Accuracy of serological tests for COVID-19: A systematic review and meta-analysis.

Objective: To determine the diagnostic accuracy of serological tests for coronavirus disease-2019 (COVID-19). Methods: PubMed, Embase and the Cochrane Library were searched from January 1 2020 to September 2 2022. We included studies that measured the sensitivity, specificity or both qualities of a COVID-19 serological test and a reference standard of a viral culture or reverse transcriptase polymerase chain reaction (RT-PCR). The risk of bias was assessed by using quality assessment of diagnostic accuracy studies 2 (QUADAS-2). The primary outcomes included overall sensitivity and specificity, as stratified by the methods of serological testing [enzyme-linked immunosorbent assays (ELISAs), lateral flow immunoassays (LFIAs) or chemiluminescent immunoassays (CLIAs)] and immunoglobulin classes (IgG, IgM, or both). Secondary outcomes were stratum-specific sensitivity and specificity within the subgroups, as defined by study or participant characteristics, which included the time from the onset of symptoms, testing via commercial kits or an in-house assay, antigen target, clinical setting, serological kit as the index test and the type of specimen for the RT-PCR reference test. Results: Eight thousand seven hundred and eighty-five references were identified and 169 studies included. Overall, we judged the risk of bias to be high in 47.9 % (81/169) of the studies, and a low risk of applicability concerns was found in 100% (169/169) of the studies. For each method of testing, the pooled sensitivity of the ELISAs ranged from 81 to 82%, with sensitivities ranging from 69 to 70% for the LFIAs and 77% to 79% for the CLIAs. Among the evaluated tests, IgG (80-81%)-based tests exhibited better sensitivities than IgM-based tests (66-68%). IgG/IgM-based CLIA had the highest sensitivity [87% (86-88%)]. All of the tests displayed high specificity (97-98%). Heterogeneity was observed in all of the analyses. The detection of nucleocapsid protein (77-80%) as the antigen target was found to offer higher sensitivity results than surface protein detection (66-68%). Sensitivity was higher in the in-house assays (78-79%) than in the commercial kits (47-48%). Conclusion: Among the evaluated tests, ELISA and CLIA tests performed better in terms of sensitivity than did the LFIA. IgG-based tests had higher sensitivity than IgM-based tests, and combined IgG/IgM test-based CLIA tests had the best overall diagnostic test accuracy. The type of sample, serological kit and timing of use of the specific tests were associated with the diagnostic accuracy. Due to the limitations of the serological tests, other techniques should be quickly approved to provide guidance for the correct diagnosis of COVID-19.

Gut microbiota, inflammation, and molecular signatures of host response to infection.

Gut microbial dysbiosis has been linked to many noncommunicable diseases. However, little is known about specific gut microbiota composition and its correlated metabolites associated with molecular signatures underlying host response to infection. Here, we describe the construction of a proteomic risk score based on 20 blood proteomic biomarkers, which have recently been identified as molecular signatures predicting the progression of the COVID-19. We demonstrate that in our cohort of 990 healthy individuals without infection, this proteomic risk score is positively associated with proinflammatory cytokines mainly among older, but not younger, individuals. We further discover that a core set of gut microbiota can accurately predict the above proteomic biomarkers among 301 individuals using a machine learning model and that these gut microbiota features are highly correlated with proinflammatory cytokines in another independent set of 366 individuals. Fecal metabolomics analysis suggests potential amino acid-related pathways linking gut microbiota to host metabolism and inflammation. Overall, our multi-omics analyses suggest that gut microbiota composition and function are closely related to inflammation and molecular signatures of host response to infection among healthy individuals. These results may provide novel insights into the cross-talk between gut microbiota and host immune system.

Hospital factors that predict intention of health care workers to leave their job during the COVID-19 pandemic.

PURPOSE: To identify factors responsible for hospital health care workers' intention to leave their job during the COVID-19 pandemic. DESIGN: A cross-sectional study was performed. METHODS: A self-administered questionnaire was delivered to solicit hospital health care workers' demographics, intention to leave, workplace environment, and changes related to COVID-19 from July to November 2020 in Taiwan. Principal component analysis was performed to compare group-related factors. Multiple logistic regression was used to determine the risk factors for the intention of health care workers to leave their job. FINDINGS: Among the 1209 health care workers (mean age, 36.3 years) who participated in the study, intention to leave the job was found to be related to factors relating to COVID-19, including perceived risk, affected social relationships, and increased workload and job stress, after adjustment for demographic and work factors. Supportive administration/management were protective factors against leaving the job. These results were supported by sensitivity analyses. CONCLUSIONS: Our findings suggest that the intention of health care workers to leave their job during a pandemic is related to potentially modifiable factors relating to the infection itself and work environment. CLINICAL RELEVANCE: High perceived risk of COVID-19, affected social relationaops, and increased workload and job stress were positively associated with the intention of health care workers to leave their job, whereas supportive administration and management were protective factors against leaving the job. Development of workplace strategies is important to help mitigate these above factors, improve psychological wellbeing, and promote workforce stability.

ASGR1 is a candidate receptor for SARS-CoV-2 that promotes infection of liver cells (preprint)

Currently, the COVID-19 pandemic, caused by SARS-CoV-2 infection, represents a serious public health problem worldwide. Although it has been shown that ACE2 serves as the main receptor for SARS-CoV-2 entry into host cells, studies have shown that ACE2 is expressed at extremely low levels in various tissues, especially in some organs where virus particles have been found, such as the heart and liver. Therefore, these organs potentially express additional SARS-CoV-2 receptors that have not yet been discovered. Here, by a genome-wide CRISPR-Cas9 activation library screening, we found that ASGR1 promoted SARS-CoV-2 infection of 293T cells. In Huh-7 and HepG2 cell lines, simultaneous knock out of ACE2 and ASGR1 prevented SARS-CoV-2 pseudovirus infection. In the immortalized THLE-2 hepatocyte cell line and primary liver parenchymal cells, both of which hardly express ACE2, SARS-CoV-2 could successfully establish an infection. After treatment with ASGR1 antibody, the infection rate significantly reduced. This suggests that SARS-CoV-2 infects liver cells mainly through an ASGR1-dependent mechanism. Finally, we also found that the soluble ASGR1 could not only prevent the SARS-CoV-2 pseudovirus, which binds to the ASGR1 receptors, from infecting host liver cells, but also had a protective effect on those expressing ACE2, indicating that administration of soluble ASGR1 protein may represent a new treatment approach. CONCLUSIONS: Colletively, these findings indicate that ASGR1 is a candidate receptor for SARS-CoV-2 that promotes infection of liver cells.

Potential Use of Serum Proteomics for Monitoring COVID-19 Progression to Complement RT-PCR Detection.

RT-PCR is the primary method to diagnose COVID-19 and is also used to monitor the disease course. This approach, however, suffers from false negatives due to RNA instability and poses a high risk to medical practitioners. Here, we investigated the potential of using serum proteomics to predict viral nucleic acid positivity during COVID-19. We analyzed the proteome of 275 inactivated serum samples from 54 out of 144 COVID-19 patients and shortlisted 42 regulated proteins in the severe group and 12 in the non-severe group. Using these regulated proteins and several key clinical indexes, including days after symptoms onset, platelet counts, and magnesium, we developed two machine learning models to predict nucleic acid positivity, with an AUC of 0.94 in severe cases and 0.89 in non-severe cases, respectively. Our data suggest the potential of using a serum protein-based machine learning model to monitor COVID-19 progression, thus complementing swab RT-PCR tests. More efforts are required to promote this approach into clinical practice since mass spectrometry-based protein measurement is not currently widely accessible in clinic.

Eleven routine clinical features predict COVID-19 severity uncovered by machine learning of longitudinal measurements.

Severity prediction of COVID-19 remains one of the major clinical challenges for the ongoing pandemic. Here, we have recruited a 144 COVID-19 patient cohort, resulting in a data matrix containing 3,065 readings for 124 types of measurements over 52 days. A machine learning model was established to predict the disease progression based on the cohort consisting of training, validation, and internal test sets. A panel of eleven routine clinical factors constructed a classifier for COVID-19 severity prediction, achieving accuracy of over 98% in the discovery set. Validation of the model in an independent cohort containing 25 patients achieved accuracy of 80%. The overall sensitivity, specificity, positive predictive value (PPV), and negative predictive value (NPV) were 0.70, 0.99, 0.93, and 0.93, respectively. Our model captured predictive dynamics of lactate dehydrogenase (LDH) and creatine kinase (CK) while their levels were in the normal range. This model is accessible at https://www.guomics.com/covidAI/ for research purpose.

Proteomic and metabolomic investigation of serum lactate dehydrogenase elevation in COVID-19 patients.

Serum lactate dehydrogenase (LDH) has been established as a prognostic indicator given its differential expression in COVID-19 patients. However, the molecular mechanisms underneath remain poorly understood. In this study, 144 COVID-19 patients were enrolled to monitor the clinical and laboratory parameters over 3 weeks. Serum LDH was shown elevated in the COVID-19 patients on admission and declined throughout disease course, and its ability to classify patient severity outperformed other biochemical indicators. A threshold of 247 U/L serum LDH on admission was determined for severity prognosis. Next, we classified a subset of 14 patients into high- and low-risk groups based on serum LDH expression and compared their quantitative serum proteomic and metabolomic differences. The results showed that COVID-19 patients with high serum LDH exhibited differentially expressed blood coagulation and immune responses including acute inflammatory responses, platelet degranulation, complement cascade, as well as multiple different metabolic responses including lipid metabolism, protein ubiquitination and pyruvate fermentation. Specifically, activation of hypoxia responses was highlighted in patients with high LDH expressions. Taken together, our data showed that serum LDH levels are associated with COVID-19 severity, and that elevated serum LDH might be consequences of hypoxia and tissue injuries induced by inflammation.

The risk factors for severe patients with COVID-19 in China: A systematic review and meta-analysis

COVID-19 is spreading exponentially. In order to optimize medical resources allocation and reduce mortality, biomarkers are needed to differentiate between COVID-19 patients with or without severe diseases early as possible. We searched Ovid MEDLINE(R), Ovid EMBASE, CNKI, Wanfang, VIP databases, the Cochrane Library, and medRxiv for primary articles in English or Chinese up to March 30, 2020 to systematically evaluate the risk factors for severe patients in China. Mean difference or standardize mean difference and odds ratio with 95% confidence intervals were performed by random-effect or fixed models in cases of significant heterogeneity between studies. We used I 2 to evaluate the magnitude of heterogeneity. A total of 54 articles involving about 7000 patients were eligible for this meta-analysis. In total, 52 of 67 parameters between severe and non-severe cases were significantly different. Elderly male patients with comorbidities including hypertension, diabetes, chronic obstructive pulmonary disease (COPD) cardiovascular disease, cerebrovascular disease, chronic kidney disease, or cancer were more common in severe COVID-19 patients. Regarding the clinical manifestations on admission, fever, cough, expectoration, dyspnea, chest distress, fatigue, headache, chills, anorexia, or abdominal pain were more prevalent in severe COVID-19 patients. The results of the clinical examination showed that high C-reactive protein (CRP), high lactate dehydrogenase (LDH), high D-dimer, and decreased T lymphocytes cells subsets, decreased lymphocyte may help clinicians predict the progression of severe illness in patients with COVID-19. Our findings will be conducive for clinician to stratify the COVID-19 patients to reduce mortality under the relative shortage of medical resources. [ABSTRACT FROM AUTHOR] Copyright of European Journal of Inflammation (Sage Publications, Ltd.) is the property of Sage Publications, Ltd. and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Multi-organ proteomic landscape of COVID-19 autopsies.

The molecular pathology of multi-organ injuries in COVID-19 patients remains unclear, preventing effective therapeutics development. Here, we report a proteomic analysis of 144 autopsy samples from seven organs in 19 COVID-19 patients. We quantified 11,394 proteins in these samples, in which 5,336 were perturbed in the COVID-19 patients compared to controls. Our data showed that cathepsin L1, rather than ACE2, was significantly upregulated in the lung from the COVID-19 patients. Systemic hyperinflammation and dysregulation of glucose and fatty acid metabolism were detected in multiple organs. We also observed dysregulation of key factors involved in hypoxia, angiogenesis, blood coagulation, and fibrosis in multiple organs from the COVID-19 patients. Evidence for testicular injuries includes reduced Leydig cells, suppressed cholesterol biosynthesis, and sperm mobility. In summary, this study depicts a multi-organ proteomic landscape of COVID-19 autopsies that furthers our understanding of the biological basis of COVID-19 pathology.

Erratum to "Withdrawal notice to the effectiveness of diaphragmatic breathing relaxation training for improving sleep quality among nursing staff during the COVID-19 outbreak: a before and after study" [Sleep Medicine X (2) (2020) 100026].

[This corrects the article DOI: 10.1016/j.sleepx.2020.100026.].

The effectiveness of diaphragmatic breathing relaxation training for improving sleep quality among nursing staff during the COVID-19 outbreak: a before and after study.

OBJECTIVES: Recent studies have demonstrated that first-line nurses involved in the coronavirus disease-2019 (COVID-19) crisis may experience sleep disturbances. As breathing relaxation techniques can improve sleep quality, anxiety, and depression, the current study aimed to evaluate the effectiveness of diaphragmatic breathing relaxation training (DBRT) for improving sleep quality among nurses in Wuhan, China during the COVID-19 outbreak. METHODS: This study used a quasi-experimental (before and after) intervention strategy, with 151 first-line nurses from four wards in Leishenshan hospital. The Pittsburgh Sleep Quality Index (PSQI), Self-Rating Anxiety Scale (SAS), and Self-Rating Depression Scale (SDS) to evaluate the effectiveness of DBRT before and after the intervention. Data were examined using the Shapiro-Wilk test, Levene's test, and paired t-test. RESULTS: A total of 140 nurses completed the DBRT sessions. First-line nurses achieved significant reductions in global sleep quality (p < 0.01), subjective sleep quality (p < 0.001), sleep latency (p < 0.01), sleep duration (p < 0.001), sleep disturbances (p < 0.001), habitual sleep efficiency (p = 0.015), daytime dysfunction (p = 0.001), and anxiety (p = 0.001). There were no significant reductions in the use of sleeping medication (p = 0.134) and depression (p = 0.359). CONCLUSION: DBRT is a useful non-pharmacological treatment for improving sleep quality and reducing anxiety among first-line nurses involved in the COVID-19 outbreak. The study protocol was clinically registered by the Chinese Clinical Trial Registry. CLINICAL TRIAL REGISTRATION NUMBER: ChiCTR2000032743.