As the core vehicle for preserving and transmitting traditional Chinese medicine（TCM） academic thought and clinical experience， the establishment of a robust quality evaluation system for TCM clinical case reports is a crucial component in the current standardization and modernization of TCM. Based on the practical experience of constructing the China Clinical Cases Library of Traditional Chinese Medicine by the China Association of Chinese Medicine， this study conducted a comprehensive analysis of critical challenges， including insufficient authenticity and unfocused evaluation criteria. It proposed a three-dimensional evaluation framework grounded in the structure-process-outcome logic， encompassing three dimensions of authenticity and standardization， characteristics and advantages， application and translational impact. This framework integrated 12 key evaluation indicators in a systematic manner. The model preserved the academic characteristics of TCM syndrome differentiation and treatment， while aligning with modern scientific research standards， achieving a balance between individualized TCM experience and standardized evaluation. Concurrently， this study provided theoretical foundations and methodological guidance for evaluating the quality of TCM clinical cases， contributing significantly to the inheritance of TCM knowledge， evidence-based practice， and the reform of talent evaluation mechanisms.

Driven by policy initiatives promoting the integration of digital-intelligent technologies with primary healthcare and the digital transformation of traditional Chinese medicine (TCM), Smart TCM has emerged as a pivotal strategy for enhancing primary healthcare services for major chronic diseases. This paper reviews the current status, challenges, and feasible pathways of Smart TCM in community-level management of cardiovascular–cerebrovascular diseases and tumors, which represent major chronic disease burdens. Our findings indicate that Smart TCM demonstrates emerging potential in primary healthcare for chronic diseases across the entire continuum of “prevention-screening-diagnosis-treatment-management.” However, several significant challenges persist, including data silos and security vulnerabilities, limited applicability of existing models to real-world clinical needs, and insufficient digital literacy among primary healthcare physicians and elderly patients. To address these constraints, this paper proposes a multidimensional strategy encompassing the development of secure and interoperable regional data platforms, lightweight intelligent devices and support services aligned with primary care capacity, unified technical and data standards with corresponding quality control systems, adaptive and dynamically updated artificial intelligent models, interdisciplinary workforce training and patient education programs, and enhanced policy and health insurance support. Overall, Smart TCM shows great promise for improving the efficiency of primary healthcare delivery and establishing innovative TCM-based chronic disease management paradigms.

BACKGROUND: Spicy food consumption has been reported to be inversely associated with mortality from multiple diseases. However, the effect of spicy food intake on the incidence of vascular diseases in the Chinese population remains unclear. This study was conducted to explore this association. METHODS: This study was performed using the large-scale China Kadoorie Biobank (CKB) prospective cohort of 486,335 participants. The primary outcomes were vascular disease, ischemic heart disease (IHD), major coronary events (MCEs), cerebrovascular disease, stroke, and non-stroke cerebrovascular disease. A Cox proportional hazards regression model was used to assess the association between spicy food consumption and incident vascular diseases. Subgroup analysis was also performed to evaluate the heterogeneity of the association between spicy food consumption and the risk of vascular disease stratified by several basic characteristics. In addition, the joint effects of spicy food consumption and the healthy lifestyle score on the risk of vascular disease were also evaluated, and sensitivity analyses were performed to assess the reliability of the association results. RESULTS: During a median follow-up time of 12.1 years, a total of 136,125 patients with vascular disease, 46,689 patients with IHD, 10,097 patients with MCEs, 80,114 patients with cerebrovascular disease, 56,726 patients with stroke, and 40,098 patients with non-stroke cerebrovascular disease were identified. Participants who consumed spicy food 1-2 days/week (hazard ratio [HR] = 0.95, 95% confidence interval [95% CI] = [0.93, 0.97], P <0.001), 3-5 days/week (HR = 0.96, 95% CI = [0.94, 0.99], P = 0.003), and 6-7 days/week (HR = 0.97, 95% CI = [0.95, 0.99], P = 0.002) had a significantly lower risk of vascular disease than those who consumed spicy food less than once a week ( Ptrend <0.001), especially in those who were younger and living in rural areas. Notably, the disease-based subgroup analysis indicated that the inverse associations remained in IHD ( Ptrend = 0.011) and MCEs ( Ptrend = 0.002) risk. Intriguingly, there was an interaction effect between spicy food consumption and the healthy lifestyle score on the risk of IHD ( Pinteraction = 0.037). CONCLUSIONS Our findings support an inverse association between spicy food consumption and vascular disease in the Chinese population, which may provide additional dietary guidance for the prevention of vascular diseases.
Humans ; Male ; Female ; Prospective Studies ; Middle Aged ; Aged ; Vascular Diseases/etiology* ; Risk Factors ; China/epidemiology* ; Adult ; Proportional Hazards Models ; Cerebrovascular Disorders/epidemiology* ; East Asian People

With the rapid development of personalized medicine, recommending the optimal treatment regimes among multiple options for individual patients has become a key topic in the study of individualized treatment rules. Existing methods often face challenges such as limited accuracy and robustness when handling multi-category treatment problems. This study proposes a weighted random forest method that formulates the treatment decision problem as a weighted classification task. By incorporating the expected loss differences among treatment outcomes, the method enhances its learning process and improves recommendation performance with the non-parametric nature and flexibility of random forests. The weighted random forest method is further applied to real-world hypertension intervention data to generate personalized antihypertensive treatment recommendations based on the patient's baseline characteristics, demonstrating its potential value in clinical practice. This research aims to provide a new approach for individualized treatment rules in multi-treatment settings and to support the development of data-driven clinical decision-making systems.

Objective To compare the predictive performance of the deep forest ensemble model,deep forest,and random forest in medical classification.Methods This study proposed a deep forest ensemble model that integrated Sobol-MDA(Sobol-mean decrease accuracy)with the cascading structure of deep forest and feature extraction capabilities of random forest.The model was applied to both simulation and real medical data analysis.The simulation experiments covered scenarios such as unbalanced outcome variables,nonlinear relationships,noise variables,multicollinearity,and interactions.The real data analysis was conducted on parotid MRI data to compare the performance of the models in terms of area under curve(AUC)and other metrics.Results In both the simulation and real data analysis,the deep forest ensemble model demonstrated superiority,especially in complex interaction scenarios,where its predictive performance significantly outperformed deep forest and random forest models.Conclusion Deep forest ensemble model shows significant advantages in addressing complex medical classification tasks.Its predictive performance outperforms traditional models.

ObjectiveTo investigate the mechanism of Tangbikang dry paste in the prevention and treatment of type 2 diabetic peripheral neuropathy （DPN） based on the glyoxalase-1 （GLO-1）/advanced glycation end products （AGE）/receptor for advanced glycation end products （RAGE） pathway. MethodsA total of 56 Sprague-Dawley rats were randomly divided， with eight assigned to the normal group. The remaining 48 rats were fed a high-fat diet combined with intraperitoneal injection of streptozotocin （STZ） to induce a type 2 diabetes mellitus （T2DM） model. Based on blood glucose levels， the rats were randomly assigned to the model group， Tanglin group （13.5 mg·kg^-1）， metformin group （135 mg·kg^-1）， and Tangbikang dry paste low-， medium-， and high-dose groups （3， 6， 12 g·kg^-1）. Successful modeling of DPN was confirmed by a decrease in mechanical pain threshold in the model group at week 4. Fasting blood glucose， body weight， and mechanical pain threshold were measured every 4 weeks. After 16 weeks of intervention， the pathological morphology of the sciatic nerve was observed using hematoxylin-eosin （HE） staining. The expression of RAGE， AGE， protein kinase C （PKC）， and collagen （COL） in the sciatic nerve was assessed by immunohistochemistry. The mRNA expression of RAGE， PKC， Toll-like receptor （TLR）， COL， and GLO-1 was detected using real-time quantitative PCR （Real-time PCR）. Serum levels of aspartate aminotransferase （AST）， alanine aminotransferase （ALT）， creatinine （CREA）， urea （UREA）， interleukin-6 （IL-6）， and tumor necrosis factor （TNF）-α were measured by enzyme-linked immunosorbent assay （ELISA）. ResultsCompared with the normal group， the model group showed significantly increased fasting blood glucose （P<0.01）， decreased body weight and mechanical pain threshold （P<0.01）， and elevated serum AST， ALT， CREA， UREA， IL-6， and TNF-α levels （P<0.01）. The expression of RAGE， AGE， and PKC in the sciatic nerve was significantly increased （P<0.01）， while COL expression was decreased （P<0.01）. The mRNA expression of TLR， RAGE， and PKC was upregulated （P<0.01）， whereas COL and GLO-1 mRNA levels were downregulated （P<0.01）. Histological examination showed irregular nerve morphology， axonal alterations， and myelin degeneration. Compared with the model group， fasting blood glucose levels in the Tangbikang dry paste high-dose group at all time points and in the medium-dose group at weeks 4 and 16 were significantly reduced （P<0.05， P<0.01）. No significant changes in body weight were observed across all Tangbikang dose groups. The mechanical pain threshold was elevated at different time points after administration in all Tangbikang groups （P<0.05， P<0.01）. Serum IL-6 and TNF-α levels were decreased in all dose groups （P<0.05， P<0.01）. The expression of RAGE， AGE， and PKC in the sciatic nerve was reduced （P<0.01）， while COL expression was increased （P<0.01）. The mRNA expression of TLR， RAGE， and PKC was downregulated （P<0.01）， whereas GLO-1 mRNA expression was upregulated （P<0.05， P<0.01）. Additionally， COL mRNA expression was significantly increased in the low- and high-dose groups （P<0.01）. Pathological changes in the sciatic nerve were milder in all Tangbikang groups compared to the model group. ConclusionTangbikang dry paste significantly improves DPN， and its mechanism may be associated with the regulation of the GLO-1/AGE/RAGE signaling pathway.

ObjectiveTo investigate the mechanism of Tangbikang dry paste in the prevention and treatment of type 2 diabetic peripheral neuropathy （DPN） based on the glyoxalase-1 （GLO-1）/advanced glycation end products （AGE）/receptor for advanced glycation end products （RAGE） pathway. MethodsA total of 56 Sprague-Dawley rats were randomly divided， with eight assigned to the normal group. The remaining 48 rats were fed a high-fat diet combined with intraperitoneal injection of streptozotocin （STZ） to induce a type 2 diabetes mellitus （T2DM） model. Based on blood glucose levels， the rats were randomly assigned to the model group， Tanglin group （13.5 mg·kg^-1）， metformin group （135 mg·kg^-1）， and Tangbikang dry paste low-， medium-， and high-dose groups （3， 6， 12 g·kg^-1）. Successful modeling of DPN was confirmed by a decrease in mechanical pain threshold in the model group at week 4. Fasting blood glucose， body weight， and mechanical pain threshold were measured every 4 weeks. After 16 weeks of intervention， the pathological morphology of the sciatic nerve was observed using hematoxylin-eosin （HE） staining. The expression of RAGE， AGE， protein kinase C （PKC）， and collagen （COL） in the sciatic nerve was assessed by immunohistochemistry. The mRNA expression of RAGE， PKC， Toll-like receptor （TLR）， COL， and GLO-1 was detected using real-time quantitative PCR （Real-time PCR）. Serum levels of aspartate aminotransferase （AST）， alanine aminotransferase （ALT）， creatinine （CREA）， urea （UREA）， interleukin-6 （IL-6）， and tumor necrosis factor （TNF）-α were measured by enzyme-linked immunosorbent assay （ELISA）. ResultsCompared with the normal group， the model group showed significantly increased fasting blood glucose （P<0.01）， decreased body weight and mechanical pain threshold （P<0.01）， and elevated serum AST， ALT， CREA， UREA， IL-6， and TNF-α levels （P<0.01）. The expression of RAGE， AGE， and PKC in the sciatic nerve was significantly increased （P<0.01）， while COL expression was decreased （P<0.01）. The mRNA expression of TLR， RAGE， and PKC was upregulated （P<0.01）， whereas COL and GLO-1 mRNA levels were downregulated （P<0.01）. Histological examination showed irregular nerve morphology， axonal alterations， and myelin degeneration. Compared with the model group， fasting blood glucose levels in the Tangbikang dry paste high-dose group at all time points and in the medium-dose group at weeks 4 and 16 were significantly reduced （P<0.05， P<0.01）. No significant changes in body weight were observed across all Tangbikang dose groups. The mechanical pain threshold was elevated at different time points after administration in all Tangbikang groups （P<0.05， P<0.01）. Serum IL-6 and TNF-α levels were decreased in all dose groups （P<0.05， P<0.01）. The expression of RAGE， AGE， and PKC in the sciatic nerve was reduced （P<0.01）， while COL expression was increased （P<0.01）. The mRNA expression of TLR， RAGE， and PKC was downregulated （P<0.01）， whereas GLO-1 mRNA expression was upregulated （P<0.05， P<0.01）. Additionally， COL mRNA expression was significantly increased in the low- and high-dose groups （P<0.01）. Pathological changes in the sciatic nerve were milder in all Tangbikang groups compared to the model group. ConclusionTangbikang dry paste significantly improves DPN， and its mechanism may be associated with the regulation of the GLO-1/AGE/RAGE signaling pathway.

In recent years, the reform of the registration, evaluation, and approval system for traditional Chinese medicine(TCM) has been promoted at the national level, with establishment of an evaluation evidence system for TCM registration that combines TCM theory, human use experience, and clinical trials(known as the "three-combined" evaluation evidence system). This system, which aligns with the characteristics of TCM clinical practice and the laws of TCM research and development, recognizes the unique value of human use experience in medicine and returns to the essence of medicine as an applied science, thus receiving widespread recognition from both academia and industry. However, it meanwhile poses new and higher challenges. This article delves into the value and challenges faced by the "three-combined" evaluation evidence system from three perspectives: registration management, medical institutions, and the TCM industry. Furthermore, it discusses how the China Association of Chinese Medicine, leveraging its academic platform advantages and leading roles, has made exploratory and practical efforts to facilitate the research and development of new TCM drugs and the implementation of the "three-combined" evaluation evidence system.
Drugs, Chinese Herbal/standards* ; Humans ; Medicine, Chinese Traditional/standards* ; China ; Drug Development

Objective To compare the predictive performance of the deep forest ensemble model,deep forest,and random forest in medical classification.Methods This study proposed a deep forest ensemble model that integrated Sobol-MDA(Sobol-mean decrease accuracy)with the cascading structure of deep forest and feature extraction capabilities of random forest.The model was applied to both simulation and real medical data analysis.The simulation experiments covered scenarios such as unbalanced outcome variables,nonlinear relationships,noise variables,multicollinearity,and interactions.The real data analysis was conducted on parotid MRI data to compare the performance of the models in terms of area under curve(AUC)and other metrics.Results In both the simulation and real data analysis,the deep forest ensemble model demonstrated superiority,especially in complex interaction scenarios,where its predictive performance significantly outperformed deep forest and random forest models.Conclusion Deep forest ensemble model shows significant advantages in addressing complex medical classification tasks.Its predictive performance outperforms traditional models.

With the rapid development of personalized medicine, recommending the optimal treatment regimes among multiple options for individual patients has become a key topic in the study of individualized treatment rules. Existing methods often face challenges such as limited accuracy and robustness when handling multi-category treatment problems. This study proposes a weighted random forest method that formulates the treatment decision problem as a weighted classification task. By incorporating the expected loss differences among treatment outcomes, the method enhances its learning process and improves recommendation performance with the non-parametric nature and flexibility of random forests. The weighted random forest method is further applied to real-world hypertension intervention data to generate personalized antihypertensive treatment recommendations based on the patient's baseline characteristics, demonstrating its potential value in clinical practice. This research aims to provide a new approach for individualized treatment rules in multi-treatment settings and to support the development of data-driven clinical decision-making systems.