Journal of virological methods最新文献

Background: Western blot (WB) is the gold standard for herpes simplex virus (HSV-1/HSV-2) serology but requires manual interpretation by trained laboratory personnel, which is time-consuming and variable. This study presents BlotDx, a deep learning tool to assist in HSV WB interpretation to improve efficiency and diagnostic consistency.

Methods: BlotDx uses a two-stage approach: (1) instance segmentation or object detection to identify blots from input images, and (2) classification models to determine positive or negative serostatus for HSV-1 and HSV-2, excluding indeterminate results. We developed three classifiers that differ in how information flows between stages. The primary dataset consisted of 926 blot pairs derived from samples collected between 2016 and 2017 and photographed in 2018; and a second institutional validation dataset containing 185 blot pairs from samples collected between 2019 and 2024 and photographed in a different laboratory space in 2025.

Results: Evaluated against the ground truth of expert human review by three independent technicians, BlotDx demonstrated high diagnostic accuracy by 5-fold cross validation in both the primary dataset (98.8% for HSV-1, 95% confidence interval (CI): 97.9%-99.3%; and 98.9% for HSV-2, 95% CI: 98.0%-99.4%), and in the institutional validation dataset (97.3% for HSV-1, 95% CI: 93.8%-98.8%; and 96.2% for HSV-2, 95% CI: 92.4%-98.1%).

Conclusions: This study highlights the utility of AI as a diagnostic assistant for image-based assays like HSV Western blots, with potential applications in other diseases. The proposed two-stage approach, combined with modern deep learning techniques is scalable and offers a step towards transforming traditional diagnostic workflows by reducing costs and increasing efficiency.

Purpose: This scoping review summarizes the proposed mechanisms by which oseltamivir may improve clinical outcomes in COVID-19. Although SARS-CoV-2 lacks neuraminidase, several studies have reported reduced fever duration, shorter hospitalization, and faster viral clearance with oseltamivir administration. This review integrates current evidence regarding antiviral, immunomodulatory, and autonomic-nervous-system-related mechanisms.

Methods: A structured literature search was conducted in PubMed, Scopus, and Google Scholar. Studies addressing oseltamivir's antiviral activity, neutrophil modulation, antipyretic effects, or influence on sympathetic nervous system activity in COVID-19 were included.

Results: Oseltamivir may exert therapeutic effects through inhibition of SARS-CoV-2 proliferation, reduction of neutrophil overactivation, attenuation of sympathetic nervous system hyperactivity, and modulation of fever pathways.

Conclusion: This scoping review identifies multiple mechanisms through which oseltamivir may influence COVID-19 pathophysiology. Although evidence remains heterogeneous, findings suggest that oseltamivir may have broader biological effects beyond neuraminidase inhibition. Further clinical studies are needed to clarify its therapeutic role, optimal timing, and potential benefits in unvaccinated or high-risk populations.

Porcine deltacoronavirus (PDCoV) is a porcine intestinal coronavirus that causes severe diarrhea in piglets and is extremely harmful to the pig industry. Rapid and accurate detection of antibody levels is necessary for effective prevention of the disease. We previously demonstrated that PDCoV and porcine epidemic diarrhea (PEDV) N protein have serum cross-reactivity. Therefore, in this study, we first constructed and expressed five recombinant truncated PDCoVN proteins ( N1-N5). Through the reactivity of these proteins to porcine PDCoV positive serum and porcine PEDV positive serum and the established ELISA method, the truncated proteins with less cross-reaction between PDCoV and PEDV serum were selected, and these recombinant truncated N proteins and PDCoVS1 proteins were used to establish an indirect enzyme-linked immunosorbent assay ( ELISA) for IgA detection. The ELISAs with S1 protein or truncated protein N3（aa1-122） as the coating antigen had the highest sensitivity and specificity(S1-ELISA: sensitivity 75%, specificity 86%; N3-ELISA: sensitivity 75%, specificity 86%). The two methods showed > 90% consistency in detecting anti-PDCoV-specific IgA levels in a total of 63 colostrum and serum samples. Although S1-ELISA and N3-ELISA have the same sensitivity and specificity, the N protein is more stable than the S protein in coronavirus. Our ELISA therefore can be applied to detect anti-PDCoV specific IgA after natural infection or vaccination. Our findings provide an important basis for antigen selection in the future establishment of a specific clinical rapid diagnosis method for PDCoV.