Development and validation of AI models using LR and LightGBM for predicting distant metastasis in breast cancer: a dual-center study.

Objective: This study aims to develop an artificial intelligence model utilizing clinical blood markers, ultrasound data, and breast biopsy pathological information to predict the distant metastasis in breast cancer patients. Methods: Data from two medical centers were utilized, Clinical blood markers, ultrasound data, and breast biopsy pathological information were separately extracted and selected. Feature dimensionality reduction was performed using Spearman correlation and LASSO regression. Predictive models were constructed using LR and LightGBM machine learning algorithms and validated on internal and external validation sets. Feature correlation analysis was conducted for both models. Results: The LR model achieved AUC values of 0.892, 0.816, and 0.817 for the training, internal validation, and external validation cohorts, respectively. The LightGBM model achieved AUC values of 0.971, 0.861, and 0.890 for the same cohorts, respectively. Clinical decision curve analysis showed a superior net benefit of the LightGBM model over the LR model in predicting distant metastasis in breast cancer. Key features identified included creatine kinase isoenzyme (CK-MB) and alpha-hydroxybutyrate dehydrogenase. Conclusion: This study developed an artificial intelligence model using clinical blood markers, ultrasound data, and pathological information to identify distant metastasis in breast cancer patients. The LightGBM model demonstrated superior predictive accuracy and clinical applicability, suggesting it as a promising tool for early diagnosis of distant metastasis in breast cancer.

Electroacupuncture prevents astrocyte atrophy to alleviate depression.

Astrocyte atrophy is the main histopathological hallmark of major depressive disorder (MDD) in humans and in animal models of depression. Here we show that electroacupuncture prevents astrocyte atrophy in the prefrontal cortex and alleviates depressive-like behaviour in mice subjected to chronic unpredictable mild stress (CUMS). Treatment of mice with CUMS induced depressive-like phenotypes as confirmed by sucrose preference test, tail suspension test, and forced swimming test. These behavioural changes were paralleled with morphological atrophy of astrocytes in the prefrontal cortex, revealed by analysis of 3D reconstructions of confocal Z-stack images of mCherry expressing astrocytes. This morphological atrophy was accompanied by a decrease in the expression of cytoskeletal linker Ezrin, associated with formation of astrocytic leaflets, which form astroglial synaptic cradle. Electroacupuncture at the acupoint ST36, as well as treatment with anti-depressant fluoxetine, prevented depressive-like behaviours, astrocytic atrophy, and down-regulation of astrocytic ezrin. In conclusion, our data further strengthen the notion of a primary role of astrocytic atrophy in depression and reveal astrocytes as cellular target for electroacupuncture in treatment of depressive disorders.