Combined Diagnosis of SARS-CoV-2: Rapid Antigen Detection as an Adjunct to Nucleic Acid Detection.

Coronavirus disease 2019 is a serious threat to human life, and early diagnosis and screening can help control the COVID-19 pandemic. The high sensitivity of reverse transcriptase-polymerase chain reaction (RT-PCR) assay is the gold standard for the diagnosis of COVID-19, but there are still some false-negative results. Rapid antigen detection (RAD) is recommended by the World Health Organization (WHO) as a screening method for COVID-19. This review analyzed the characteristics of RDT and found that although the overall sensitivity of RAD was not as high as that of RT-PCR, but RAD was more sensitive in COVID-19 patients within 5 days of the onset of symptoms and in COVID-19 patients with Ct ≤ 25. Therefore, RAD can be used as an adjunct to RT-PCR for screening patients with early COVID-19. Finally, this review provides a combined diagnostic protocol for RAD and nucleic acid testing with the aim of providing a feasible approach for COVID-19 screening.

Comparison of the performance of two real-time fluorescent quantitative PCR kits for the detection of SARS-CoV-2 nucleic acid: a study based on large real clinical samples.

BACKGROUND: The global pandemic of coronavirus disease 2019 (COVID-19) has led to the development of multiple detection kits by national manufacturers for severe acute respiratory syndrome coronavirus 2 viral nucleic acid testing. The purpose of this study is to evaluate the performance of different kits (i.e., Maccura kit and Sansure kit) in real clinical work using clinical samples, which will help with the optimization of the test kits. METHOD: During the past three months (March-May 2022), 1399 pharyngeal swabs from suspected COVID-19 patients have been initially screened using the Maccura kit in Jilin, China, and the test results were verified using the Sansure kit. The cycle threshold (Ct) values generated by the two kits were compared at different viral load levels. Correlation and consistency of the Ct values were investigated using Spearman correlation, Deming regression, and Bland-Altman plots. The cut-off Ct values of the Maccura kit were recalculated by referencing the result of the Sansure kit as a standard. Furthermore, another 163 pharyngeal swabs from suspected COVID-19 patients were collected to verify the new cut-off values. RESULTS: As a result of the Maccura kit testing, 1192 positive cases and 207 suspected COVID-19 cases were verified. After re-examination by the Sansure kit, 1118 positive cases were confirmed. The difference between the Ct values provided by the two kits was statistically significant, except for the N gene at high viral load. The Ct values obtained from the two kits presented a linear positive correlation. The Maccura kit used new cut-off Ct values of 35.00 (ORF1ab gene) and 35.07 (N gene). Based on that, the validation pass rate for the new cut-off Ct values was 91.41%. CONCLUSION: Since the Maccura kit is found to have false positives in actual clinical work, recalculation of the cut-off values can reduce this occurrence. In order to improve the accuracy of the testing, laboratories should use two kits for COVID-19 testing, and the adjusting and optimizing of the kits for their situation are needed.

A complete blood count-based multivariate model for predicting the recovery of patients with moderate COVID-19: a retrospective study.

Many resource-limited countries need an efficient and convenient method to assess disease progression in patients with coronavirus disease 2019 (COVID-19). This study developed and validated a complete blood count-based multivariate model for predicting the recovery of patients with moderate COVID-19. We collected the clinical data and laboratory test results of 86 patients with moderate COVID-19. These data were categorized into two subgroups depending on the laboratory test time. Univariate logistic regression and covariance diagnosis were used to screen for independent factors, and multifactorial logistic regression was used for model building. Data from 38 patients at another hospital were collected for external verification of the model. Basophils (OR 6.372; 95% CI 3.284-12.363), mean corpuscular volume (OR 1.244; 95% CI 1.088-1.422), red blood cell distribution width (OR 2.585; 95% CI 1.261-5.297), and platelet distribution width (OR 1.559; 95% CI 1.154-2.108) could be combined to predict recovery of patients with moderate COVID-19. The ROC curve showed that the model has good discrimination. The calibration curve showed that the model was well-fitted. The DCA showed that the model is clinically useful. Small increases in the above parameters within the normal range suggest an improvement in patients with moderate COVID-19.

Potential Predictive Value of miR-125b-5p, miR-155-5p and Their Target Genes in the Course of COVID-19.

Purpose: This study aimed to provide new biomarkers for predicting the disease course of COVID-19 by analyzing the dynamic changes of microRNA (miRNA) and its target gene expression in the serum of COVID-19 patients at different stages. Methods: Serum samples were collected from all COVID-19 patients at three time points: the acute stage, the turn-negative stage, and the recovery stage. The expression level of miRNA and the target mRNA was measured by Quantitative Real-Time Polymerase Chain Reaction (RT-qPCR). The classification tree model was established to predict the disease course, and the prediction efficiency of independent variables in the model was analyzed using the receiver operating characteristic (ROC) curve. Results: The expression of miR-125b-5p and miR-155-5p was significantly up-regulated in the acute stage and gradually decreased in the turn-negative and recovery stages. The expression of the target genes CDH5, STAT3, and TRIM32 gradually down-regulated in the acute, turn-negative, and recovery stages. MiR-125b-5p, miR-155-5p, STAT3, and TRIM32 constituted a classification tree model with 100% accuracy of prediction and AUC >0.7 for identification and prediction in all stages. Conclusion: MiR-125b-5p, miR-155-5p, STAT3, and TRIM32 could be useful biomarkers to predict the time nodes of the acute, turn-negative, and recovery stages of COVID-19.

Early changes in laboratory tests predict liver function damage in patients with moderate coronavirus disease 2019: a retrospective multicenter study.

BACKGROUND: Most patients with coronavirus disease 2019 demonstrate liver function damage. In this study, the laboratory test data of patients with moderate coronavirus disease 2019 were used to establish and evaluate an early prediction model to assess the risk of liver function damage. METHODS: Clinical data and the first laboratory examination results of 101 patients with moderate coronavirus disease 2019 were collected from four hospitals' electronic medical record systems in Jilin Province, China. Data were randomly divided into training and validation sets. A logistic regression analysis was used to determine the independent factors related to liver function damage in patients in the training set to establish a prediction model. Model discrimination, calibration, and clinical usefulness were evaluated in the training and validation sets. RESULTS: The logistic regression analysis showed that plateletcrit, retinol-binding protein, and carbon dioxide combining power could predict liver function damage (P < 0.05 for all). The receiver operating characteristic curve showed high model discrimination (training set area under the curve: 0.899, validation set area under the curve: 0.800; P < 0.05). The calibration curve showed a good fit (training set: P = 0.59, validation set: P = 0.19; P > 0.05). A decision curve analysis confirmed the clinical usefulness of this model. CONCLUSIONS: In this study, the combined model assesses liver function damage in patients with moderate coronavirus disease 2019 performed well. Thus, it may be helpful as a reference for clinical differentiation of liver function damage. Trial registration retrospectively registered.

Mechanism of HanShi ZuFei formula in treatment of COVID-19 based on network pharmacology and molecular docking technology

Objective: To explore the mechanism of Hanshi Zufei Formula (HSZFF) treating coronavirus disease 2019 (COVID-19) by the data mining analysis of network pharmacology and the molecular docking.

Understanding factors influencing the length of hospital stay among non-severe COVID-19 patients: A retrospective cohort study in a Fangcang shelter hospital.

As a novel concept of responding to disease epidemics, Fangcang shelter hospitals were deployed to expand the health system's capacity and provide medical services for non-severe COVID-19 patients during the outbreak in Wuhan. To give insights on patient management within Fangcang hospitals, we conducted a retrospective analysis to: 1) describe the characteristics of the patients admitted to Fangcang hospitals and 2) explore risk factors for longer length of stay (LOS). We enrolled 136 confirmed COVID-19 patients, including asymptomatic patients and those with mild symptoms, who were hospitalized in the Wuti Fangcang Hospital. 58 patients completed the treatment and discharged before 1 March 2020. After describing patients' demographic and clinical characteristics, exposure history, treatment received and time course of the disease, we conducted linear regression analysis to identify factors influencing LOS. We found that patients having fever before admission were hospitalized 3.5 days (95%CI 1.39 to 5.63, p = 0.002) longer than those without fever and that patients having bilateral pneumonia were hospitalized 3.4 days (95%CI 0.49 to 6.25, p = 0.023) longer than those with normal CT scan results. We also found weak evidence suggesting that patients with diabetes were hospitalized 3.2 days longer than those without diabetes (95%CI -0.2 to 6.56, p = 0.065). However, we observed no significant differences in LOS between symptomatic and asymptomatic patients and between patients who received treatment and those without treatment. Longer duration of hospitalization among non-severe COVID-19 patients is associated with having fever, bilateral pneumonia on CT scan and diabetes. However, being asymptomatic and using supportive medications at the early stage of infection do not have significant influences on LOS. Our study is a single-centered study with relatively small sample size. The findings provide evidence for predicting hospital bed demand in a novel response scenario and may help decision-makers in preparing for ramping up the health system capacity.