CT-based radiomics of machine-learning to screen high-risk individuals with kidney stones.

Screening high-risk populations is crucial for the prevention and treatment of kidney stones. Here, we employed radiomics to screen high-risk patients for kidney stones. A total of 513 independent kidneys from our hospital between 2020 and 2022 were randomly allocated to training and validation sets at a 7:3 ratio. Radiomic features were extracted using 3Dslicer software. The least absolute shrinkage and selection operator (LASSO) method was used to select radiomic features from the 107 extracted features, and logistic regression, decision tree, AdaBoost, and support vector machine (SVM) models were subsequently used to construct radiomic feature prediction models. Among these, the logistic regression algorithm demonstrated the best predictive performance and stability. The area under the curve (AUC) of the logistic regression model based on radiomic features was 0.858 in the training cohort and 0.806 in the validation cohort. Furthermore, univariate and multivariate logistic regression analyses were performed to identify the independent risk factors for kidney stones, which were gender and body mass index (BMI). Combining these independent risk factors improved the predictive performance of the model, with AUC values of 0.860 in the training cohort and 0.814 in the validation cohort. Clinical decision curve analysis (DCA) indicated that the radiomic model provided clinical benefit when the probability ranged from 0.2 to 1.0. The radiomic model has a good ability to screen high-risk patients with kidney stones, facilitating early intervention in kidney stone cases and improving patient prognosis.

Peripheral blood indicators and COVID-19: an observational and bidirectional Mendelian randomization study.

Blood is critical for health, supporting key functions like immunity and oxygen transport. While studies have found links between common blood clinical indicators and COVID-19, they cannot provide causal inference due to residual confounding and reverse causality. To identify indicators affecting COVID-19, we analyzed clinical data (n = 2,293, aged 18-65 years) from Guangzhou Medical University's first affiliated hospital (2022-present), identifying 34 significant indicators differentiating COVID-19 patients from healthy controls. Utilizing bidirectional Mendelian randomization analyses, integrating data from over 2.46 million participants from various large-scale studies, we established causal links for six blood indicators with COVID-19 risk, five of which is consistent with our observational findings. Specifically, elevated Troponin I and Platelet Distribution Width levels are linked with increased COVID-19 susceptibility, whereas higher Hematocrit, Hemoglobin, and Neutrophil counts confer a protective effect. Reverse MR analysis confirmed four blood biomarkers influenced by COVID-19, aligning with our observational data for three of them. Notably, COVID-19 exhibited a positive causal relationship with Troponin I (Tnl) and Serum Amyloid Protein A, while a negative association was observed with Plateletcrit. These findings may help identify high-risk individuals and provide further direction on the management of COVID-19.