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.

OBJECTIVE To investigate the metabolic differences in roots， stems and leaves of Angelica dahurica （Fisch.ex Hoffm.） Benth. et Hook. f. ex Franch. et Savat. （shortened for A. dahurica ）， and to provide a basis for the development and utilization of its medicinal resources. METHODS UPLC-MS/MS untargeted metabolomics technology was employed to detect metabolites in roots， stems and leaves of A. dahurica . Multivariate statistical analysis was used to screen differential metabolites and perform cluster analysis. Furthermore， Kyoto Encyclopedia of Genes and Genomes （KEGG） pathway enrichment analysis was conducted on these differential metabolites. RESULTS A total of 769 metabolites were identified in roots， stems and leaves of A. dahurica . Among t hem， lipids， alkaloids， amino acids and their derivatives accounted for the highest proportion， which accounted for 14.31%， 13.91% and 13.65%， respectively， and the coumarin compounds accounted for 3.25%. Results of principal component analysis， orthogonal partial least squares-discriminant analysis and cluster analysis indicated that there were certain differences in metabolites among the three tissues. A total of 205 differential metabolites were identified between stems and roots （82 up-regulated and 123 down-regulated）， 326 differential metabolites between leaves and roots （247 up-regulated and 79 down-regulated）， and 293 differential metabolites between the leaves and stems （263 up-regulated and 30 down-regulated）. Further analysis of the differential coumarin metabolites in different plant parts revealed that several compounds， including （ R ）-oxypeucedanin， 4-hydroxycoumarin， 4-methylumbelliferyl acetate， exhibited relatively high contents in the leaves. KEGG pathway enrichment analysis indicated that differential metabolites predominantly accumulated in the phenylpropanoid biosynthesis and ABC transporter pathways. CONCLUSIONS There are significant differences in metabolites among roots， stems and leaves of A. dahurica . Coumarin compounds are highly enriched in the leaves， and their synthesis and transport may be related to the synergistic effect of the phenylpropanoid biosynthesis pathway and the ABC transporter pathway.

Objective To investigate the post-discharge exercise behavior and factors influencing moderate to vigorous intensity physical activity (MVPA) in patients undergoing lung surgery. Methods A total of 2874 patients from the large prospective, observational perioperative lung symptom study cohort (CN-PRO-Lung 3) in the Department of Thoracic Surgery at Sichuan Cancer Hospital between April 7, 2021, and January 31, 2024, were selected as the survey subjects. A survey was conducted using the Investigation of Exercise Behavior after Lung Surgery questionnaire and the International Physical Activity Questionnaire-Short Form (IPAQ-SF) among patients who underwent lung surgery. Binary logistic regression was used to analyze the factors influencing patients’ engagement in MVPA. Results A total of 702 patients were surveyed, including 252 males and 450 females, with an average age of (52.4±10.2) years. Patients with lung cancer accounted for 85.9%. Only 36.0% of the patients had regular exercise habits, while 42.3% did not engage in any physical activity. The three main barriers for postoperative exercise were physical discomfort (pain, coughing, shortness of breath, etc, 54.7%), lack of professional guidance (41.7%), and concerns about the surgical wound (28.9%). The proportions of patients engaging in vigorous, moderate, and low-intensity physical activity were 5.7%, 28.2%, and 66.1%, respectively. Multivariate analysis showed that patients with a personal annual income ≥50000 yuan (OR=1.52, 95%CI 1.01-2.29, P=0.044), high school education or above (OR=1.92, 95%CI 1.33-2.76, P<0.001), and lobectomy (OR=1.44, 95%CI 1.02-2.03, P=0.037) engaged in more MVPA. Conclusion Patients undergoing lung surgery have inadequate physical activity after discharge, particularly lacking in MVPA. Patients with higher income, higher educational levels, and lobectomy are more frequently engaged in MVPA. Measures such as symptom control, providing exercise guidance, and enhancing education on wound care may potentially improve the inadequate physical activity in lung surgery patients after discharge.

Objective To explore the visiting behavior and health management strategy of patients with cardiovascular disease (CVD) and the industry management strategies under the family doctor contract service model. Methods A total of 351 patients with CVD from January 2022 to January 2025 were included in the case investigation by random sampling. Taking the number of visits in community health service institutions/total number of visits ≥90% as high compliance, the influencing factors of the visiting behavior of CVD patients were analyzed. Results Finally, 344 valid questionnaires were collected, with an effective recovery rate of 98.01%. The study subjects were categorized into high compliance group (271 cases) and low compliance group (73 cases). The scores of policy cognition understanding, institutional trust and medical service understanding in the high compliance group were higher compared with those in the low compliance group (P<0.05). There was no significant difference in the understanding of medical expenses between the two groups (P>0.05). Multivariate logistic regression model analysis revealed that CVD duration [OR (95% CI): 1.72 (1.21-2.44)], policy cognition understanding [OR (95% CI): 2.16 (1.20-3.89)] and institutional trust [OR (95% CI): 1.88 (1.08-3.25)] were independently associated with medical treatment behavior in CVD patients (P<0.05). Conclusion Enhancing the policy cognition understanding and institutional trust of CVD patients on family doctor contract service can promote patients to seek medical treatment and promote the high-quality development of family doctor contract service.

This study systematically reviews the modern research progress of sanjiao （三焦） theory in the past 20 years， and differentiates concepts such as membrane-source， membrane-interstices， and membrane-system in the study of sanjiao essence. On the basis of its core functions of transporting original qi and regulating water metabolism， the study further expands the theoretical connotation of transporting original qi， defines the application boundary of sanjiao syndrome differentiation， and clarifies the conceptual categories of "sanjiao qi-transformation" and "qi-transforming sanjiao"， which reconciles the contradictions between sanjiao as a six fu （腑） organ and sanjiao as a regional division to a certain extent， and provides a valuable exploration for the innovative development of the theoretical system of traditional Chinese medicine.

Objective: Machine learning (ML) has been reported to have better predictive capability than traditional statistical techniques. The aim of this study was to assess the efficacy of ML algorithms and logistic regression (LR) for predicting depressive symptoms during the COVID-19 pandemic. Methods: Analyses were carried out in a national cross-sectional study involving 21,916 participants. The ML algorithms in this study included random forest (RF), support vector machine (SVM), neural network (NN), and gradient boosting machine (GBM) methods. The performance indices were sensitivity, specificity, accuracy, precision, F1-score, and area under the receiver operating characteristic curve (AUC). Results: LR and NN had the best performance in terms of AUCs. The risk of overfitting was found to be negligible for most ML models except for RF, and GBM obtained the highest sensitivity, specificity, accuracy, precision, and F1-score. Therefore, LR, NN, and GBM models ranked among the best models. Conclusion Compared with ML models, LR model performed comparably to ML models in predicting depressive symptoms and identifying potential risk factors while also exhibiting a lower risk of overfitting.

Objective: Machine learning (ML) has been reported to have better predictive capability than traditional statistical techniques. The aim of this study was to assess the efficacy of ML algorithms and logistic regression (LR) for predicting depressive symptoms during the COVID-19 pandemic. Methods: Analyses were carried out in a national cross-sectional study involving 21,916 participants. The ML algorithms in this study included random forest (RF), support vector machine (SVM), neural network (NN), and gradient boosting machine (GBM) methods. The performance indices were sensitivity, specificity, accuracy, precision, F1-score, and area under the receiver operating characteristic curve (AUC). Results: LR and NN had the best performance in terms of AUCs. The risk of overfitting was found to be negligible for most ML models except for RF, and GBM obtained the highest sensitivity, specificity, accuracy, precision, and F1-score. Therefore, LR, NN, and GBM models ranked among the best models. Conclusion Compared with ML models, LR model performed comparably to ML models in predicting depressive symptoms and identifying potential risk factors while also exhibiting a lower risk of overfitting.

Objective: Machine learning (ML) has been reported to have better predictive capability than traditional statistical techniques. The aim of this study was to assess the efficacy of ML algorithms and logistic regression (LR) for predicting depressive symptoms during the COVID-19 pandemic. Methods: Analyses were carried out in a national cross-sectional study involving 21,916 participants. The ML algorithms in this study included random forest (RF), support vector machine (SVM), neural network (NN), and gradient boosting machine (GBM) methods. The performance indices were sensitivity, specificity, accuracy, precision, F1-score, and area under the receiver operating characteristic curve (AUC). Results: LR and NN had the best performance in terms of AUCs. The risk of overfitting was found to be negligible for most ML models except for RF, and GBM obtained the highest sensitivity, specificity, accuracy, precision, and F1-score. Therefore, LR, NN, and GBM models ranked among the best models. Conclusion Compared with ML models, LR model performed comparably to ML models in predicting depressive symptoms and identifying potential risk factors while also exhibiting a lower risk of overfitting.

OBJECTIVE: This study reports the first imported case of Lassa fever (LF) in China. Laboratory detection and molecular epidemiological analysis of the Lassa virus (LASV) from this case offer valuable insights for the prevention and control of LF. METHODS: Samples of cerebrospinal fluid (CSF), blood, urine, saliva, and environmental materials were collected from the patient and their close contacts for LASV nucleotide detection. Whole-genome sequencing was performed on positive samples to analyze the genetic characteristics of the virus. RESULTS: LASV was detected in the patient's CSF, blood, and urine, while all samples from close contacts and the environment tested negative. The virus belongs to the lineage IV strain and shares the highest homology with strains from Sierra Leone. The variability in the glycoprotein complex (GPC) among different strains ranged from 3.9% to 15.1%, higher than previously reported for the seven known lineages. Amino acid mutation analysis revealed multiple mutations within the GPC immunogenic epitopes, increasing strain diversity and potentially impacting immune response. CONCLUSION The case was confirmed through nucleotide detection, with no evidence of secondary transmission or viral spread. The LASV strain identified belongs to lineage IV, with broader GPC variability than previously reported. Mutations in the immune-related sites of GPC may affect immune responses, necessitating heightened vigilance regarding the virus.
Humans ; China/epidemiology* ; Genome, Viral ; Lassa Fever/virology* ; Lassa virus/classification* ; Molecular Epidemiology ; Phylogeny