sample adult participants for <30 min under informed consent;cases symptomatic for <5 days (n=30) or >5 days (n=12), positive by nasopharyngeal swab RT-PCR (Ct>=13.1), were sampled in clinical triage 'red zones', or COVID-19 wards with no mechanical ventilation or open windows. Salivary alpha amylase activity (Salimetrics LLC), or SARS-CoV-2 viral load (VIASURE SARS-CoV-2 (ORF1ab and N gene)) after QIAsymhpony DSP midi extraction, was quantified in 0.2mL FA EBC fractions. No salivary alpha amylase activity was detected in healthy participant FA EBC (>1:1,750 dilution of paired saliva vs assay detection limit (n=300)). No SARS-CoV-2 RNA was detected in FA EBC (1.18mL +/- 0.32 total volume) among any COVID-19 cases (Aug 2020-Jan 2022) at limits of detection of 120 genomes/mL FA EBC or 4.72 genomes/min exhalation. No pre-extraction spike-in control reaction inhibition was observed. No ambient contamination of the alveolar FA EBC was detected with this sampling device. The alveolar fraction of orally exhaled tidal breath lacks detectable SARS-CoV-2 viral load.

The IOMT-Based Risk-Free Approach to Lung Disorders Detection from Exhaled Breath Examination

The lungs are the main fundamental part of the human respiratory system and are among the major organs of the human body. Lung disorders, including Coronavirus (Covid-19), are among the world's deadliest and most life-threatening diseases. Early and social distance-based detection and treatment can save lives as well as protect the rest of humanity. Even though X-rays or Computed Tomography (CT) scans are the imaging techniques to analyze lung-related disorders, medical practitioners still find it challenging to analyze and identify lung cancer from scanned images. unless COVID-19 reaches the lungs, it is unable to be diagnosed. through these modalities. So, the Internet of Medical Things (IoMT) and machine learning-based computer-assisted approaches have been developed and applied to automate these diagnostic procedures. This study also aims at investigating an automated approach for the detection of COVID-19 and lung disorders other than COVID-19 infection in a non-invasive manner at their early stages through the analysis of human breath. Human breath contains several volatile organic compounds, i.e., water vapor (5.0%–6.3%), nitrogen (79%), oxygen (13.6%–16.0%), carbon dioxide (4.0%–5.3%), argon (1%), hydrogen (1 ppm) (parts per million), carbon monoxide (1%), proteins (1%), isoprene (1%), acetone (1%), and ammonia (1%). Beyond these limits, the presence of a certain volatile organic compound (VOC) may indicate a disease. The proposed research not only aims to increase the accuracy of lung disorder detection from breath analysis but also to deploy the model in a real-time environment as a home appliance. Different sensors detect VOC;microcontrollers and machine learning models have been used to detect these lung disorders. Overall, the suggested methodology is accurate, efficient, and non-invasive. The proposed method obtained an accuracy of 93.59%, a sensitivity of 89.59%, a specificity of 94.87%, and an AUC-Value of 0.96. © 2023, Tech Science Press. All rights reserved.

Proteomic characteristics and diagnostic potential of exhaled breath particles in patients with COVID-19.

BACKGROUND: SARS-CoV-2 has been shown to predominantly infect the airways and the respiratory tract and too often have an unpredictable and different pathologic pattern compared to other respiratory diseases. Current clinical diagnostical tools in pulmonary medicine expose patients to harmful radiation, are too unspecific or even invasive. Proteomic analysis of exhaled breath particles (EBPs) in contrast, are non-invasive, sample directly from the pathological source and presents as a novel explorative and diagnostical tool. METHODS: Patients with PCR-verified COVID-19 infection (COV-POS, n = 20), and patients with respiratory symptoms but with > 2 negative polymerase chain reaction (PCR) tests (COV-NEG, n = 16) and healthy controls (HCO, n = 12) were prospectively recruited. EBPs were collected using a "particles in exhaled air" (PExA 2.0) device. Particle per exhaled volume (PEV) and size distribution profiles were compared. Proteins were analyzed using liquid chromatography-mass spectrometry. A random forest machine learning classification model was then trained and validated on EBP data achieving an accuracy of 0.92. RESULTS: Significant increases in PEV and changes in size distribution profiles of EBPs was seen in COV-POS and COV-NEG compared to healthy controls. We achieved a deep proteome profiling of EBP across the three groups with proteins involved in immune activation, acute phase response, cell adhesion, blood coagulation, and known components of the respiratory tract lining fluid, among others. We demonstrated promising results for the use of an integrated EBP biomarker panel together with particle concentration for diagnosis of COVID-19 as well as a robust method for protein identification in EBPs. CONCLUSION: Our results demonstrate the promising potential for the use of EBP fingerprints in biomarker discovery and for diagnosing pulmonary diseases, rapidly and non-invasively with minimal patient discomfort.

Prediction of Individual Dynamic Thermal Sensation in Subway Commute Using Smart Face Mask.

Wearable sensors and machine learning algorithms are widely used for predicting an individual's thermal sensation. However, most of the studies are limited to controlled laboratory experiments with inconvenient wearable sensors without considering the dynamic behavior of ambient conditions. In this study, we focused on predicting individual dynamic thermal sensation based on physiological and psychological data. We designed a smart face mask that can measure skin temperature (SKT) and exhaled breath temperature (EBT) and is powered by a rechargeable battery. Real-time human experiments were performed in a subway cabin with twenty male students under natural conditions. The data were collected using a smartphone application, and we created features using the wavelet decomposition technique. The bagged tree algorithm was selected to train the individual model, which showed an overall accuracy and f-1 score of 98.14% and 96.33%, respectively. An individual's thermal sensation was significantly correlated with SKT, EBT, and associated features.

Exhaled Breath Aerosol Shedding by Highly Transmissible Versus Prior SARS-CoV-2 Variants.

BACKGROUND: Aerosol inhalation is recognized as the dominant mode of SARS-CoV-2 transmission. Three highly transmissible lineages evolved during the pandemic. One hypothesis to explain increased transmissibility is that natural selection favors variants with higher rates of viral aerosol shedding. However, the extent of aerosol shedding of successive SARS-CoV-2 variants is unknown. We aimed to measure the infectivity and rate of SARS-CoV-2 shedding into exhaled breath aerosol (EBA) by individuals during the Delta and Omicron waves and compared those rates with those of prior SARS-CoV-2 variants from our previously published work. METHODS: COVID-19 cases (n = 93, 32 vaccinated and 20 boosted) were recruited to give samples, including 30-minute breath samples into a Gesundheit-II exhaled breath aerosol sampler. Samples were quantified for viral RNA using RT-PCR and cultured for virus. RESULTS: Alpha (n = 4), Delta (n = 3), and Omicron (n = 29) cases shed significantly more viral RNA copies into exhaled breath aerosols than cases infected with ancestral strains and variants not associated with increased transmissibility (n = 57). All Delta and Omicron cases were fully vaccinated and most Omicron cases were boosted. We cultured virus from the EBA of one boosted and three fully vaccinated cases. CONCLUSIONS: Alpha, Delta, and Omicron independently evolved high viral aerosol shedding phenotypes, demonstrating convergent evolution. Vaccinated and boosted cases can shed infectious SARS-CoV-2 via EBA. These findings support a dominant role of infectious aerosols in transmission of SARS-CoV-2. Monitoring aerosol shedding from new variants and emerging pathogens can be an important component of future threat assessments and guide interventions to prevent transmission.

Mask device as a new wearable sampler for breath analysis: what can we expect in the future?

Human exhaled breath is becoming an attractive clinical source as it is foreseen to enable noninvasive diagnosis of many diseases. Because mask devices can be used for efficiently filtering exhaled substances, mask-wearing has been required in the past few years in daily life since the unprecedented COVID-19 pandemic. In recent years, there is a new development of mask devices as new wearable breath samplers for collecting exhaled substances for disease diagnosis and biomarker discovery. This paper attempts to identify new trends in mask samplers for breath analysis. The couplings of mask samplers with different (bio)analytical approaches, including mass spectrometry (MS), polymerase chain reaction (PCR), sensor, and others for breath analysis, are summarized. The developments and applications of mask samplers in disease diagnosis and human health are reviewed. The limitations and future trends of mask samplers are also discussed.

Exhaled breath condensate as bioanalyte: from collection considerations to biomarker sensing.

Since the SARS-CoV-2 pandemic, the potential of exhaled breath (EB) to provide valuable information and insight into the health status of a person has been revisited. Mass spectrometry (MS) has gained increasing attention as a powerful analytical tool for clinical diagnostics of exhaled breath aerosols (EBA) and exhaled breath condensates (EBC) due to its high sensitivity and specificity. Although MS will continue to play an important role in biomarker discovery in EB, its use in clinical setting is rather limited. EB analysis is moving toward online sampling with portable, room temperature operable, and inexpensive point-of-care devices capable of real-time measurements. This transition is happening due to the availability of highly performing biosensors and the use of wearable EB collection tools, mostly in the form of face masks. This feature article will outline the last developments in the field, notably the novel ways of EBA and EBC collection and the analytical aspects of the collected samples. The inherit non-invasive character of the sample collection approach might open new doors for efficient ways for a fast, non-invasive, and better diagnosis.

Determination of global chemical patterns in exhaled breath for the discrimination of lung damage in postCOVID patients using olfactory technology.

The objective of this work was to evaluate the use of an electronic nose and chemometric analysis to discriminate global patterns of volatile organic compounds (VOCs) in breath of postCOVID syndrome patients with pulmonary sequelae. A cross-sectional study was performed in two groups, the group 1 were subjects recovered from COVID-19 without lung damage and the group 2 were subjects recovered from COVID-19 with impaired lung function. The VOCs analysis was executed using a Cyranose 320 electronic nose with 32 sensors, applying principal component analysis (PCA), Partial Least Square-Discriminant Analysis, random forest, canonical discriminant analysis (CAP) and the diagnostic power of the test was evaluated using the ROC (Receiver Operating Characteristic) curve. A total of 228 participants were obtained, for the postCOVID group there are 157 and 71 for the control group, the chemometric analysis results indicate in the PCA an 84% explanation of the variability between the groups, the PLS-DA indicates an observable separation between the groups and 10 sensors related to this separation, by random forest, a classification error was obtained for the control group of 0.090 and for the postCOVID group of 0.088 correct classification. The CAP model showed 83.8% of correct classification and the external validation of the model showed 80.1% of correct classification. Sensitivity and specificity reached 88.9% (73.9%-96.9%) and 96.9% (83.7%-99.9%) respectively. It is considered that this technology can be used to establish the starting point in the evaluation of lung damage in postCOVID patients with pulmonary sequelae.

Metabolic Changes of VOCs in Human Caused by Wearing Masks: A Preliminary Study Based on Electronic Nose

Wearing masks has been generally recommended to reduce the spreading of COVID-19. However, little is known about its effects on metabolic VOC changes in human body. To explore how the duration of wearing masks influences VOC metabolism in the human body, the essay used a self-developed electronic nose to analyse exhaled breath samples from 10 healthy individuals in this study. Firstly, polytetrafluoroethylene sampling bags are used to collect breath samples after volunteers wearing masks for 1h, 2h, 3h, 4h, and 5h. Secondly, data pre-processing, including baseline calibration and normalization are carried out. Thirdly, the study used LDA for dimensionality reduction on the original data to extract 4 features. Fourthly, differences in the length of time of wearing masks are analysed. Then, 4 algorithms were applied for cluster analysis based on extracted features. Moreover, 3 supervised classification algorithms were used to recognize the duration of wearing masks. Finally, multi-dimensional linear regression is used to study the possibility of predicting the duration of wearing masks based on breath signals acquired through electronic noses. As a result, the first feature extracted by LDA significantly differs from each other in the duration of wearing masks (p<0.05). Cluster analysis results show that the optimal internal parameters Adjusted Rand Index, Adjusted Mutual Information, Homogeneity and V-measure reach 80.2%, 81.5%, 83.5% and 83.7% respectively. Using 5-fold cross-validation on the K nearest neighbour classification model, the best accuracy of recognizing durations of wearing a mask reaches 88%. R-square of multi-dimensional linear regression reaches 92.5%, which shows excellent fitting performance. It can be concluded that the VOC metabolism of the human may change with the duration of wearing masks. Further, "breath prints” obtained by electronic nose may have the potential to predict the effective time and even the quality of masks. © 2022 SPIE.

Accuracy of Volatile Organic Compound (VOC) Detection in Exhaled Breath Compared to Reverse-transcriptase Polymerase Chain Reaction (RT-PCR) for Diagnosis of COVID-19: An Evidence-based Case Report

Background: Coronavirus disease 2019 (COVID-19) is a contagious infectious disease caused by severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2). The World Health Organization (WHO) declared this infection a global pandemic in 2020. In addition, various methods have been developed to diagnose COVID-19 rapidly and accurately to reverse transcription-polymerase chain re-action (RT-PCR) as a gold standard method. One of these methods is the detection of volatile organic compounds (VOC) in exhaled breath. Objectives: The aim was to collect and investigate studies on the accuracy of VOC detection as a diagnostic method for COVID-19. Methods: A literature search was performed in five electronic databases, including PubMed, Cochrane Library, ProQuest, EBSCO-host, and Scopus, along with hand searching. The search was conducted in the titles and s of articles using keywords and their equivalent terms, combined with the Boolean operators (OR and AND). The search results were then selected according to the inclusion and exclusion criteria and compatibility with the Population, Intervention, Control, and Outcomes (PICO) framework. Results: Based on the search results, two cross-sectional studies by Wintjens et al. and Ruszkiewicz et al. were selected, which were then critically appraised. Both studies showed good validity. Wintjens et al. reported 86% sensitivity and 54% specificity for their method, with a positive predictive value (PPV) and a negative predictive value (NPV) of 40% and 92%, respectively. Besides, Ruszkiewicz et al., who conducted a study in two different locations, reported 82.4% sensitivity and 75% specificity for their method in Edinburgh (UK), with PPV and NPV of 87.5% and 66.7%, respectively, while they reported 90% sensitivity and 80% specificity in Dortmund (Germany), with PPV and NPV of 45% and 97.8%, respectively. The accuracy of these three methods was 62%, 80%, and 82%, respectively. Conclusions: Detection of VOCs from exhaled breath can be a rapid, cost-effective, and simple method for diagnosing COVID-19. However, the accuracy of this method is still relatively low (62 -82%) and inconsistent;therefore, it is only recommended for screen-ing.

Aerosol transmission of human pathogens: From miasmata to modern viral pandemics and their preservation potential in the Anthropocene record

Ongoing uncertainty over the relative importance of aerosol transmission of COVID-19 is in part rooted in the history of medical science and our understanding of how epidemic diseases can spread through human populations. Ancient Greek medical theory held that such illnesses are transmitted by airborne pathogenic emanations containing particulate matter ("miasmata"). Notable Roman and medieval schol-ars such as Varro, Ibn al-Khatib and Fracastoro developed these ideas, combining them with early germ theory and the concept of contagion. A widely held but vaguely defined belief in toxic miasmatic mists as a dominant causative agent in disease propagation was overtaken by the science of 19th century micro-biology and epidemiology, especially in the study of cholera, which was proven to be mainly transmitted by contaminated water. Airborne disease transmission came to be viewed as burdened by a dubious his-torical reputation and difficult to demonstrate convincingly. A breakthrough came with the classic mid -20th century work of Wells, Riley and Mills who proved how expiratory aerosols (their "droplet nuclei") could transport still-infectious tuberculosis bacteria through ventilation systems. The topic of aerosol transmission of pathogenic respiratory diseases assumed a new dimension with the mid-late 20th cen-tury "Great Acceleration" of an increasingly hypermobile human population repeatedly infected by dif-ferent strains of zoonotic viruses, and has taken centre stage this century in response to outbreaks of new respiratory infections that include coronaviruses. From a geoscience perspective, the consequences of pandemic-status diseases such as COVID-19, produced by viral pathogens utilising aerosols to infect a human population currently approaching 8 billion, are far-reaching and unprecedented. The obvious and sudden impacts on for example waste plastic production, water and air quality and atmospheric chem-istry are accelerating human awareness of current environmental challenges. As such, the "anthropause" lockdown enforced by COVID-19 may come to be seen as a harbinger of change great enough to be pre-served in the Anthropocene stratal record.(c) 2021 China University of Geosciences (Beijing) and Peking University. Production and hosting by Elsevier B.V. This is an open access article under the CC BY-NC-ND license (http://creativecommons.org/ licenses/by-nc-nd/4.0/).

Electrochemical Biosensing and Deep Learning-Based Approaches in the Diagnosis of COVID-19: A Review

COVID-19 caused by the transmission of SARS-CoV-2 virus taking a huge toll on global health and caused life-threatening medical complications and elevated mortality rates, especially among older adults and people with existing morbidity. Current evidence suggests that the virus spreads primarily through respiratory droplets emitted by infected persons when breathing, coughing, sneezing, or speaking. These droplets can reach another person through their mouth, nose, or eyes, resulting in infection. The "gold standard" for clinical diagnosis of SARS-CoV-2 is the laboratory-based nucleic acid amplification test, which includes the reverse transcription-polymerase chain reaction (RT-PCR) test on nasopharyngeal swab samples. The main concerns with this type of test are the relatively high cost, long processing time, and considerable false-positive or false-negative results. Alternative approaches have been suggested to detect the SARS-CoV-2 virus so that those infected and the people they have been in contact with can be quickly isolated to break the transmission chains and hopefully, control the pandemic. These alternative approaches include electrochemical biosensing and deep learning. In this review, we discuss the current state-of-the-art technology used in both fields for public health surveillance of SARS-CoV-2 and present a comparison of both methods in terms of cost, sampling, timing, accuracy, instrument complexity, global accessibility, feasibility, and adaptability to mutations. Finally, we discuss the issues and potential future research approaches for detecting the SARS-CoV-2 virus utilizing electrochemical biosensing and deep learning.

Potential Use of Exhaled Breath Condensate for Diagnosis of SARS-CoV-2 Infections: A Systematic Review and Meta-Analysis.

BACKGROUND: Reverse-transcriptase polymerase chain reaction (RT-qPCR) assays performed on respiratory samples collected through nasal swabs still represent the gold standard for COVID-19 diagnosis. Alternative methods to this invasive and time-consuming options are still being inquired, including the collection of airways lining fluids through exhaled breath condensate (EBC). MATERIALS AND METHODS: We performed a systematic review and meta-analysis in order to explore the reliability of EBC as a way to collect respiratory specimens for RT-qPCR for diagnosis of COVID-19. RESULTS: A total of 4 studies (205 specimens), were ultimately collected, with a pooled sensitivity of 69.5% (95%CI 26.8-93.4), and a pooled specificity of 98.3% (95%CI 87.8-99.8), associated with high heterogeneity and scarce diagnostic agreement with the gold standard represented by nasal swabs (Cohen's kappa = 0.585). DISCUSSION: Even though non-invasive options for diagnosis of COVID-19 are still necessary, EBC-based RT-qPCR showed scarce diagnostic performances, ultimately impairing its implementation in real-world settings. However, as few studies have been carried out to date, and the studies included in the present review are characterized by low numbers and low sample power, further research are requested to fully characterize the actual reliability of EBC-based RT-qPCR in the diagnosis of COVID-19.

Infectious Severe Acute Respiratory Syndrome Coronavirus 2 (SARS-CoV-2) in Exhaled Aerosols and Efficacy of Masks During Early Mild Infection.

BACKGROUND: Severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) epidemiology implicates airborne transmission; aerosol infectiousness and impacts of masks and variants on aerosol shedding are not well understood. METHODS: We recruited coronavirus disease 2019 (COVID-19) cases to give blood, saliva, mid-turbinate and fomite (phone) swabs, and 30-minute breath samples while vocalizing into a Gesundheit-II, with and without masks at up to 2 visits 2 days apart. We quantified and sequenced viral RNA, cultured virus, and assayed serum samples for anti-spike and anti-receptor binding domain antibodies. RESULTS: We enrolled 49 seronegative cases (mean days post onset 3.8 ±â€…2.1), May 2020 through April 2021. We detected SARS-CoV-2 RNA in 36% of fine (≤5 µm), 26% of coarse (>5 µm) aerosols, and 52% of fomite samples overall and in all samples from 4 alpha variant cases. Masks reduced viral RNA by 48% (95% confidence interval [CI], 3 to 72%) in fine and by 77% (95% CI, 51 to 89%) in coarse aerosols; cloth and surgical masks were not significantly different. The alpha variant was associated with a 43-fold (95% CI, 6.6- to 280-fold) increase in fine aerosol viral RNA, compared with earlier viruses, that remained a significant 18-fold (95% CI, 3.4- to 92-fold) increase adjusting for viral RNA in saliva, swabs, and other potential confounders. Two fine aerosol samples, collected while participants wore masks, were culture-positive. CONCLUSIONS: SARS-CoV-2 is evolving toward more efficient aerosol generation and loose-fitting masks provide significant but only modest source control. Therefore, until vaccination rates are very high, continued layered controls and tight-fitting masks and respirators will be necessary.

Exhaled breath SARS-CoV-2 shedding patterns across variants of concern.

OBJECTIVES: We performed exhaled breath (EB) and nasopharyngeal (NP) quantitative polymerase chain reaction (qPCR) and NP rapid antigen testing (NP RAT) of SARS-CoV-2 infections with different variants. METHODS: We included immuno-naïve alpha-infected (n = 11) and partly boosted omicron-infected patients (n = 8) as high-risk contacts. We compared peak NP and EB qPCR cycle time (ct) values between cohorts (Wilcoxon-Mann-Whitney test). Test positivity was compared for three infection phases using Cochran Q test. RESULTS: Peak median NP ct was 11.5 (interquartile range [IQR] 10.1-12.1) for alpha and 12.2 (IQR 11.1-15.3) for omicron infections. Peak median EB ct was 25.2 (IQR 24.5-26.9) and 28.3 (IQR 26.4-30.8) for alpha and omicron infections, respectively. Distributions did not differ between cohorts for NP (P = 0.19) or EB (P = 0.09). SARS-CoV-2 shedding peaked on day 1 in EB (confidence interval [CI] 0.0 - 4.5) and day 3 in NP (CI 1.5 - 6.0). EB qPCR positivity equaled NP qPCR positivity on D0-D1 (P = 0.44) and D2-D6 (P = 1.0). It superseded NP RAT positivity on D0-D1 (P = 0.003) and D2-D6 (P = 0.008). It was inferior to both on D7-D10 (P < 0.001). CONCLUSION: Peak EB and nasopharynx shedding were comparable across variants. EB qPCR positivity matched NP qPCR and superseded NP RAT in the first week of infection.