Comparing variability in diagnosis of upper respiratory tract infections in patients using syndromic, next generation sequencing, and PCR-based methods

Early and accurate diagnosis of respiratory pathogens and associated outbreaks can allow for the control of spread, epidemiological modeling, targeted treatment, and decision making–as is evident with the current COVID-19 pandemic. Many respiratory infections share common symptoms, making them difficult to diagnose using only syndromic presentation. Yet, with delays in getting reference laboratory tests and limited availability and poor sensitivity of point-of-care tests, syndromic diagnosis is the most-relied upon method in clinical practice today. Here, we examine the variability in diagnostic identification of respiratory infections during the annual infection cycle in northern New Mexico, by comparing syndromic diagnostics with polymerase chain reaction (PCR) and sequencing-based methods, with the goal of assessing gaps in our current ability to identify respiratory pathogens. Of 97 individuals that presented with symptoms of respiratory infection, only 23 were positive for at least one RNA virus, as confirmed by sequencing. Whereas influenza virus (n = 7) was expected during this infection cycle, we also observed coronavirus (n = 7), respiratory syncytial virus (n = 8), parainfluenza virus (n = 4), and human metapneumovirus (n = 1) in individuals with respiratory infection symptoms. Four patients were coinfected with two viruses. In 21 individuals that tested positive using PCR, RNA sequencing completely matched in only 12 (57%) of these individuals. Few individuals (37.1%) were diagnosed to have an upper respiratory tract infection or viral syndrome by syndromic diagnostics, and the type of virus could only be distinguished in one patient. Thus, current syndromic diagnostic approaches fail to accurately identify respiratory pathogens associated with infection and are not suited to capture emerging threats in an accurate fashion. We conclude there is a critical and urgent need for layered agnostic diagnostics to track known and unknown pathogens at the point of care to control future outbreaks.

Public Attitudes Toward Anxiety Disorder on Sina Weibo: Content Analysis.

BACKGROUND: Anxiety disorder has become a major clinical and public health problem, causing a significant economic burden worldwide. Public attitudes toward anxiety can impact the psychological state, help-seeking behavior, and social activities of people with anxiety disorder. OBJECTIVE: The purpose of this study was to explore public attitudes toward anxiety disorders and the changing trends of these attitudes by analyzing the posts related to anxiety disorders on Sina Weibo, a Chinese social media platform that has about 582 million users, as well as the psycholinguistic and topical features in the text content of the posts. METHODS: From April 2018 to March 2022, 325,807 Sina Weibo posts with the keyword "anxiety disorder" were collected and analyzed. First, we analyzed the changing trends in the number and total length of posts every month. Second, a Chinese Linguistic Psychological Text Analysis System (TextMind) was used to analyze the changing trends in the language features of the posts, in which 20 linguistic features were selected and presented. Third, a topic model (biterm topic model) was used for semantic content analysis to identify specific themes in Weibo users' attitudes toward anxiety. RESULTS: The changing trends in the number and the total length of posts indicated that anxiety-related posts significantly increased from April 2018 to March 2022 (R2=0.6512; P<.001 to R2=0.8133; P<.001, respectively) and were greatly impacted by the beginning of a new semester (spring/fall). The analysis of linguistic features showed that the frequency of the cognitive process (R2=0.1782; P=.003), perceptual process (R2=0.1435; P=.008), biological process (R2=0.3225; P<.001), and assent words (R2=0.4412; P<.001) increased significantly over time, while the frequency of the social process words (R2=0.2889; P<.001) decreased significantly, and public anxiety was greatly impacted by the COVID-19 pandemic. Feature correlation analysis showed that the frequencies of words related to work and family are almost negatively correlated with those of other psychological words. Semantic content analysis identified 5 common topical areas: discrimination and stigma, symptoms and physical health, treatment and support, work and social, and family and life. Our results showed that the occurrence probability of the topical area "discrimination and stigma" reached the highest value and averagely accounted for 26.66% in the 4-year period. The occurrence probability of the topical area "family and life" (R2=0.1888; P=.09) decreased over time, while that of the other 4 topical areas increased. CONCLUSIONS: The findings of our study indicate that public discrimination and stigma against anxiety disorder remain high, particularly in the aspects of self-denial and negative emotions. People with anxiety disorders should receive more social support to reduce the impact of discrimination and stigma.

Ensemble learning with speaker embeddings in multiple speech task stimuli for depression detection.

Introduction: As a biomarker of depression, speech signal has attracted the interest of many researchers due to its characteristics of easy collection and non-invasive. However, subjects' speech variation under different scenes and emotional stimuli, the insufficient amount of depression speech data for deep learning, and the variable length of speech frame-level features have an impact on the recognition performance. Methods: The above problems, this study proposes a multi-task ensemble learning method based on speaker embeddings for depression classification. First, we extract the Mel Frequency Cepstral Coefficients (MFCC), the Perceptual Linear Predictive Coefficients (PLP), and the Filter Bank (FBANK) from the out-domain dataset (CN-Celeb) and train the Resnet x-vector extractor, Time delay neural network (TDNN) x-vector extractor, and i-vector extractor. Then, we extract the corresponding speaker embeddings of fixed length from the depression speech database of the Gansu Provincial Key Laboratory of Wearable Computing. Support Vector Machine (SVM) and Random Forest (RF) are used to obtain the classification results of speaker embeddings in nine speech tasks. To make full use of the information of speech tasks with different scenes and emotions, we aggregate the classification results of nine tasks into new features and then obtain the final classification results by using Multilayer Perceptron (MLP). In order to take advantage of the complementary effects of different features, Resnet x-vectors based on different acoustic features are fused in the ensemble learning method. Results: Experimental results demonstrate that (1) MFCC-based Resnet x-vectors perform best among the nine speaker embeddings for depression detection; (2) interview speech is better than picture descriptions speech, and neutral stimulus is the best among the three emotional valences in the depression recognition task; (3) our multi-task ensemble learning method with MFCC-based Resnet x-vectors can effectively identify depressed patients; (4) in all cases, the combination of MFCC-based Resnet x-vectors and PLP-based Resnet x-vectors in our ensemble learning method achieves the best results, outperforming other literature studies using the depression speech database. Discussion: Our multi-task ensemble learning method with MFCC-based Resnet x-vectors can fuse the depression related information of different stimuli effectively, which provides a new approach for depression detection. The limitation of this method is that speaker embeddings extractors were pre-trained on the out-domain dataset. We will consider using the augmented in-domain dataset for pre-training to improve the depression recognition performance further.

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.

Optimizing COVID-19 vaccine allocation considering the target population.

Vaccine allocation strategy for COVID-19 is an emerging and important issue that affects the efficiency and control of virus spread. In order to improve the fairness and efficiency of vaccine distribution, this paper studies the optimization of vaccine distribution under the condition of limited number of vaccines. We pay attention to the target population before distributing vaccines, including attitude toward the vaccination, priority groups for vaccination, and vaccination priority policy. Furthermore, we consider inventory and budget indexes to maximize the precise scheduling of vaccine resources. A mixed-integer programming model is developed for vaccine distribution considering the target population from the viewpoint of fairness and efficiency. Finally, a case study is provided to verify the model and provide insights for vaccine distribution.

Optimizing COVID-19 vaccine allocation considering the target population

Vaccine allocation strategy for COVID-19 is an emerging and important issue that affects the efficiency and control of virus spread. In order to improve the fairness and efficiency of vaccine distribution, this paper studies the optimization of vaccine distribution under the condition of limited number of vaccines. We pay attention to the target population before distributing vaccines, including attitude toward the vaccination, priority groups for vaccination, and vaccination priority policy. Furthermore, we consider inventory and budget indexes to maximize the precise scheduling of vaccine resources. A mixed-integer programming model is developed for vaccine distribution considering the target population from the viewpoint of fairness and efficiency. Finally, a case study is provided to verify the model and provide insights for vaccine distribution.

The Impact of Psychological Capital and Social Capital on Residents' Mental Health and Happiness During COVID-19: Evidence From China.

Objective: This paper studies the mediating and interactive effects of social capital on psychological capital and the feeling of happiness from the impact of COVID-19. Since its emergence, the COVID-19 pandemic has taken a toll on people's mental health and affected their hopes for the future. Lifestyle and economic conditions have also been affected and have subsequently impacted people's sense of confidence in life. This could increase the likelihood of many people developing mental health issues, such as anxiety or depression. Therefore, it is vital to study the influence of psychological capital and social capital on people's subjective psychology and happiness experiences. Materials and Methods: Using an ordered probit model, this paper studied the independent influence and interaction between psychological capital and social capital on people's happiness. The ordered probit model was chosen because subjective well-being (SWB) is an ordered variable. We further used structural equation modeling (SEM) to study the mediating effects of social capital on psychological capital and happiness. Results: The regression results showed that both psychological capital and social capital were significantly positively correlated with happiness when controlling for other factors. In addition, psychological and social capital significantly interacted, in which the psychological capital promotes the effect of social capital on happiness. Moreover, the effect of psychological capital on happiness was greater than that of social capital, demonstrating that happiness is more greatly influenced by subjective psychological experience. The interaction coefficient of psychological and social capital was also significant, showing that the two have mutually reinforcing effects on happiness. Finally, health, income class, real estate, stranger trust, age, and urban household registration had significant positive effects on happiness, while the view of money, being female, education had a negative relationship with happiness. The SEM results showed that the mediating effect of psychological capital on happiness was partly transmitted through social capital: the total effect of psychological capital on happiness was highly significant (p < 0.0001), as was the total effect of social capital on happiness (p < 0.0001); however, the coefficient for psychological capital was greater than that for social capital. Through heterogeneity analysis, we found that the relationship between psychological capital, social capital, and happiness was significantly positive in each sub-sample group. There was also a significant interaction between psychological and social capital for men, women, urban and rural residents, and higher education background sample groups. However, the interaction was not significant in the sample group without higher education. In addition, the relationship between the happiness of rural residents and their educational background and gender was not significant. Conclusion: We found that psychological and social capital have significant positive relationships and effects on happiness. Psychological capital demonstrated both direct and indirect influences on happiness, and further strengthens the influence of social capital on happiness. These results support a scheme to emphasize psychological support during the COVID-19 pandemic period to enhance the mental health of citizens.

Food supply network disruption and mitigation: an integrated perspective of traceability technology and network structure

The 2019 coronavirus disease (COVID-19) epidemic has caused serious disruptions in food supply networks. Based on the case of the remerging epidemic in China, this paper aims to investigate food supply network disruption and its mitigation from technical and structural perspectives. To solve the optimal policy choice problem that how to improve mitigation capability of food supply networks by using traceability technology and adjusting network structure, the occurrence mechanism of food supply network disruptions is revealed through a case study of the remerging COVID-19 outbreak in Beijing’s Xinfadi market. Five typical traceability solutions are proposed to mitigate network disruptions and their technical attributes are analyzed to establish disruption mitigation models. The structure of food supply networks is also controlled to mitigate disruptions. The structural attributes of three fundamental networks are extracted to adjust the network connections pattern in disruption mitigation models. Next, simulation experiments involving the disruption mitigation models are carried out to explore the independent and joint effects of traceability technology and network structure on mitigation capability. The findings suggest that accuracy makes a more positive effect on the mitigation capability of food supply networks than timeliness due to the various technical compositions behind them;the difference between these effects determines the choice decision of supply networks on traceability solution types. Likewise, betweenness centralization makes a positive effect but degree centralization makes a negative effect on mitigation capability because intermediary firms and focal firms in food supply networks have different behavior characteristics;these effects are both regulated by supply network types and exhibit different sensitivities. As for the joint effect of technical and structural attributes on mitigation capability, the joint effect of accuracy and betweenness centralization is bigger than the independent effects but smaller than their sum;the joint effect of timeliness and betweenness centralization depends on networks type;while the positive effect of accuracy or timeliness on mitigation capability is greater than the negative effect of degree centralization;theses joint effects are caused by the complicated interactive effects between technical composition and behaviors of intermediary firms or focal firms. These findings contribute to disruption management and decision-making theories and practices.

Comparing variability in diagnosis of upper respiratory tract infections in patients using syndromic, next generation sequencing, and PCR-based methods.

Early and accurate diagnosis of respiratory pathogens and associated outbreaks can allow for the control of spread, epidemiological modeling, targeted treatment, and decision making-as is evident with the current COVID-19 pandemic. Many respiratory infections share common symptoms, making them difficult to diagnose using only syndromic presentation. Yet, with delays in getting reference laboratory tests and limited availability and poor sensitivity of point-of-care tests, syndromic diagnosis is the most-relied upon method in clinical practice today. Here, we examine the variability in diagnostic identification of respiratory infections during the annual infection cycle in northern New Mexico, by comparing syndromic diagnostics with polymerase chain reaction (PCR) and sequencing-based methods, with the goal of assessing gaps in our current ability to identify respiratory pathogens. Of 97 individuals that presented with symptoms of respiratory infection, only 23 were positive for at least one RNA virus, as confirmed by sequencing. Whereas influenza virus (n = 7) was expected during this infection cycle, we also observed coronavirus (n = 7), respiratory syncytial virus (n = 8), parainfluenza virus (n = 4), and human metapneumovirus (n = 1) in individuals with respiratory infection symptoms. Four patients were coinfected with two viruses. In 21 individuals that tested positive using PCR, RNA sequencing completely matched in only 12 (57%) of these individuals. Few individuals (37.1%) were diagnosed to have an upper respiratory tract infection or viral syndrome by syndromic diagnostics, and the type of virus could only be distinguished in one patient. Thus, current syndromic diagnostic approaches fail to accurately identify respiratory pathogens associated with infection and are not suited to capture emerging threats in an accurate fashion. We conclude there is a critical and urgent need for layered agnostic diagnostics to track known and unknown pathogens at the point of care to control future outbreaks.

Capturing Time Dynamics From Speech Using Neural Networks for Surgical Mask Detection.

The importance of detecting whether a person wears a face mask while speaking has tremendously increased since the outbreak of SARS-CoV-2 (COVID-19), as wearing a mask can help to reduce the spread of the virus and mitigate the public health crisis. Besides affecting human speech characteristics related to frequency, face masks cause temporal interferences in speech, altering the pace, rhythm, and pronunciation speed. In this regard, this paper presents two effective neural network models to detect surgical masks from audio. The proposed architectures are both based on Convolutional Neural Networks (CNNs), chosen as an optimal approach for the spatial processing of the audio signals. One architecture applies a Long Short-Term Memory (LSTM) network to model the time-dependencies. Through an additional attention mechanism, the LSTM-based architecture enables the extraction of more salient temporal information. The other architecture (named ConvTx) retrieves the relative position of a sequence through the positional encoder of a transformer module. In order to assess to which extent both architectures can complement each other when modelling temporal dynamics, we also explore the combination of LSTM and Transformers in three hybrid models. Finally, we also investigate whether data augmentation techniques, such as, using transitions between audio frames and considering gender-dependent frameworks might impact the performance of the proposed architectures. Our experimental results show that one of the hybrid models achieves the best performance, surpassing existing state-of-the-art results for the task at hand.

The Impact of Psychological Capital and Social Capital on Residents’ Mental Health and Happiness During COVID-19: Evidence From China

Objective This paper studies the mediating and interactive effects of social capital on psychological capital and the feeling of happiness from the impact of COVID-19. Since its emergence, the COVID-19 pandemic has taken a toll on people’s mental health and affected their hopes for the future. Lifestyle and economic conditions have also been affected and have subsequently impacted people’s sense of confidence in life. This could increase the likelihood of many people developing mental health issues, such as anxiety or depression. Therefore, it is vital to study the influence of psychological capital and social capital on people’s subjective psychology and happiness experiences. Materials and Methods Using an ordered probit model, this paper studied the independent influence and interaction between psychological capital and social capital on people’s happiness. The ordered probit model was chosen because subjective well-being (SWB) is an ordered variable. We further used structural equation modeling (SEM) to study the mediating effects of social capital on psychological capital and happiness. Results The regression results showed that both psychological capital and social capital were significantly positively correlated with happiness when controlling for other factors. In addition, psychological and social capital significantly interacted, in which the psychological capital promotes the effect of social capital on happiness. Moreover, the effect of psychological capital on happiness was greater than that of social capital, demonstrating that happiness is more greatly influenced by subjective psychological experience. The interaction coefficient of psychological and social capital was also significant, showing that the two have mutually reinforcing effects on happiness. Finally, health, income class, real estate, stranger trust, age, and urban household registration had significant positive effects on happiness, while the view of money, being female, education had a negative relationship with happiness. The SEM results showed that the mediating effect of psychological capital on happiness was partly transmitted through social capital: the total effect of psychological capital on happiness was highly significant (p < 0.0001), as was the total effect of social capital on happiness (p < 0.0001);however, the coefficient for psychological capital was greater than that for social capital. Through heterogeneity analysis, we found that the relationship between psychological capital, social capital, and happiness was significantly positive in each sub-sample group. There was also a significant interaction between psychological and social capital for men, women, urban and rural residents, and higher education background sample groups. However, the interaction was not significant in the sample group without higher education. In addition, the relationship between the happiness of rural residents and their educational background and gender was not significant. Conclusion We found that psychological and social capital have significant positive relationships and effects on happiness. Psychological capital demonstrated both direct and indirect influences on happiness, and further strengthens the influence of social capital on happiness. These results support a scheme to emphasize psychological support during the COVID-19 pandemic period to enhance the mental health of citizens.

China’s Systemic Financial Risk: Basic Dimensions, Key Areas, and Evolving Trends

The COVID-19 pandemic, the regulation of real estate, and external uncertainties are the core variables in the recent evolution of China’s financial risks, and overall planning and structural deployment are the key guarantees for China’s financial stability. From an aggregate perspective, China’s systemic financial risk tended to ease overall in 2021, but remained high. The risk profile of China’s financial system in 2021 presented five important features. First, the macro leverage ratio fell slightly, but exposed the hidden dangers of balance sheet recession. Second, there was a certain blockage in the transmission of financial system liquidity to the real economy. Third, the fragility of the financial system was further exposed, the bond default balance reached a new high, the structural differentiation of bonds between state-owned and private enterprises became prominent, and private enterprise default became more serious. Fourth, the contagion effect of domestic cross-market financial risks remained significant. Fifth, the international political and economic situation was volatile, and spillover effects such as the rising prices of raw materials, the inauguration of a new US administration, and the shift of the Federal Reserve’s monetary policy were significantly strengthened. In terms of key risk areas, the risks of the real estate market, hidden government debt, and small- and medium-sized domestic banks were quite prominent. in 2022, pandemic prevention and control, economic recovery, and structural upgrading will remain the main themes of China’s development. China’s financial risks are generally under control, but the country will still face major risks such as a high macro leverage ratio, tight market liquidity, increasing debt vulnerability, significant spillover effects, and rising volatility in the international market.

Study on the Localization of Fangcang Shelter Hospitals During Pandemic Outbreaks.

In the event of pandemic, it is essential for government authority to implement responses to control the pandemic and protect people's health with rapidity and efficicency. In this study, we first develop an evaluation framework consisting of the entropy weight method (EWM) and the technique for order preference by similarity to ideal solution (TOPSIS) to identify the preliminary selection of Fangcang shelter hospitals; next, we consider the timeliness of isolation and treatment of patients with different degrees of severity of the infectious disease, with the referral to and triage in Fangcang shelter hospitals characterized and two optimization models developed. The computational results of Model 1 and Model 2 are compared and analyzed. A case study in Xuzhou, Jiangsu Province, China, is used to demonstrate the real-life applicability of the proposed models. The two-stage localization method gives decision-makers more options in case of emergencies and can effectively designate the location. This article may give recommendations of and new insights into parameter settings in isolation hospital for governments and public health managers.

Federated Learning Driven Secure Internet of Medical Things

With the outbreak of COVID-19, people are experiencing increasing physical and mental health issues. Therefore, personal daily healthcare and monitoring become vital for our physical and mental well being. As a combination of the Internet of Things (IoT) and healthcare services, the Internet of Medical Things (IoMT) has emerged to provide intelligent medical services. However, privacy and security concerns have deterred its wide adoption. In this article, we propose a Federated Learning Driven IoMT (FLDIoMT) framework, which aims to support flexible deployment of IoMT services and address the privacy and security issues at the same time. Also, a systematic workflow of IoMT services is proposed to show an efficient data processing and analysis scheme for specific medical applications. Moreover, we demonstrate the feasibility of the proposed FLDIoMT framework by implementing a novel sleep monitoring system called iSmile.

Healthy humans can be a source of antibodies countering COVID-19.

Here, we describe the isolation of 18 unique anti SARS-CoV-2 human single-chain antibodies from an antibody library derived from healthy donors. The selection used a combination of phage and yeast display technologies and included counter-selection strategies meant to direct the selection of the receptor-binding motif (RBM) of SARS-CoV-2 spike protein's receptor binding domain (RBD2). Selected antibodies were characterized in various formats including IgG, using flow cytometry, ELISA, high throughput SPR, and fluorescence microscopy. We report antibodies' RBD2 recognition specificity, binding affinity, and epitope diversity, as well as ability to block RBD2 binding to the human receptor angiotensin-converting enzyme 2 (ACE2) and to neutralize authentic SARS-CoV-2 virus infection in vitro. We present evidence supporting that: 1) most of our antibodies (16 out of 18) selectively recognize RBD2; 2) the best performing 8 antibodies target eight different epitopes of RBD2; 3) one of the pairs tested in sandwich assays detects RBD2 with sub-picomolar sensitivity; and 4) two antibody pairs inhibit SARS-CoV-2 infection at low nanomolar half neutralization titers. Based on these results, we conclude that our antibodies have high potential for therapeutic and diagnostic applications. Importantly, our results indicate that readily available non immune (naïve) antibody libraries obtained from healthy donors can be used to select high-quality monoclonal antibodies, bypassing the need for blood of infected patients, and offering a widely accessible and low-cost alternative to more sophisticated and expensive antibody selection approaches (e.g. single B cell analysis and natural evolution in humanized mice).

How Do Perceived Social Support and Community Social Network Alleviate Psychological Distress During COVID-19 Lockdown? The Mediating Role of Residents' Epidemic Prevention Capability.

Aim: Following the outbreak of the COVID-19 epidemic, China adopted community isolation management measures. During the "lockdown" period, urban communities were the most basic prevention and control unit for the epidemic. The effectiveness of community epidemic prevention directly affects the spread of the virus and social stability. Therefore, the aim of this study was to explore the status quo and influencing factors of psychological distress. Methods: For this study, 1,430 community households were randomly selected in key cities affected by the epidemic, and a questionnaire survey was administered during the lockdown period. A structural equation model was used to analyse the influencing factors of community epidemic prevention effects. A total of 1,326 valid questionnaires were collected, with a valid response rate of 92.73%. Results: In this study, the differences in psychological distress among different community types were statistically significant (t = 58.41, P < 0.01). The results showed that epidemic prevention capability played a mediating role. The results of the high-order structural equation model analysis showed that perceived social support (ß = -0.275, P = 0.000) and community social network (ß = -0.296, P < 0.01) were significantly negatively correlated with psychological distress. Conclusions: Community social support indirectly relieves psychological anxiety and improves the effect of epidemic prevention by enhancing residents' ability to prevent epidemics. The community social network help residents reduce the risk of outbreaks and indirectly alleviate psychological distress.

EDGE COVID-19: A Web Platform to generate submission-ready genomes from SARS-CoV-2 sequencing efforts.

SUMMARY: Genomics has become an essential technology for surveilling emerging infectious disease outbreaks. A range of technologies and strategies for pathogen genome enrichment and sequencing are being used by laboratories worldwide, together with different, and sometimes ad hoc, analytical procedures for generating genome sequences. A fully integrated analytical process for raw sequence to consensus genome determination, suited to outbreaks such as the ongoing COVID-19 pandemic, is critical to provide a solid genomic basis for epidemiological analyses and well-informed decision making. We have developed a web-based platform and integrated bioinformatic workflows that help to provide consistent high-quality analysis of SARS-CoV-2 sequencing data generated with either the Illumina or Oxford Nanopore Technologies (ONT). Using an intuitive web-based interface, this workflow automates data quality control, SARS-CoV-2 reference-based genome variant and consensus calling, lineage determination, and provides the ability to submit the consensus sequence and necessary metadata to GenBank, GISAID, and INSDC raw data repositories. We tested workflow usability using real world data and validated the accuracy of variant and lineage analysis using several test datasets, and further performed detailed comparisons with results from the COVID-19 Galaxy Project workflow. Our analyses indicate that EC-19 workflows generate high quality SARS-CoV-2 genomes. Finally, we share a perspective on patterns and impact observed with Illumina vs ONT technologies on workflow congruence and differences. AVAILABILITY: https://edge-covid19.edgebioinformatics.org, and https://github.com/LANL-Bioinformatics/EDGE/tree/SARS-CoV2. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

Recent advances in facemask devices for in vivo sampling of human exhaled breath aerosols and inhalable environmental exposures.

Since the COVID-19 pandemic, the unprecedented use of facemasks has been requiring for wearing in daily life. By wearing facemask, human exhaled breath aerosols and inhaled environmental exposures can be efficiently filtered and thus various filtration residues can be deposited in facemask. Therefore, facemask could be a simple, wearable, in vivo, onsite and noninvasive sampler for collecting exhaled and inhalable compositions, and gain new insights into human health and environmental exposure. In this review, the recent advances in developments and applications of in vivo facemask sampling of human exhaled bacteria, viruses, proteins, and metabolites, and inhalable facemask contaminants and air pollutants, are reviewed. New features of facemask sampling are highlighted. The perspectives and challenges on further development and potential applications of facemask devices are also discussed.

Mass Spectrometry-Based Human Breath Analysis: Towards COVID-19 Diagnosis and Research.

COVID-19 is a highly contagious respiratory disease that can be infected through human exhaled breath. Human breath analysis is an attractive strategy for rapid diagnosis of COVID-19 in a non-invasive way by monitoring breath biomarkers. Mass spectrometry (MS)-based approaches offer a promising analytical platform for human breath analysis due to their high speed, specificity, sensitivity, reproducibility, and broad coverage, as well as its versatile coupling methods with different chromatographic separation, and thus can lead to a better understanding of the clinical and biochemical processes of COVID-19. Herein, we try to review the developments and applications of MS-based approaches for multidimensional analysis of COVID-19 breath samples, including metabolites, proteins, microorganisms, and elements. New features of breath sampling and analysis are highlighted. Prospects and challenges on MS-based breath analysis related to COVID-19 diagnosis and study are discussed.