To explore the advantages of traditional Chinese medicine （TCM） and integrative TCM-Western medicine approaches in the treatment of diabetic microvascular complications （DMC）， refine key pathophysiological insights and treatment principles， and promote academic innovation and strategic research planning in the prevention and treatment of DMC. The 38th session of the Expert Salon on Diseases Responding Specifically to Traditional Chinese Medicine， hosted by the China Association of Chinese Medicine， was held in Beijing， 2024. Experts in TCM， Western medicine， and interdisciplinary fields convened to conduct a systematic discussion on the pathogenesis， diagnostic and treatment challenges， and mechanism research related to DMC， ultimately forming a consensus on key directions. Four major research recommendations were proposed. The first is addressing clinical bottlenecks in the prevention and control of DMC by optimizing TCM-based evidence evaluation systems. The second is refining TCM core pathogenesis across DMC stages and establishing corresponding "disease-pattern-time" framework. The third is innovating mechanism research strategies to facilitate a shift from holistic regulation to targeted intervention in TCM. The fourth is advancing interdisciplinary collaboration to enhance the role of TCM in new drug development， research prioritization， and guideline formulation. TCM and integrative approaches offer distinct advantages in managing DMC. With a focus on the diseases responding specifically to TCM， strengthening evidence-based support and mechanism interpretation and promoting the integration of clinical care and research innovation will provide strong momentum for the modernization of TCM and the advancement of national health strategies.

Background Light gradient boosting machine (LightGBM) has become a popular choice in prediction models due to its high efficiency and speed. However, the "black box" issues in machine learning models lead to poor model interpretability. At present, few studies have evaluated the severity of occupational injuries from the perspective of LightGBM model and model interpretability. Objective To evaluate the application value of LightGBM models and model interpretability methods in occupational injury prediction. Methods The Mine Safety and Health Administration (MSHA) occupational injury data set of mining industry workers from 1983 to 2022 was used. Injury severity (death/fatal occupational injury and permanent/partial disability) was used as the outcome variable, and the predictor variables included the month of occurrence, age, sex, time of accident, time since beginning of shift, accident time interval from shift start, total experience, total mining experience, experience at this mine, cause of injury, accident type, activity of injury, source of injury, body part of injury, work environment type, product category, and nature of injury. Feature sets were screened using least absolute shrinkage and selection operator (Lasso) regression. A LightGBM model was then employed to predict occupational injury, with area under curve (AUC) of the model serving as the primary evaluation metric; an AUC closer to 1 indicates better predictive performance of the model. The interpretability of the model was evaluated using Shapley additive explanations (SHAP). Results Through Lasso regression, 7 key influencing factors were identified, including accident time interval from shift start, experience at this mine, cause of injury, accident type, body part of injury, nature of injury, and work environment type. A LightGBM model, constructed based on feature selection via Lasso regression, demonstrated good predictive performance with an AUC value of 0.9941 (95%CI: 0.9917, 0.9966), accuracy of 0.9743, specificity of 0.9781, and sensitivity of 0.9640. The predicted probability of fatal occupational injuries showed high consistency with the actual probability of fatal occupational injuries. In the occupational injury prediction model, the importance of each indicator was analyzed through its SHAP value, and it was found that the body part of injury and the nature of injury were the two main features that affected the prediction results of the model, and the impacts of other features were relatively small. The distribution of SHAP values across body part of injury was broad, with significant impacts on the model's prediction of fatal risk, particularly for injuries to the head and neck, as well as multi-part injuries. The nature of injury also exerted influences on the model in different directions, with suffocation/drowning, crushing, and multi-part injuries having a greater impact on the risk of fatal occupational injuries. Conclusion LightGBM model is capable of efficiently processing large-scale data and providing high-precision prediction results. Research on model interpretability aids in more accurately exploring and analyzing various key risk factors for fatal occupational injuries among mining workers, and further reveals the complex interactions among these factors. This, in turn, enables better preventive intervention and protection measures, as well as optimal resource allocation for labor workers.

ObjectiveUlcerative colitis is a prevalent immunoinflammatory disease. Th17/Treg cell imbalance and gut microbiota dysregulation are key factors in ulcerative colitis pathogenesis. The actin cytoskeleton contributes to regulating the proliferation, differentiation, and migration of Th17 and Treg cells. Wdr63, a gene containing the WD repeat domain, participates in the structure and functional modulation of actin cytoskeleton. Recent research indicates that WDR63 may serve as a regulator of cell migration and metastasis via actin polymerization inhibition. This article aims to explore the effect of Wdr63 deletion on Th17/Treg cells and ulcerative colitis. MethodsWe constructed Wdr63^-/- mice, induced colitis in mice using dextran sulfate sodium salt, collected colon tissue for histopathological staining, collected mesenteric lymph nodes for flow cytometry analysis, and collected healthy mouse feces for microbial diversity detection. ResultsCompared with wild-type colitis mice, Wdr63^-/- colitis mice had a more pronounced shortening of colonic tissue, higher scores on disease activity index and histological damage index, Treg cells decreased and Th17 cells increased in colonic tissue and mesenteric lymph nodes, a lower level of anti-inflammatory cytokine IL-10, and a higher level of pro-inflammatory cytokine IL-17A. In addition, WDR63 has shown positive effects on maintaining intestinal microbiota homeostasis. It maintains the balance of Bacteroidota and Firmicutes, promoting the formation of beneficial intestinal bacteria linked to immune inflammation. ConclusionWdr63 deletion aggravates ulcerative colitis in mice, WDR63 inhibits colonic inflammation likely by regulating Th17/Treg balance and maintains intestinal microbiota homeostasis.

Diabetic retinopathy(DR)is a late-stage peripheral micro-neurovascular complication of chronic hyperglycemia, leading to blood-retinal barrier impairment and retinal dysfunction. Recent studies have found that diabetic neuroretinopathy(DN)may be one of the earliest events in diabetic retinal alterations. The main features include defective electroretinographic responses in newly diagnosed patients, early self-activation of microglia and Müller cells, reduced activity of neurotransmitters(e.g., DOPA/GABA), and early mitochondrial dysfunction, such as persistent Drp1-Fis1 fission and mtDNA methylation mismatches. Understanding the molecular basis of DN is essential for elucidating its pathogenesis and developing early treatments. This review summarizes pathological changes and mechanisms of retinal function, glial cells, neurotransmitters, mitochondria, and other factors in early diabetes mellitus, in order to provide a theoretical foundation for investigating early DN mechanisms and developing targeted therapies.

Objective: To explore the association between vitamin D levels and sleep in children and adolescents，so as to provide a reference for promoting the sleep health of children and adolescents. Methods: From October to December, 2021, 4 827 primary and middle school students aged 6-17 in Shenzhen were selected by multistage cluster random sampling method, and their demographic information, family background, lifestyle and sleep status were obtained by facetoface questionnaire survey, and their fasting venous blood in the morning was collected to detect the serum 25(OH)D level. The relationship between serum vitamin D level and sleep characteristics was analyzed by binary Logistic regression, and stratified analysis was carried out according to gender. Results: The proportion of vitamin D deficiency was 41.1%, and the proportion of sleep deficiency was 19.4%. With the increase of vitamin D level, daily sleep duration of children and adolescents tended to increase (r=0.10,P<0.01). After adjusting for covariates such as gender and age, it was found that children and adolescents with insufficient vitamin D levels were more likely to experience sleep insufficiency, social jetlag, and late sleep on weekdays, with ORs being 1.32(95%CI=1.12-1.56), 1.35(95%CI=1.19-1.54), and 1.26(95%CI=1.05-1.52)(P<0.05). Sexstratified analysis showed that, among boys, vitamin D deficiency was associated with sleep deficiency, social jetlag, and late bedtime on weekdays and weekends[OR(95%CI)=1.42(1.14-1.77),1.25(1.04-1.49),1.39(1.06-1.82),1.86(1.19-2.92),P<0.05]. In girls, however, serum vitamin D levels were only associated with social jetlag with OR being 1.47 (95%CI=1.21-1.79, P<0.05). Conclusion Vitamin D levels are associated with various sleep characteristics in children and adolescents, with this association being more pronounced among boys.

Fecal microbiota transplantation (FMT) technology originated in China during the Eastern Jin Dynasty and has rapidly developed over the past two decades, becoming a primary method for studying the causal relationship between gut microbiota and the occurrence and progression of diseases. At the same time, the therapeutic effects of FMT in the field of gastrointestinal diseases have gained widespread recognition and are gradually expanding into other disease areas. The FMT procedure is relatively complex, and there is currently no standardized method; its success is influenced by various factors, including the donor, recipient, processing of the fecal material, and the method of implantation. Given the increasingly recognized relationship between gut microbiota and various diseases, FMT has become a research hotspot in both scientific studies and clinical applications, achieving a series of significant advancements. To help researchers better understand this technology, this paper will outline the development history of FMT, summarize common operational methods in research and clinical settings, review its application progress, and look forward to future development directions.

OBJECTIVE To summarize the current situation of pharmaceutical clinic service in our hospital over the past five years， and explore sustainable development strategies for service models of pharmaceutical clinics. METHODS A retrospective analysis was conducted on the consultation records of patients who registered and established files at the pharmaceutical clinic in our hospital from January 2019 to December 2023. Statistical analysis was performed on patients’ general information， medication- related problems， and types of pharmaceutical services provided by pharmacists. RESULTS A total of 963 consultation records were included， among which females aged 20-39 years accounted for the highest proportion （66.04%）； obstetrics and gynecology- related consultations accounted for the largest number of cases. Additionally， 80 patients attended follow-up visits at our hospital’s pharmaceutical clinic. A total of 1 029 medication-related issues were resolved， including 538 cases of drug consultations （52.28%）， 453 medication recommendations （44.02%）， 22 medication restructuring（2.14%）， and 16 medication education （1.55%）； the most common types of medication-related problems identified were adverse drug events（70.07%）. CONCLUSIONS Although the pharmaceutical clinic has achieved recognition from clinicians and patients， challenges such as low awareness among healthcare providers and the public persist. Future efforts should focus on strengthening information technology construction， enhancing pharmacist training， and establishing various forms of outpatient pharmaceutical service models.

Osteoporosis （OP） is a systemic metabolic disease with a strong correlation with age. The prevalence of osteoporosis is rising annually as a consequence of the growing issue of population ageing. The current treatments for OP have numerous shortcomings. In contrast， traditional Chinese medicine has a long history and a rich species diversity. Furthermore， recent years have seen an increase in the number of studies examining the anti-OP properties of traditional Chinese medicine. This may provide a safe and effective alternative strategy for the treatment of OP. The zebrafish， due to its favourable optical transparency and high homology with human genes， has been extensively employed as an animal research model in the investigation of human skeletal-related disease mechanisms and drug screening. This paper presents a review of anti-osteoporosis studies of traditional Chinese medicine using zebrafish as a model for osteoporosis. It also provides a summary of the experimental evaluation methods involved in such studies， an analysis of the current status of traditional Chinese medicine in the treatment of osteoporosis using zebrafish as a model， and a summary of the mechanism of action and the signalling pathways involved in traditional Chinese medicine in the anti-osteoporosis treatment of zebrafish. The current research status of Chinese medicine in the treatment of OP was analysed， as well as the mechanism of action of Chinese medicine against OP and the signalling pathways involved. Furthermore， the advantages and disadvantages of various zebrafish modelling methods of OP were compared with those of traditional animal models. The objective of this study is to provide a reference for the evaluation method of the zebrafish model in the study of bone-related diseases， as well as for the study of the mechanism of action of traditional Chinese medicine against OP and for the reference of the research and development of new drugs.

Circadian rhythm refers to the 24-hour periodic changes in behavior, physiology, and molecular processes in the human body. Disruptions to the circadian rhythm not only affect mental health but are also associated with various metabolic disorders, including the regulation of bone and muscle metabolism. Research has shown that work-related musculoskeletal disorders (WMSDs) are influenced not only by workload but also by circadian rhythm factors, such as shift work. This review examined the relationships between circadian rhythm-related antecedents, outcomes, and WMSDs, exploring their shared metabolic markers and mechanisms. It provided a systematic overview of the intrinsic connection between circadian rhythm disruptions and WMSDs. While current studies highlight the impact of circadian rhythm disturbances on musculoskeletal disorders, further investigation is required to address the confounding factors involved. Future research should integrate chronobiology with both subjective and objective data to explore the pathway from environmental factors to intermediate phenotypes to diseases, ultimately providing a more comprehensive understanding of the network mechanisms underlying WMSDs.

The inherent clinical uncertainties, substantial costs, and small patient cohorts of orphan drugs limit the applicability of randomized controlled trial (RCT)-based health technology assessments (HTAs) in guiding coverage criteria, sustainable financing models, and equitable reimbursement frameworks for medical financial assistance policies for rare diseases.The digital transformation in healthcare system leads to solutions to the challenges in designing the policy by using data-driven decision-making. This article summarizes the decision-making issues in policy design, discusses the current status and trends of digital transformation, and analyzes the important new opportunities for AI-driven policy design for medical financial assistance policies for rare diseases. Decision-making that is digital intelligence driven and using techniques such as big data analytics and real-world research methods will enhance targeting efficiency, improve the quality of financing, and realize the performance-based reimbursement in the medical financial assistance, providing significant value in facilitating the policy reform and development for rare diseases healthcare.