Journal of Clinical Virology最新文献

Background: Continuous viral evolution is likely to cause a loss of performance or failure of existing diagnostic assays. Here, we delineate the adaptation and validation of an operational laboratory developed (LDT) multiplex RT-qPCR for simultaneous detection of SARS-CoV-2, influenza A/B (FluA/B) and respiratory syncytial virus (RSV) on a high-throughput, fully automated platform. Furthermore, we explore the integration of an alternative SARS-CoV-2 target to enhance assay robustness METHODS: An adaption of the operational assay (PMID:38820916) was performed and a novel SARS-CoV-2 target (macrodomain, Mac1) was implemented. Analytical performance of the adapted assay was evaluated using digital-PCR based standards or international reference material and clinical performance was assessed on clinical samples with a CE-IVD comparator assay.

Results: Analytical sensitivity (lower limit of detection (LoD)) was 70.9 IU/ml for SARS-CoV-2, and 112-474, 919 and 1720 cp/ml for influenza A, influenza B and RSV, respectively, with linear ranges of 26.3-36.4 ct (SARS-CoV-2), 26.8-37.7 ct (FluA), 28.5-37.9 (FluB) and 25.6-38.2 (RSV). Clinical performance evaluation confirmed improved performance (e.g. FluA/B detection -1.6/-4.81 ct) and comparable performance to the CE-IVD assay (excellent correlation of the SARS-CoV-2 assays, more effective detection of influenza B). Overall, positive/negative agreement was 100 %/93 % (SARS-CoV-2), and 100 %/100 % (FluA/B, RSV).

Conclusion: The adapted LDT assay as a focused syndromic assay provides reliable detection of major respiratory viruses on a high-throughput platform. The strategic targeting of conserved genomic regions ensures diagnostic resilience, while automated integration facilitates scalable laboratory operations. This approach facilitates robust pathogen surveillance and expedites clinical decision-making during periods of co-circulation and epidemic surge.

Background: Complete virological response (CVR) is the primary goal of nucleos(t)ide analogues (NAs) therapy for chronic hepatitis B (CHB) patients. The objective of this study was to develop and validate a machine learning (ML) model to predict CVR in CHB patients receiving first-line NAs.

Methods: We retrospectively analyzed 2394 CHB patients treated with first-line NAs, randomly divided into a training set and an internal validation set at a 7:3 ratio. Key predictors were identified through univariate analysis, followed by Lasso, SVM-RFE, and Boruta feature selection algorithms. Seven machine learning models were developed and compared, interpreting results with SHapley Additive exPlanations (SHAP) analysis.

Results: A total of 2394 CHB patients were included, among whom 1597 (66.7 %) achieved CVR. Baseline HBV DNA, HBeAg, DBIL, Age, Cirrhosis, ALT, PLT, FIB4, and HBsAg were identified as significant influencing factors. Among the seven ML methods, the XGBoost model demonstrated the best performance with an AUC of 0.864 in the training set and 0.823 in the validation set. SHAP analysis identified baseline HBV DNA and HBeAg status as the top two predictors. This web-based calculator is designed to help predict the probability of achieving CVR at 48 weeks in CHB patients receiving first-line NAs treatment (http://yuanxq.shinyapps.io/CVR-prediction-app).

Conclusion: We developed and validated an interpretable machine learning model to predict CVR at 48 weeks in CHB patients treated with first-line NAs.