Aeromonas veronii is a major bacterial pathogen in freshwater aquaculture, yet rapid species-level quantification remains challenging within the genetically complex genus Aeromonas. We developed a singleplex hydrolysis-probe (TaqMan) quantitative PCR (qPCR) assay targeting an A. veronii-discriminatory region of the aerolysin gene (aerA) and validated it according to MIQE recommendations. Plasmid standards gave a linear range of 2 to 2 × 106 copies per reaction (R2 = 0.9962) with 100.5% amplification efficiency. The endpoint limit of detection was 2 copies per reaction, and 20 copies per reaction was set as the practical reporting limit based on reproducible detection and low intra-/inter-assay variation. Analytical specificity was evaluated with genomic DNA from an 18-strain panel, with reproducible amplification observed only for A. veronii. The assay was further tested in 55 fish-tissue and 11 aquaculture-water DNA extracts. NH8B-1D2 sample-process monitoring was used for matrix-level recovery correction, and tissue and water extraction blanks were undetermined. The aerA target was detected in all tested gill, stomach/intestine, spleen, kidney/head kidney, pond-water filter and Xiamen seawater filter extracts, and in 10/11 liver extracts. Median NH8B-corrected loads were highest in gill among tissues and higher in pond-water than Xiamen seawater filters. A separate Vibrio harveyi inhibition-check assay indicated no obvious amplification-stage inhibition. This assay supports rapid quantification of aerA-positive A. veronii in fish and aquaculture-water matrices.
Yue Wu, Bin Chen, Hong Chen et al.· Journal of Microbiological M...· 0 citations
Background Patients with peripheral artery disease (PAD) face high postoperative mortality risks, necessitating precise risk stratification. While machine learning offers superior performance, its black-box nature limits clinical utility, and the prognostic value of the neutrophil-to-lymphocyte ratio (NLR) remains controversial. Methods A total of 610 surgically managed PAD patients were enrolled (median follow-up: 4 years) and randomly split into training (70%) and test (30%) sets. Six machine learning algorithms were constructed and optimized. Model performance was evaluated using area under the receiver operating characteristic curve (AUC) and decision curve analysis (DCA). The sHapley additive exPlanations (SHAP) were employed for model interpretation and visualizing nonlinear relationships. Results The random forest model achieved optimal performance (test set AUC = 0.814) with significant clinical net benefit. SHAP analysis identified age, prothrombin activity, and Rutherford classification as top predictors. Notably, while multivariate Cox regression failed to identify NLR as a linear predictor, SHAP dependence plots revealed a distinct nonlinear pattern: risk contribution increased sharply at low standardized NLR values before plateauing. Conclusion We established an interpretable random forest model for predicting postoperative mortality in PAD. By integrating SHAP analysis, this study validates the nonlinear prognostic significance of NLR and demonstrates how explainable ML can complement traditional statistics for individualized risk assessment.
Yi-Fei Li, Qiang Zhang, Wenxin Zhao et al.· Frontiers in Cardiovascular...· 0 citations
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