Reducing COVID-19 diagnostic errors with dNTPαSe supplementation.

Timely and accurate diagnosis of COVID-19 is critical for controlling the pandemic. As the standard method to diagnose SARS-CoV-2, the real-time reverse transcription polymerase chain reaction (RT-qPCR) has good convenience. However, RT-qPCR still has a relatively high false-negative rate, particularly in the case of detecting low viral loads. In this study, using selenium-modified nucleoside triphosphates (dNTPαSe) in the RT-PCR reactions, we successfully increased the detection sensitivity and reduced the false-negative rate in COVID-19 diagnosis. By detecting positive controls, pseudovirus, and clinical samples with the commercial kits, we found that the dNTPαSe supplementation to these kits could generally offer smaller Ct values, permit the viral detection even in single-digit copies, and increase the detection specificity, sensitivity, and accuracy, thereby reducing the false-negative rate. Our experimental results demonstrated that dNTPαSe supplementation can make the commercial kits more specific, sensitive, and accurate, and this method is a convenient and efficient strategy for the disease detection and diagnosis.

Association of delayed chemoradiotherapy with elevated Epstein-Barr virus DNA load and adverse clinical outcome in nasopharyngeal carcinoma treatment during the COVID-19 pandemic: a retrospective study.

BACKGROUND: To summarize the impact of radiotherapy (RT) and chemotherapy delays on patients with nasopharyngeal carcinoma (NPC) during the COVID-19 pandemic. METHODS: We retrospectively included 233 patients with stage II-IVa NPC treated with RT and chemotherapy between December 11, 2019 and March 11, 2020. The outcomes were elevation in the EBV DNA load between two adjacent cycles of chemotherapy or during RT, and 1-year disease-free survival (DFS). RESULTS: RT delay occurred in 117 (50%) patients, and chemotherapy delay occurred in 220 (94%) patients. RT delay of ≥ 6 days was associated with a higher EBV DNA elevation rate (20.4% vs. 3.6%, odds ratio [OR] = 6.93 [95% CI = 2.49-19.32], P < 0.001), and worse 1-year DFS (91.2% vs. 97.8%, HR = 3.61 [95% CI = 1.37-9.50], P = 0.006), compared with on-schedule RT or delay of < 6 days. Chemotherapy delay of ≥ 10 days was not associated with a higher EBV DNA elevation rate (12.5% vs. 6.8%, OR = 1.94 [95% CI = 0.70-5.40], P = 0.20), or worse 1-year DFS (93.8% vs. 97.1%, HR = 3.73 [95% CI = 0.86-16.14], P = 0.059), compared with delay of < 10 days. Multivariable analyses showed RT delay of ≥ 6 days remained an independent adverse factor for both EBV DNA elevation and DFS. CONCLUSION: To ensure treatment efficacy for patients with nonmetastatic NPC, initiation of RT should not be delayed by more than 6 days; the effect of chemotherapy delay requires further investigation.

A Bibliometric Analysis of the Association Between Compassion Fatigue and Psychological Resilience From 2008 to 2021.

Aims: A negative association between the lower level of psychological resilience (PR) and increased risk of compassion fatigue (CF) and higher Coronavirus disease 2019 (COVID-19) stress has been revealed. However, bibliometric studies have not been performed to comprehensively investigate this topic. This study aimed to identify the status and trends in the CF and PR field from 2008 to 2021 and during the COVID-19 pandemic. Methods: We identified relevant literature from the Web of Science Core Collection® database using "resilience" and "compassion fatigue" on September 30, 2021. All search results were exported in plain text format for collaboration network analysis, reference-based co-citation analysis, analysis of journals, and keywords-based co-occurrence analysis, which were performed using Citespace® 5.8.R1. Results: A total of 388 publications were identified finally, and there has been an increasing trend in the annual number of publications with light fluctuations. The analysis of journals and keywords indicated that nurses and social workers are the main research targets, and their mental problems are the main research topics. The turnover intention of health care providers has been a research focus, particularly during the COVID-19. Conclusion: The results of the present study help us understand the status of the CF and PR field and its recent developments.

A Chinese host genetic study discovered IFNs and causality of laboratory traits on COVID-19 severity.

The COVID-19 pandemic has caused over 220 million infections and 4.5 million deaths worldwide. Current risk factor cannot fully explain the diversity in disease severity. Here, we present a comprehensive analysis of a broad range of patients' laboratory and clinical assessments to investigate the genetic contributions to COVID-19 severity. By performing GWAS analysis, we discovered several concrete associations for laboratory traits and used Mendelian randomization (MR) analysis to further investigate the causality of traits on disease severity. Two causal traits, WBC counts and cholesterol levels, were identified based on MR study, and their functional genes are located at genes MHC complex and ApoE, respectively. Our gene-based analysis and GSEA revealed four interferon pathways, including type I interferon receptor binding and SARS coronavirus and innate immunity. We hope that our work will contribute to studying the genetic mechanisms of disease and serve as a useful reference for COVID-19 diagnosis and treatment.

Risk factors analysis of COVID-19 patients with ARDS and prediction based on machine learning.

COVID-19 is a newly emerging infectious disease, which is generally susceptible to human beings and has caused huge losses to people's health. Acute respiratory distress syndrome (ARDS) is one of the common clinical manifestations of severe COVID-19 and it is also responsible for the current shortage of ventilators worldwide. This study aims to analyze the clinical characteristics of COVID-19 ARDS patients and establish a diagnostic system based on artificial intelligence (AI) method to predict the probability of ARDS in COVID-19 patients. We collected clinical data of 659 COVID-19 patients from 11 regions in China. The clinical characteristics of the ARDS group and no-ARDS group of COVID-19 patients were elaborately compared and both traditional machine learning algorithms and deep learning-based method were used to build the prediction models. Results indicated that the median age of ARDS patients was 56.5 years old, which was significantly older than those with non-ARDS by 7.5 years. Male and patients with BMI > 25 were more likely to develop ARDS. The clinical features of ARDS patients included cough (80.3%), polypnea (59.2%), lung consolidation (53.9%), secondary bacterial infection (30.3%), and comorbidities such as hypertension (48.7%). Abnormal biochemical indicators such as lymphocyte count, CK, NLR, AST, LDH, and CRP were all strongly related to the aggravation of ARDS. Furthermore, through various AI methods for modeling and prediction effect evaluation based on the above risk factors, decision tree achieved the best AUC, accuracy, sensitivity and specificity in identifying the mild patients who were easy to develop ARDS, which undoubtedly helped to deliver proper care and optimize use of limited resources.

Cross-cultural adaptation, validity, and reliability of the Chinese version of the Nurses' Perceptions of Disaster Core Competencies Scale (NPDCC).

BACKGROUND: In recent years, disasters occurred frequently all over the world, and the role of nurses in public health emergencies and disaster emergencies was highlighted under the background of the covid19 epidemic. However, there was a lack of education and evaluation. Our study aims to cross-cultural adapt the Nurses' Perceptions of Disaster Core Competencies Scale (NPDCC) and evaluate the reliability and validity of the Chinese version. METHODS: We translated the scale following the translation-integration-back translation-expert review procedure, adapted according to Chinese culture. We evaluated the reliability and validity of the scale, and a total sample of 911 nurse data from the Yangtze River Delta Regional Nursing Alliance Hospital was gathered. RESULTS: The Chinese version of NPDCC included 45 items, 5 factors (critical thinking skills, special diagnostic skills, general diagnostic skills, technical skills, and communication skills) were extracted from the analysis, which could explain the 68.289% of the total variance. The content validity index was 0.925. The Cronbach's α of the total NPDCC score was 0.978, and 0.884-0.945 for every factor. The split-half for the scale was 0.930, and every factor was 0.861-0.894. CONCLUSIONS: The Chinese version of NPDCC has excellent reliability and validity, and it is suitable to measure nurses' perceptions of disaster core competencies in China. The next step is to promote the application in a large scale.

Bioinformatics analysis of epitope-based vaccine design against the novel SARS-CoV-2.

BACKGROUND: An outbreak of infection caused by SARS-CoV-2 recently has brought a great challenge to public health. Rapid identification of immune epitopes would be an efficient way to screen the candidates for vaccine development at the time of pandemic. This study aimed to predict the protective epitopes with bioinformatics methods and resources for vaccine development. METHODS: The genome sequence and protein sequences of SARS-CoV-2 were retrieved from the National Center for Biotechnology Information (NCBI) database. ABCpred and BepiPred servers were utilized for sequential B-cell epitope analysis. Discontinuous B-cell epitopes were predicted via DiscoTope 2.0 program. IEDB server was utilized for HLA-1 and HLA-2 binding peptides computation. Surface accessibility, antigenicity, and other important features of forecasted epitopes were characterized for immunogen potential evaluation. RESULTS: A total of 63 sequential B-cell epitopes on spike protein were predicted and 4 peptides (Spike315-324, Spike333-338, Spike648-663, Spike1064-1079) exhibited high antigenicity score and good surface accessibility. Ten residues within spike protein (Gly496, Glu498, Pro499, Thr500, Leu1141, Gln1142, Pro1143, Glu1144, Leu1145, Asp1146) are forecasted as components of discontinuous B-cell epitopes. The bioinformatics analysis of HLA binding peptides within nucleocapsid protein produced 81 and 64 peptides being able to bind MHC class I and MHC class II molecules respectively. The peptides (Nucleocapsid66-75, Nucleocapsid104-112) were predicted to bind a wide spectrum of both HLA-1 and HLA-2 molecules. CONCLUSIONS: B-cell epitopes on spike protein and T-cell epitopes within nucleocapsid protein were identified and recommended for developing a protective vaccine against SARS-CoV-2.

A prediction model based on machine learning for diagnosing the early COVID-19 patients (preprint)

With the dramatically fast spread of COVID-9, real-time reverse transcription polymerase chain reaction (RT-PCR) test has become the gold standard method for confirmation of COVID-19 infection. However, RT-PCR tests are complicated in operation andIt usually takes 5-6 hours or even longer to get the result. Additionally, due to the low virus loads in early COVID-19 patients, RT-PCR tests display false negative results in a number of cases. Analyzing complex medical datasets based on machine learning provides health care workers excellent opportunities for developing a simple and efficient COVID-19 diagnostic system. This paper aims at extracting risk factors from clinical data of early COVID-19 infected patients and utilizing four types of traditional machine learning approaches including logistic regression(LR), support vector machine(SVM), decision tree(DT), random forest(RF) and a deep learning-based method for diagnosis of early COVID-19. The results show that the LR predictive model presents a higher specificity rate of 0.95, an area under the receiver operating curve (AUC) of 0.971 and an improved sensitivity rate of 0.82, which makes it optimal for the screening of early COVID-19 infection. We also perform the verification for generality of the best model (LR predictive model) among Zhejiang population, and analyze the contribution of the factors to the predictive models. Our manuscript describes and highlights the ability of machine learning methods for improving the accuracy and timeliness of early COVID-19 infection diagnosis. The higher AUC of our LR-base predictive model makes it a more conducive method for assisting COVID-19 diagnosis. The optimal model has been encapsulated as a mobile application (APP) and implemented in some hospitals in Zhejiang Province.

Avoiding health worker infection and containing the coronavirus disease 2019 pandemic: Perspectives from the frontline in Wuhan.

Coronavirus disease 2019 (COVID-19), which is caused by severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), has caused great public concern worldwide due to its high rates of infectivity and pathogenicity. The Chinese government responded in a timely manner, alleviated the dilemma, achieved a huge victory and lockdown has now been lifted in Wuhan. However, the outbreak has occurred in more than 200 other countries. Globally, as of 9:56 am CEST on 19 May 2020, there have been 4,696,849 confirmed cases of COVID-19, including 315,131 deaths, reported to Word Health Organization (WHO). The spread of COVID-19 overwhelmed the healthcare systems of many countries and even crashed the fragile healthcare systems of some. Although the situation in each country is different, health workers play a critical role in the fight against COVID-19. In this review, we highlight the status of health worker infections in China and other countries, especially the causes of infection in China and the standardised protocol to protect health workers from the perspective of an anaesthesiologist, in the hope of providing references to reduce medical infections and contain the COVID-19 epidemic.