Evaluation of reducing the south and reinforcing the north method on postmenopausal atherosclerosis disease based on 4 diagnostic objectifications: A prospective observational study.

Reducing the south and reinforcing the north method (RSRN) has a positive effect on atherosclerosis disease. However, there is a lack of objective standards based on the quantification of 4 diagnostic methods in evaluating the improvement or effectiveness of the treatment. This study aimed to explore the quantitative evaluation of the therapeutic effect of RSRN on postmenopausal atherosclerosis based on the 4 diagnostic methods. The observational prospective cohort study was conducted at Longhua hospital Shanghai University of traditional Chinese medicine. According to the inclusion criteria, 96 patients (disease group) and 38 healthy cases (control group) were selected, the pulse parameters were compared between the 2 groups to demonstrate the reliability and success of the disease model. Then 4 diagnostic information before and after RSRN treatment were collected and statistical analyzed by 1-way analysis of variance (ANOVA) (with Bonferroni correction). Furthermore, social network analysis was used to analyze the changes of symptoms, tongue, pulse, and complexion characteristics before and after treatment. There was a significant difference in pulse parameters between the disease group and the control group. The pulse parameters t1, h3, h3/h1, h4/h1, S, As, and w values in disease group were higher than those in control group, while the h5, h5/h1, and Ad values were lower than those in control group (P < .05). After the treatment of RSRN, the clinical symptoms of patients were greatly improved. The facial color indexes L, a, b values of the disease group at week 6 were different from those at week 0 (P < .05). The overall brightness and chroma of the patient's facial color were significantly improved. The patients had virtual string pulse at week 0, and mainly string I and string II at week 7. The pulse parameters t1, t5, w, w/t, h1, h5, h3/h1, and h5/h1 values at week 7 were different from those at weeks 0, 1, 2 (P < .05); the tongue image was mainly red and crimson, peeling or greasy fur at week 0, while at weeks 6, 7, mainly light red, or thin white tongue. The RSRN method can regulate the complexion, tongue and pulse condition, clinical symptoms of postmenopausal atherosclerosis.

Mechanism of Tonifying Spleen-lung Recipe in the Treatment of Chronic Bronchitis Based on Network Pharmacology and Observational Data.

BACKGROUND: Chronic bronchitis is a type of common chronic inflammatory respiratory disease, which is mainly characterized by chronic cough and expectoration. Clinical practice and experimental research have shown that the modified tonifying spleen-lung method has significant preventive and therapeutic effects on chronic lung diseases, but the mechanism of TSLR in the treatment of chronic bronchitis are not yet clear. OBJECTIVE: To explore the mechanism of tonifying spleen-lung recipe (TSLR) in the treatment of chronic bronchitis (CB) through network pharmacology combined with observational studies. MATERIALS AND METHODS: The effective components, core targets and signaling pathways of TSLR in the treatment of chronic bronchitis were obtained using network pharmacology. One hundred and thirty-seven elderly CB patients were selected as the observational group who were treated by TSLR, and 67 no-CB cases from the Physical Examination Center were selected as the control group. We compared the levels of inflammatory parameters between patients before and after TSLR treatment, and after treatment group with the control group were also compared. RESULTS: The key effective components of TSLR selected by network pharmacology included quercetin, kaempferol, luteolin, and nobiletin, and the core targets involved HSP90AA1, AKT1, JUN, MAPK1, IL6, MAPK3, MAPK14, STAT1, NFKB1, and CDKN1. KEGG pathway enrichment analysis revealed that the TNF signaling pathway, PI3K-AKT and AGE-RAGE signaling pathways might play a key role in the treatment of CB. The observation study demonstrated that compared with the control group, the levels of WBC, NEU, NLR, PCT, and CRP in the research group after TSLR treatment were increased. Although the levels of WBC, NEU, NLR, and PCT in the research group after TSLR treatment were higher than those in the control group, the above indicators trend tended towards the control group, and there was no significant difference in CRP indicators between the control group and after treatment group. CONCLUSION: TSLR had a good therapeutic effect on chronic bronchitis patients, which might be related to the fact that the natural active components in TSLR inhibit inflammation by regulating the expression of proteins related to PI3K-AKT and TNF signaling pathways.

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.