Factors Associated with Primary Care Provider's Job Satisfaction and Organizational Commitment in China: A Machine Learning-Based Random Forest Analysis.

The objective of the study is to explore the factors that influence the job satisfaction and organizational commitment of primary care providers in China, with a focus on the impact of the COVID-19 pandemic and the rescission of restriction policies. We utilized the 20-item Minnesota Satisfaction Questionnaire (MSQ) and the 25-item organizational commitment survey to assess job satisfaction and organizational commitment. In total, 435 valid responses were included in our analysis. The average scores for job satisfaction and organizational commitment were 80.6 and 90.8. After a two-step tuning process, we built random forest models by machine learning. The results show income change, working years, working years in the current institute, and age were the four most important features associated with job satisfaction, organizational commitment, and most of their dimensions. The number of professional fields engaged, gender, job status, and types of endowment insurance were least associated. During pandemic time, income-related factors remain a core concern for primary care providers, whereas job security may lose its importance. These findings suggest that financial bonuses may be an effective way to boost morale, and age-specific motivation plans may be necessary.

Automated Quantification of CT Patterns Associated with COVID-19 from Chest CT.

PURPOSE: To present a method that automatically segments and quantifies abnormal CT patterns commonly present in coronavirus disease 2019 (COVID-19), namely ground glass opacities and consolidations. MATERIALS AND METHODS: In this retrospective study, the proposed method takes as input a non-contrasted chest CT and segments the lesions, lungs, and lobes in three dimensions, based on a dataset of 9749 chest CT volumes. The method outputs two combined measures of the severity of lung and lobe involvement, quantifying both the extent of COVID-19 abnormalities and presence of high opacities, based on deep learning and deep reinforcement learning. The first measure of (PO, PHO) is global, while the second of (LSS, LHOS) is lobe-wise. Evaluation of the algorithm is reported on CTs of 200 participants (100 COVID-19 confirmed patients and 100 healthy controls) from institutions from Canada, Europe and the United States collected between 2002-Present (April 2020). Ground truth is established by manual annotations of lesions, lungs, and lobes. Correlation and regression analyses were performed to compare the prediction to the ground truth. RESULTS: Pearson correlation coefficient between method prediction and ground truth for COVID-19 cases was calculated as 0.92 for PO (P < .001), 0.97 for PHO (P < .001), 0.91 for LSS (P < .001), 0.90 for LHOS (P < .001). 98 of 100 healthy controls had a predicted PO of less than 1%, 2 had between 1-2%. Automated processing time to compute the severity scores was 10 seconds per case compared to 30 minutes required for manual annotations. CONCLUSION: A new method segments regions of CT abnormalities associated with COVID-19 and computes (PO, PHO), as well as (LSS, LHOS) severity scores.

Association between diagnostic delay and prognosis of pulmonary tuberculosis in Shandong, China: a retrospective study.

BACKGROUND: Tuberculosis (TB) is one of the main infectious diseases that seriously threatens global health, while diagnostic delay (DD) and treatment dramatically threaten TB control. METHODS: Between 2005 and 2017 in Shandong, China, we enrolled pulmonary tuberculosis (PTB) patients with DD. DD trends were evaluated by Joinpoint regression, and associations between PTB patient characteristics and DD were estimated by univariate and multivariate logistic regression. The influence of DD duration on prognosis and sputum smear results were assessed by Spearman correlation coefficients. RESULTS: We identified 208,822 PTB cases with a median DD of 33 days (interquartile range (IQR) 18-63). The trend of PTB with DD declined significantly between 2009 and 2017 (annual percent change (APC): - 4.0%, P = 0.047, 2009-2013; APC: - 6.6%, P = 0.001, 2013-2017). Patients aged > 45 years old (adjusted odds ratio (aOR): 1.223, 95% confidence interval (CI) 1.189-1.257, 46-65 years; aOR: 1.306, 95% CI 1.267-1.346, > 65 years), farmers (aOR: 1.520, 95% CI 1.447-1.596), and those with a previous treatment history (aOR: 1.759, 95% CI 1.699-1.821) were prone to developing long DD (> 30 days, P < 0.05). An unfavorable outcome was negatively associated with a short DD (OR: 0.876, 95% CI 0.843-0.910, P < 0.001). Sputum smear positive rate and unfavorable outcomes were positively correlated with DD duration (Spearman correlation coefficients (rs) = 1, P < 0.001). CONCLUSIONS: The DD situation remains serious; more efficient and comprehensive strategies are urgently required to minimize DD, especially for high-risk patients.

Evaluation of Spike Protein Epitopes by Assessing the Dynamics of Humoral Immune Responses in Moderate COVID-19.

The coronavirus disease 2019 (COVID-19) pandemic is caused by a novel coronavirus called severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2). The spike protein (S) of SARS-CoV-2 is a major target for diagnosis and vaccine development because of its essential role in viral infection and host immunity. Currently, time-dependent responses of humoral immune system against various S protein epitopes are poorly understood. In this study, enzyme-linked immunosorbent assay (ELISA), peptide microarray, and antibody binding epitope mapping (AbMap) techniques were used to systematically analyze the dynamic changes of humoral immune responses against the S protein in a small cohort of moderate COVID-19 patients who were hospitalized for approximately two months after symptom onset. Recombinant truncated S proteins, target S peptides, and random peptides were used as antigens in the analyses. The assays demonstrated the dynamic IgM- and IgG recognition and reactivity against various S protein epitopes with patient-dependent patterns. Comprehensive analysis of epitope distribution along the spike gene sequence and spatial structure of the homotrimer S protein demonstrated that most IgM- and IgG-reactive peptides were clustered into similar genomic regions and were located at accessible domains. Seven S peptides were generally recognized by IgG antibodies derived from serum samples of all COVID-19 patients. The dynamic immune recognition signals from these seven S peptides were comparable to those of the entire S protein or truncated S1 protein. This suggested that the humoral immune system recognized few conserved S protein epitopes in most COVID-19 patients during the entire duration of humoral immune response after symptom onset. Furthermore, in this cohort, individual patients demonstrated stable immune recognition to certain S protein epitopes throughout their hospitalization period. Therefore, the dynamic characteristics of humoral immune responses to S protein have provided valuable information for accurate diagnosis and immunotherapy of COVID-19 patients.

Progress Identifying and Analyzing the Human Proteome: 2021 Metrics from the HUPO Human Proteome Project.

The 2021 Metrics of the HUPO Human Proteome Project (HPP) show that protein expression has now been credibly detected (neXtProt PE1 level) for 18 357 (92.8%) of the 19 778 predicted proteins coded in the human genome, a gain of 483 since 2020 from reports throughout the world reanalyzed by the HPP. Conversely, the number of neXtProt PE2, PE3, and PE4 missing proteins has been reduced by 478 to 1421. This represents remarkable progress on the proteome parts list. The utilization of proteomics in a broad array of biological and clinical studies likewise continues to expand with many important findings and effective integration with other omics platforms. We present highlights from the Immunopeptidomics, Glycoproteomics, Infectious Disease, Cardiovascular, Musculo-Skeletal, Liver, and Cancers B/D-HPP teams and from the Knowledgebase, Mass Spectrometry, Antibody Profiling, and Pathology resource pillars, as well as ethical considerations important to the clinical utilization of proteomics and protein biomarkers.

Crystal Structures of Bat and Human Coronavirus ORF8 Protein Ig-Like Domain Provide Insights Into the Diversity of Immune Responses.

ORF8 is a viral immunoglobulin-like (Ig-like) domain protein encoded by the severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) RNA genome. It tends to evolve rapidly and interfere with immune responses. However, the structural characteristics of various coronavirus ORF8 proteins and their subsequent effects on biological functions remain unclear. Herein, we determined the crystal structures of SARS-CoV-2 ORF8 (S84) (one of the epidemic isoforms) and the bat coronavirus RaTG13 ORF8 variant at 1.62 Å and 1.76 Å resolution, respectively. Comparison of these ORF8 proteins demonstrates that the 62-77 residues in Ig-like domain of coronavirus ORF8 adopt different conformations. Combined with mutagenesis assays, the residue Cys20 of ORF8 is responsible for forming the covalent disulfide-linked dimer in crystal packing and in vitro biochemical conditions. Furthermore, immune cell-binding assays indicate that various ORF8 (SARS-CoV-2 ORF8 (L84), ORF8 (S84), and RaTG13 ORF8) proteins have different interaction capabilities with human CD14+ monocytes in human peripheral blood. These results provide new insights into the specific characteristics of various coronavirus ORF8 and suggest that ORF8 variants may influence disease-related immune responses.

Structural Basis of a Human Neutralizing Antibody Specific to the SARS-CoV-2 Spike Protein Receptor-Binding Domain.

The emerging new lineages of severe acute respiratory syndrome coronavirus-2 (SARS-CoV-2) have marked a new phase of coronavirus disease 2019 (COVID-19). Understanding the recognition mechanisms of potent neutralizing monoclonal antibodies (NAbs) against the spike protein is pivotal for developing new vaccines and antibody drugs. Here, we isolated several monoclonal antibodies (MAbs) against the SARS-CoV-2 spike protein receptor-binding domain (S-RBD) from the B cell receptor repertoires of a SARS-CoV-2 convalescent. Among these MAbs, the antibody nCoV617 demonstrates the most potent neutralizing activity against authentic SARS-CoV-2 infection, as well as prophylactic and therapeutic efficacies against the human angiotensin-converting enzyme 2 (ACE2) transgenic mouse model in vivo. The crystal structure of S-RBD in complex with nCoV617 reveals that nCoV617 mainly binds to the back of the "ridge" of RBD and shares limited binding residues with ACE2. Under the background of the S-trimer model, it potentially binds to both "up" and "down" conformations of S-RBD. In vitro mutagenesis assays show that mutant residues found in the emerging new lineage B.1.1.7 of SARS-CoV-2 do not affect nCoV617 binding to the S-RBD. These results provide a new human-sourced neutralizing antibody against the S-RBD and assist vaccine development. IMPORTANCE COVID-19 is a respiratory disease caused by severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2). The COVID-19 pandemic has posed a serious threat to global health and the economy, so it is necessary to find safe and effective antibody drugs and treatments. The receptor-binding domain (RBD) in the SARS-CoV-2 spike protein is responsible for binding to the angiotensin-converting enzyme 2 (ACE2) receptor. It contains a variety of dominant neutralizing epitopes and is an important antigen for the development of new coronavirus antibodies. The significance of our research lies in the determination of new epitopes, the discovery of antibodies against RBD, and the evaluation of the antibodies' neutralizing effect. The identified antibodies here may be drug candidates for the development of clinical interventions for SARS-CoV-2.

COVID-19 and Tuberculosis Coinfection: An Overview of Case Reports/Case Series and Meta-Analysis.

Background: Coronavirus disease 2019 (COVID-19) and tuberculosis (TB) are two major infectious diseases posing significant public health threats, and their coinfection (aptly abbreviated COVID-TB) makes the situation worse. This study aimed to investigate the clinical features and prognosis of COVID-TB cases. Methods: The PubMed, Embase, Cochrane, CNKI, and Wanfang databases were searched for relevant studies published through December 18, 2020. An overview of COVID-TB case reports/case series was prepared that described their clinical characteristics and differences between survivors and deceased patients. Pooled odds ratios (ORs) with 95% confidence intervals (CIs) for death or severe COVID-19 were calculated. The quality of outcomes was assessed using GRADEpro. Results: Thirty-six studies were included. Of 89 COVID-TB patients, 19 (23.46%) died, and 72 (80.90%) were male. The median age of non-survivors (53.95 ± 19.78 years) was greater than that of survivors (37.76 ± 15.54 years) (p < 0.001). Non-survivors were more likely to have hypertension (47.06 vs. 17.95%) or symptoms of dyspnea (72.73% vs. 30%) or bilateral lesions (73.68 vs. 47.14%), infiltrates (57.89 vs. 24.29%), tree in bud (10.53% vs. 0%), or a higher leucocyte count (12.9 [10.5-16.73] vs. 8.015 [4.8-8.97] × 109/L) than survivors (p < 0.05). In terms of treatment, 88.52% received anti-TB therapy, 50.82% received antibiotics, 22.95% received antiviral therapy, 26.23% received hydroxychloroquine, and 11.48% received corticosteroids. The pooled ORs of death or severe disease in the COVID-TB group and the non-TB group were 2.21 (95% CI: 1.80, 2.70) and 2.77 (95% CI: 1.33, 5.74) (P < 0.01), respectively. Conclusion: In summary, there appear to be some predictors of worse prognosis among COVID-TB cases. A moderate level of evidence suggests that COVID-TB patients are more likely to suffer severe disease or death than COVID-19 patients. Finally, routine screening for TB may be recommended among suspected or confirmed cases of COVID-19 in countries with high TB burden.

Innate Immune Responses and Pulmonary Diseases.

Innate immunity is the first defense line of the host against various infectious pathogens, environmental insults, and other stimuli causing cell damages. Upon stimulation, pattern recognition receptors (PRRs) act as sensors to activate innate immune responses, containing NF-κB signaling, IFN response, and inflammasome activation. Toll-like receptors (TLRs), retinoic acid-inducible gene I-like receptors (RLRs), NOD-like receptors (NLRs), and other nucleic acid sensors are involved in innate immune responses. The activation of innate immune responses can facilitate the host to eliminate pathogens and maintain tissue homeostasis. However, the activity of innate immune responses needs to be tightly controlled to ensure the optimal intensity and duration of activation under various contexts. Uncontrolled innate immune responses can lead to various disorders associated with aberrant inflammatory response, including pulmonary diseases such as COPD, asthma, and COVID-19. In this chapter, we will have a broad overview of how innate immune responses function and the regulation and activation of innate immune response at molecular levels as well as their contribution to various pulmonary diseases. A better understanding of such association between innate immune responses and pulmonary diseases may provide potential therapeutic strategies.

Development and application of therapeutic antibodies against COVID-19.

The pandemic of Coronavirus disease 2019 (COVID-19) caused by the severe acute respiratory syndrome 2 coronavirus (SARS-CoV-2) continues to be a global health crisis. Fundamental studies at genome, transcriptome, proteome, and interactome levels have revealed many viral and host targets for therapeutic interventions. Hundreds of antibodies for treating COVID-19 have been developed at preclinical and clinical stages in the format of polyclonal antibodies, monoclonal antibodies, and cocktail antibodies. Four products, i.e., convalescent plasma, bamlanivimab, REGN-Cov2, and the cocktail of bamlanivimab and etesevimab have been authorized by the U.S. Food and Drug Administration (FDA) for emergency use. Hundreds of relevant clinical trials are ongoing worldwide. Therapeutic antibody therapies have been a very active and crucial part of COVID-19 treatment. In this review, we focus on the progress of therapeutic COVID-19 antibody development and application, discuss corresponding problems and challenges, suggesting new strategies and solutions.

Research on the Human Proteome Reaches a Major Milestone: >90% of Predicted Human Proteins Now Credibly Detected, According to the HUPO Human Proteome Project.

According to the 2020 Metrics of the HUPO Human Proteome Project (HPP), expression has now been detected at the protein level for >90% of the 19 773 predicted proteins coded in the human genome. The HPP annually reports on progress made throughout the world toward credibly identifying and characterizing the complete human protein parts list and promoting proteomics as an integral part of multiomics studies in medicine and the life sciences. NeXtProt release 2020-01 classified 17 874 proteins as PE1, having strong protein-level evidence, up 180 from 17 694 one year earlier. These represent 90.4% of the 19 773 predicted coding genes (all PE1,2,3,4 proteins in neXtProt). Conversely, the number of neXtProt PE2,3,4 proteins, termed the "missing proteins" (MPs), was reduced by 230 from 2129 to 1899 since the neXtProt 2019-01 release. PeptideAtlas is the primary source of uniform reanalysis of raw mass spectrometry data for neXtProt, supplemented this year with extensive data from MassIVE. PeptideAtlas 2020-01 added 362 canonical proteins between 2019 and 2020 and MassIVE contributed 84 more, many of which converted PE1 entries based on non-MS evidence to the MS-based subgroup. The 19 Biology and Disease-driven B/D-HPP teams continue to pursue the identification of driver proteins that underlie disease states, the characterization of regulatory mechanisms controlling the functions of these proteins, their proteoforms, and their interactions, and the progression of transitions from correlation to coexpression to causal networks after system perturbations. And the Human Protein Atlas published Blood, Brain, and Metabolic Atlases.

Automated Detection and Quantification of COVID-19 Airspace Disease on Chest Radiographs: A Novel Approach Achieving Expert Radiologist-Level Performance Using a Deep Convolutional Neural Network Trained on Digital Reconstructed Radiographs From Computed Tomography-Derived Ground Truth.

OBJECTIVES: The aim of this study was to leverage volumetric quantification of airspace disease (AD) derived from a superior modality (computed tomography [CT]) serving as ground truth, projected onto digitally reconstructed radiographs (DRRs) to (1) train a convolutional neural network (CNN) to quantify AD on paired chest radiographs (CXRs) and CTs, and (2) compare the DRR-trained CNN to expert human readers in the CXR evaluation of patients with confirmed COVID-19. MATERIALS AND METHODS: We retrospectively selected a cohort of 86 COVID-19 patients (with positive reverse transcriptase-polymerase chain reaction test results) from March to May 2020 at a tertiary hospital in the northeastern United States, who underwent chest CT and CXR within 48 hours. The ground-truth volumetric percentage of COVID-19-related AD (POv) was established by manual AD segmentation on CT. The resulting 3-dimensional masks were projected into 2-dimensional anterior-posterior DRR to compute area-based AD percentage (POa). A CNN was trained with DRR images generated from a larger-scale CT dataset of COVID-19 and non-COVID-19 patients, automatically segmenting lungs, AD, and quantifying POa on CXR. The CNN POa results were compared with POa quantified on CXR by 2 expert readers and to the POv ground truth, by computing correlations and mean absolute errors. RESULTS: Bootstrap mean absolute error and correlations between POa and POv were 11.98% (11.05%-12.47%) and 0.77 (0.70-0.82) for average of expert readers and 9.56% to 9.78% (8.83%-10.22%) and 0.78 to 0.81 (0.73-0.85) for the CNN, respectively. CONCLUSIONS: Our CNN trained with DRR using CT-derived airspace quantification achieved expert radiologist level of accuracy in the quantification of AD on CXR in patients with positive reverse transcriptase-polymerase chain reaction test results for COVID-19.

Quantification of Tomographic Patterns associated with COVID-19 from Chest CT.

PURPOSE: To present a method that automatically segments and quantifies abnormal CT patterns commonly present in coronavirus disease 2019 (COVID-19), namely ground glass opacities and consolidations. MATERIALS AND METHODS: In this retrospective study, the proposed method takes as input a non-contrasted chest CT and segments the lesions, lungs, and lobes in three dimensions, based on a dataset of 9749 chest CT volumes. The method outputs two combined measures of the severity of lung and lobe involvement, quantifying both the extent of COVID-19 abnormalities and presence of high opacities, based on deep learning and deep reinforcement learning. The first measure of (PO, PHO) is global, while the second of (LSS, LHOS) is lobewise. Evaluation of the algorithm is reported on CTs of 200 participants (100 COVID-19 confirmed patients and 100 healthy controls) from institutions from Canada, Europe and the United States collected between 2002-Present (April, 2020). Ground truth is established by manual annotations of lesions, lungs, and lobes. Correlation and regression analyses were performed to compare the prediction to the ground truth. RESULTS: Pearson correlation coefficient between method prediction and ground truth for COVID-19 cases was calculated as 0.92 for PO (P < .001), 0.97 for PHO(P < .001), 0.91 for LSS (P < .001), 0.90 for LHOS (P < .001). 98 of 100 healthy controls had a predicted PO of less than 1%, 2 had between 1-2%. Automated processing time to compute the severity scores was 10 seconds per case compared to 30 minutes required for manual annotations. CONCLUSION: A new method segments regions of CT abnormalities associated with COVID-19 and computes (PO, PHO), as well as (LSS, LHOS) severity scores.