Application of machine learning models in predicting insomnia severity: an integrative approach with constitution of traditional Chinese medicine.

Objective: This study sought to explore the utility of machine learning models in predicting insomnia severity based on Traditional Chinese Medicine (TCM) constitution classifications, with an aim to discuss the potential applications of such models in the treatment and prevention of insomnia. Methods: We analyzed a dataset of 165 insomnia patients from the Shanghai Minhang District Integrated Traditional Chinese and Western Medicine Hospital. TCM constitution was assessed using a standardized Constitution in Chinese Medicine (CCM) scale. Sleep quality, or insomnia severity, was evaluated using the Spiegel Sleep Questionnaire (SSQ). Machine learning models, including Random Forest Classifier (RFC), Support Vector Classifier (SVC), and K-Nearest Neighbors (KNN), were utilized. These models were optimized using Grid Search algorithm and were trained and tested on stratified patient data, with the TCM constitution classifications serving as primary predictors. Results: The RFC outperformed others, achieving a weighted average accuracy, precision, recall, and F1-score of 0.91, 0.94, 0.92, and 0.92 respectively, it also effectively classified the severity of insomnia with high area under receiver operating characteristic curve (AUC-ROC) values. Feature importance analysis demonstrated the Damp-heat constitution as the most influential predictor, followed by Yang-deficiency, Qi-depression, Qi-deficiency, and Blood-stasis constitutions. Conclusion: The results demonstrate the potent utility of machine learning, specifically RFC, coupled with TCM constitution classifications in predicting insomnia severity. Notably, the constitution classifications such as Damp-heat and Yang-deficiency emerged as crucial determinants, emphasizing its potential in guiding targeted insomnia treatments. This approach enables the development of more personalized and efficient interventions, thereby enhancing patient outcomes.

Reciprocal Regulation Between O-GlcNAcylation and ß-Catenin Facilitates Cell Viability and Inhibits Apoptosis in Liver Cancer.

Abnormal expression of O-Linked ß-N-acetylglucosamine (O-GlcNAc) and ß-catenin is a general feature of cancer and contributes to transformed phenotypes. In this study, we identified the interaction between O-GlcNAc and ß-catenin, and explored their effects on the progression of liver cancer. Our results demonstrated that upregulation of O-GlcNAc was induced by high glucose, whereas the application of PuGNAc and GlcNAc increased ß-catenin protein expression levels, as well as the protein's stability and nuclear accumulation in the liver cancer cell lines HEP-G2 and HuH-7. In addition, overexpression of ß-catenin could increase O-GlcNAc expression levels through upregulation of uridine 5'-diphosphate (UDP)-N-acetylglucosamine pyrophosphorylase 1 (UAP1) protein expression, protein stability, and inhibition of its ubiquitination. Moreover, the O-GlcNAcylation of ß-catenin promoted the proliferation, colony formation, and repressed the induction of apoptosis in HEP-G2 and HuH-7 cells. Knockdown of ß-catenin reduced cell proliferation, colony formation, and tumorigenesis, and promoted cell apoptosis through the downregulation of UAP1 expression. In conclusion, this study revealed that the reciprocal regulation between O-GlcNAcylation and ß-catenin facilitated the proliferation of liver cancer.