Shandong University of Traditional Chinese Medicine

Although many previous studies have highlighted the advances in prediction models, instruments for pathological and histological diagnosis and treatment, as well as individualized treatment modalities in mental disorders, these previous syntheses usually study the research outcomes separately and ignore the holistic integration of research regarding artificial intelligence technological approaches, data sets used and applications in mental health research. We used the BioBERT pretrained language model to systematically extract relevant information and develop an extensive knowledge graph that includes 3158 entities connected with 3248 different relationships. Our knowledge graph delineates essential artificial intelligence technological frameworks and explicitly maps out the relationships linking artificial intelligence methods, mental disorders, and diverse data modalities. The synthesis, centered on the analytical axis of "method-disease-data," highlights key research areas where artificial intelligence and neuropsychiatry meet. Specifically, it focuses on key applications in early detection, improved accuracy of diagnosis, and individualized therapeutic interventions. In addition, we summarized the applications derived from basic research findings that extend to ongoing clinical trials, revealing the path toward future clinical application in psychiatric work. Importantly, the research paid special attention to the application of artificial intelligence in identifying key brain regions and neural circuits, providing important clues for elucidating the neural mechanisms of mental disorders and developing targeted interventions. Although artificial intelligence presents great opportunities, there are also significant challenges, including imbalanced data sets, ethical issues, and clinical concerns about trustworthiness and transparency. Strategies to address these challenges are proposed, and a perspective on emerging methods enabled by artificial intelligence is provided, which are expected to greatly change the management and treatment of the future.

Atherosclerosis (AS) severely threatens global health, while current therapies exhibit limitations. Recognized as a 'superior-grade' herb in the Shennong Ben Cao Jing, Salvia miltiorrhiza Bunge (Danshen) has been shown in modern studies to protect against cardiovascular diseases. The aim of this study was to investigate the therapeutic potential and protective mechanism of pharmacological components of Salvia miltiorrhiza against AS. Relevant animal studies were collected from 8 databases, namely, PubMed, Web of Science, Embase, Cochrane Library, CNKI, Wanfang Data, VIP, and SinoMed. Risk of bias of the included studies was evaluated using the SYRCLE's tool. Statistical analysis was performed using R 4.2.0 and Python 3.14.2 software. Machine learning model was trained to predict optimal intervention parameters and was subsequently validated. A total of 64 studies were included. The pharmacological components of Salvia miltiorrhiza ameliorated atherosclerotic plaque formation and stability, and modulated various biomarkers, including lipid profiles, inflammatory cytokines, oxidative stress indicators, endothelial function markers, as well as matrix metalloproteinases. Machine learning identified an optimal Tanshinone IIA regimen against AS, which was defined as a single dose of 33.18mg/kg dose over 84 days and demonstrated predictive robustness in validation. The pharmacological components of Salvia miltiorrhiza attenuate AS by regulating lipid metabolism, anti-inflammatory and antioxidant actions, improving endothelial function, modulating of vascular smooth muscle cells, remodeling extracellular matrix, and regulating programmed cell death. These findings provide translational insights that pave the way for subsequent preclinical and early-clinical studies, pending systematic validation through more rigorous research.