Fusing Diverse Decision Rules in 3D-Radiomics for Assisting Diagnosis of Lung Adenocarcinoma.

This study aimed to develop an interpretable diagnostic model for subtyping of pulmonary adenocarcinoma, including minimally invasive adenocarcinoma (MIA), adenocarcinoma in situ (AIS), and invasive adenocarcinoma (IAC), by integrating 3D-radiomic features and clinical data. Data from multiple hospitals were collected, and 10 key features were selected from 1600 3D radiomic signatures and 11 radiological features. Diverse decision rules were extracted using ensemble learning methods (gradient boosting, random forest, and AdaBoost), fused, ranked, and selected via RuleFit and SHAP to construct a rule-based diagnostic model.

Brain optimization with additional study time: potential brain differences between high- and low-performance college students.

This study investigates potential differences in brain function among high-, average-, and low-performance college students using electroencephalography (EEG). We hypothesize that the increased academic engagement of high-performance students will lead to discernible EEG variations due to the brain's structural plasticity. 61 third-year college students from identical majors were divided into high-performance ( = 20), average-performance ( = 21), and low-performance ( = 20) groups based on their academic achievements.

Urban fine particulate air pollution exposure promotes atherosclerosis in apolipoprotein E-deficient mice by activating perivascular adipose tissue inflammation via the Wnt5a/Ror2 signaling pathway.

Urban fine particulate matter (PM) is a deleterious risk factor in the ambient air and is recognized to exacerbate atherosclerosis. Perivascular adipose tissue (PVAT) secretes a large number of inflammatory cytokines and plays a crucial role in the pathogenic microenvironment of atherogenesis. However, there is a lack of knowledge about the role of PVAT inflammation in the genesis of PM-related atherosclerosis.