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.

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.