Parkinson's disease （PD） is a common neurodegenerative disorder characterized by motor impairments， with its pathological mechanisms involving multiple processes such as the degeneration of dopaminergic neurons and the abnormal aggregation of α-synuclein. Current Western medical treatments face challenges including diminished long-term efficacy and motor complications. In recent years， Traditional Chinese Medicine （TCM） has demonstrated advantages in the prevention and treatment of PD through its systematic regulatory capabilities， featuring multi-component， multi-target， and multi-pathway approaches.This article systematically reviews the roles of seven key signaling pathways-NF-κB， AMPK/mTOR， PI3K/Akt， MAPKs， Nrf2/ARE， Wnt/β-catenin， and BDNF/TrkB-in the pathological process of PD and the regulatory mechanisms of TCM. Research indicates that active ingredients of Chinese herbs and compound formulations can synergistically modulate these pathways， exerting comprehensive effects in inhibiting neuroinflammation， alleviating oxidative stress， promoting autophagy to clear abnormal proteins， and enhancing neurotrophic support. These signaling pathways form a complex regulatory network through crosstalk among key nodal molecules， constituting an intricate regulatory system in PD pathology. The multi-target intervention characteristics of TCM align well with this network-based regulatory requirement， achieving integrated anti-inflammatory， antioxidant， autophagy-regulating， and neurorestorative effects through synergistic multi-pathway modulation. This article systematically outlines the mechanisms of TCM in the coordinated regulation of multiple pathways， providing a theoretical basis for elucidating the pathological process of PD and the intervention mechanisms of TCM， while also offering new perspectives and directions for modern research on TCM in the prevention and treatment of PD.

ObjectiveThis paper aims to find the identified and validated clinical biomarker data building upon a clinical study of early-phase phase Ⅱ and investigate the correlation analysis of Huanglian Jiedu Wan on syndrome improvement and clinical biomarkers in the treatment of "excess heat-toxicity" based on a machine learning model. Additionally， the effective prediction of clinical biomarker values for the main symptoms of the "excess heat-toxicity" syndrome was assessed. MethodsA total of 229 patients meeting the inclusion criteria for "excess heat-toxicity" syndrome were randomly divided into the Huanglian Jiedu Wan group and the placebo group. Syndrome score transition matrices were constructed for the Huanglian Jiedu Wan group and the placebo group based on three main symptoms of "excess heat-toxicity" syndrome， such as oral ulcers， sore throat， and gum swelling and pain. Data from the patients with these three syndromes were also integrated for an overall analysis. The corresponding syndrome score transition matrices were further constructed to visualize symptom change trends of the patients in the two groups via heatmaps. Based on the identified and validated clinical biomarkers related to inflammation， oxidative stress， and energy metabolism in the early phase， Spearman correlation analysis was employed to analyze and evaluate the associations between clinical biomarkers and syndrome improvement. Key clinical biomarkers reflecting the effect of Huanglian Jiedu Wan were screened through the comparison of differences between groups. An extreme gradient boosting （XGBoost） algorithm was used to develop a prediction model for main symptom classification， with classification performance evaluated through 10-fold cross-validation. Feature importance analysis was applied to identify variables with the greatest contribution to the prediction result. ResultsThe syndrome transition matrix results indicated that the Huanglian Jiedu Wan group showed a superior effect to the placebo group in improving oral ulcers， sore throat， and overall symptoms， with significant effects observed especially in sore throat and overall symptom analyses （P<0.01）. Spearman correlation analysis revealed that several clinical biomarkers positively correlated with "excess heat-toxicity" syndrome and its main symptom improvement， were also called "heat-related biomarkers"， including succinic acid， α-ketoglutaric acid， glycine， lactic acid， adenosine monophosphate （AMP）， tumor necrosis factor-α （TNF-α）， interferon-γ （IFN-γ）， interleukin-1β （IL-1β）， interleukin-4 （IL-4）， interleukin-6 （IL-6）， interleukin-8 （IL-8）， interleukin-10 （IL-10）， and so on. Conversely， clinical biomarkers negatively correlated with symptom severity， were also called "heat-clearing related biomarkers" after administration of Huanglian Jiedu Wan， including malic acid， fumaric acid， cis-aconitic acid， adrenocorticotropic hormone （ACTH）， IL-1β， IL-4， IL-8， succinic acid， and citric acid. The XGBoost classification model using all 52 biomarkers as variables achieved an average test accuracy of 0.754 and an average F1 score of 0.777. Feature importance analysis identified the scores of glutamic acid in saliva and IL-6 were the highest in all the variables， with importance scores of 0.081 and 0.080， respectively. After screening out 14 key variables and optimizing the parameters， model performance improved to an average accuracy of 0.758 and an F1 score of 0.798. Feature importance analysis further determined that the glutamic acid in saliva and IL-6 showed obvious changes after screening the variables， confirming the good syndrome prediction ability of the model constructed by these key clinical biomarkers. ConclusionThis study systematically elucidates the correlation between syndrome improvement and clinical biomarkers of Huanglian Jiedu Wan in the treatment of "excess heat-toxicity" syndrome. An XGBoost classification model based on key clinical biomarkers is successfully established， achieving effective prediction of the symptoms related to the "excess heat-toxicity" syndrome such as oral ulcers and sore throat and providing a new insight for objective identification of traditional Chinese medicine syndromes.

ObjectiveThis paper aims to find the identified and validated clinical biomarker data building upon a clinical study of early-phase phase Ⅱ and investigate the correlation analysis of Huanglian Jiedu Wan on syndrome improvement and clinical biomarkers in the treatment of "excess heat-toxicity" based on a machine learning model. Additionally， the effective prediction of clinical biomarker values for the main symptoms of the "excess heat-toxicity" syndrome was assessed. MethodsA total of 229 patients meeting the inclusion criteria for "excess heat-toxicity" syndrome were randomly divided into the Huanglian Jiedu Wan group and the placebo group. Syndrome score transition matrices were constructed for the Huanglian Jiedu Wan group and the placebo group based on three main symptoms of "excess heat-toxicity" syndrome， such as oral ulcers， sore throat， and gum swelling and pain. Data from the patients with these three syndromes were also integrated for an overall analysis. The corresponding syndrome score transition matrices were further constructed to visualize symptom change trends of the patients in the two groups via heatmaps. Based on the identified and validated clinical biomarkers related to inflammation， oxidative stress， and energy metabolism in the early phase， Spearman correlation analysis was employed to analyze and evaluate the associations between clinical biomarkers and syndrome improvement. Key clinical biomarkers reflecting the effect of Huanglian Jiedu Wan were screened through the comparison of differences between groups. An extreme gradient boosting （XGBoost） algorithm was used to develop a prediction model for main symptom classification， with classification performance evaluated through 10-fold cross-validation. Feature importance analysis was applied to identify variables with the greatest contribution to the prediction result. ResultsThe syndrome transition matrix results indicated that the Huanglian Jiedu Wan group showed a superior effect to the placebo group in improving oral ulcers， sore throat， and overall symptoms， with significant effects observed especially in sore throat and overall symptom analyses （P<0.01）. Spearman correlation analysis revealed that several clinical biomarkers positively correlated with "excess heat-toxicity" syndrome and its main symptom improvement， were also called "heat-related biomarkers"， including succinic acid， α-ketoglutaric acid， glycine， lactic acid， adenosine monophosphate （AMP）， tumor necrosis factor-α （TNF-α）， interferon-γ （IFN-γ）， interleukin-1β （IL-1β）， interleukin-4 （IL-4）， interleukin-6 （IL-6）， interleukin-8 （IL-8）， interleukin-10 （IL-10）， and so on. Conversely， clinical biomarkers negatively correlated with symptom severity， were also called "heat-clearing related biomarkers" after administration of Huanglian Jiedu Wan， including malic acid， fumaric acid， cis-aconitic acid， adrenocorticotropic hormone （ACTH）， IL-1β， IL-4， IL-8， succinic acid， and citric acid. The XGBoost classification model using all 52 biomarkers as variables achieved an average test accuracy of 0.754 and an average F1 score of 0.777. Feature importance analysis identified the scores of glutamic acid in saliva and IL-6 were the highest in all the variables， with importance scores of 0.081 and 0.080， respectively. After screening out 14 key variables and optimizing the parameters， model performance improved to an average accuracy of 0.758 and an F1 score of 0.798. Feature importance analysis further determined that the glutamic acid in saliva and IL-6 showed obvious changes after screening the variables， confirming the good syndrome prediction ability of the model constructed by these key clinical biomarkers. ConclusionThis study systematically elucidates the correlation between syndrome improvement and clinical biomarkers of Huanglian Jiedu Wan in the treatment of "excess heat-toxicity" syndrome. An XGBoost classification model based on key clinical biomarkers is successfully established， achieving effective prediction of the symptoms related to the "excess heat-toxicity" syndrome such as oral ulcers and sore throat and providing a new insight for objective identification of traditional Chinese medicine syndromes.

The disease-syndrome combination animal model in traditional Chinese medicine （TCM） andrology serves as an important bridge linking TCM theory with modern medical research， providing a key experimental platform for elucidating the 'syndrome-disease' correlation mechanism in male-specific diseases and for screening effective prescriptions. This article reviews recent progress in animal model research on common TCM andrological diseases， including prostatic diseases， sexual dysfunction， and male infertility， with a focus on analyzing the application， advantages， and disadvantages of various modeling strategies， such as immune induction， hormonal intervention， and multi-factor combination across different syndrome types. However， despite breakthroughs in model construction techniques， current research still faces several challenges， including insufficient standardization of syndrome differentiation and difficulties in quantifying TCM-specific indicators. Future studies need to optimize model evaluation systems by integrating modern technologies， in order to promote the standardization and internationalization of TCM andrology research.

Objective To construct a One Health-based performance evaluation indicator system for integrated interventions for schistosomiasis in the elimination phase, clarify the dimensions and connotations of intervention performance evaluation, and provide methodological reference for subsequent real-world performance evaluation in schistosomiasis control. Methods On the basis of literature analysis, a preliminary indicator system was developed through group discussion and expert consultation. Nineteen experts in schistosomiasis control were invited to participate in two rounds of Delphi consultation. Expert response rate, degree of consensus, coordination of opinions, and authority were assessed. The indicators were optimized and refined according to the scoring results and expert feedback, and the analytic hierarchy process was used to determine the normalized weights of indicators at each level. Results Through literature analysis, group discussion, and expert consultation, a preliminary performance evaluation indicator system for integrated interventions for schistosomiasis was developed, consisting of 4 first-level indicators, 8 second-level indicators, and 20 third-level indicators. The response rates of the two rounds of expert consultation were both 100.00%. In the first round, the ranges of the experts’ judgment coefficient, familiarity coefficient, and authority coefficient were 0.88 ~ 1.00, 0.74 ~ 0.99, and 0.82 ~ 0.99, respectively; in the second round, the corresponding ranges were 0.88 ~ 0.98, 0.73 ~ 0.98, and 0.81 ~ 0.98, respectively. In the first round, Kendall’s coefficients of concordance for the rationality, importance, and operability of the indicators were 0.201, 0.252, and 0.221, respectively; in the second round, the corresponding values were 0.267, 0.319, and 0.296, respectively. The Kendall’s coefficients of concordance in both rounds were statistically significant (χ² = 112.063, 140.415, 123.386, 121.939, 145.393, and 135.072, all P < 0.05). After optimization and revision, the final indicator system consisted of 4 first-level indicators, 7 second-level indicators, and 14 third-level indicators. Among the first-level indicators, the normalized weights of “health performance”, “economic performance”, “ecological performance”, and “social performance” were 40.44%, 13.88%, 11.67%, and 34.01%, respectively. Among the second-level indicators, the normalized weights of “disease burden level” and “promotion of health equity” were both 100.00%; those of “resource consumption” and “production capacity” were both 50.00%; and those of “water and soil regulation”, “atmospheric environmental regulation”, and “species conservation” were all 33.33%. Conclusions A One Health-based performance evaluation indicator system was constructed for integrated interventions for schistosomiasis in the elimination phase. The system incorporates performance evaluation indicators across multiple dimensions, including health, economic, ecological, and social domains, and provides a tool for evaluating the performance of cross-sector collaborative governance in integrated schistosomiasis interventions.

Objective:To discuss the effect of asiatic acid(AA)on the inflammation and oxidative stress damage induced by lipopolysaccharide(LPS)in the primary cultured hippocampus neurons,and to clarify its mechanism.Methods:The primary cultured rat hippocampus neurons(cell purity identified by immunofluorescence staining)were divided into control group,LPS(10 mg·L-1)group,and LPS+AA group(10 mg·L-1 LPS+10,20,and 40 μmol·L-1 AA),AA group(20 μmol·L-1 AA),ML385 group[10 μmol·L-1 nuclear factor erythroid 2-related factor(Nrf2)inhibitor],and LPS+ML385+AA group(10mg·L-1 LPS+10 μmol·L-1 ML385+20 μmol·L-1 AA).After drug treatment,methylthiazolyldiphenyl-tetrazolium bromide(MTT)method was used to detect the survival rates of the hippocampus neurons in various groups;lactate dehydrogenase(LDH)kit was used to detect the LDH leakage rates of the hippocampus neurons in various groups;enzyme linked immunosorbent assay(ELISA)kit was used to detect the expression levels of inflammatory factors[interleukin(IL)-1β and tumor necrosis factor(TNF)-α]and the activities of superoxide dismutase(SOD)and malondialdehyde(MDA)levels in the hippocampus neurons in various groups;Griess method was used to detect the nitric oxide(NO)levels in supernatant of the hippocampus neurons in various groups;immunofluorescence staining was used to detect the expressions of Nrf2 and heme oxygenase-1(HO-1)proteins in the hippocampus neurons in various groups;Western blotting method was used to detect the expression levels of Nrf2,HO-1,nuclear factor-kappa B(NF-κB),and B-cell lymphoma 2(Bcl-2)proteins in the hippocampus neurons in various groups.Results:Compared with control group,the survival rate of the hippocampus neurons,SOD activity,and Bcl-2 expression level in the cells in LPS group were significantly decreased(P＜0.01),while the LDH leakage rate,expression levels of IL-1β and TNF-α,MDA level,and NO level,as well as the expression level of NF-κB protein,were significantly increased(P＜0.01);the fluorescence intensities and expression levels of Nrf2 and HO-1 proteins in hippocampus neurons were significantly decreased(P＜0.01).Compared with LPS group,the survival rates of hippocampus neurons,SOD activities,and expression levels of Bcl-2 in the cells in LPS+10 μmol·L-1 AA group and 20 μmol·L-1 AA group were significantly increased(P＜0.01),while the LDH leakage rates,expression levels of IL-1β and TNF-α,MDA levels,and NO levels,as well as expression levels of NF-κB protein,were significantly decreased(P＜0.05 or P＜0.01),and the fluorescence intensities and protein expression levels of Nrf2 and HO-1 in the cells were significantly increased(P＜0.01).Compared with LPS+20 μmol·L-1 AA group,the fluorescence intensities of Nrf2 and HO-1 in the cells in LPS+ML385+AA group were significantly decreased(P＜0.01),and the expression levels of Nrf2 protein in the nucleus and cytoplasm,the expression levels of HO-1 and Bcl-2 proteins in the cells were significantly decreased(P＜0.01),while the expression level of NF-κB protein was significantly increased(P＜0.01).Conclusion:AA can improve LPS-induced inflammation and oxidative stress damage in the primary cultured rat hippocampus neurons,and its mechanism may be related to the activation of the Nrf2/HO-1 signaling pathway.

Ischemic stroke(IS)is a disease caused by insufficient blood and oxygen supply to cerebral vessels,with the main clinical manifestations of hemiplegia,sensory disturbance,aphasia,and ataxia.Studies have shown that hypoxia-inducible factor 1α(HIF-1α),as the core regulatory element of oxygen homeostasis,can be rapidly activated under hypoxia/ischemia conditions,thereby playing an important role in the pathophysiology of IS.In recent years,more and more articles have shown that the active components of tradi-tional Chinese medicine,Chinese patent drugs,and compound traditional Chinese medicine prescriptions can effectively regulate the HIF-1α-related signaling pathway in the treatment of IS,but there is no systematic summary on regulation of the HIF-1α signaling pathway in the treatment of IS.Therefore,this article mainly summarizes the structure and physiological activity of HIF-1α and its mechanisms of action in IS and reviews related studies on Chinese medicine monomers and compound prescriptions in the treatment of IS in the past five years in China and globally.It is pointed out that the Chinese medicine monomers and compound prescriptions can repair brain tissue,alleviate brain tissue damage,and exert a therapeutic effect on IS by regulating the HIF-1α pathway to promote an-giogenesis,inhibit neuroinflammation and oxidative stress,promote energy metabolism,and repair blood-brain barrier damage.

Ischemic stroke(IS)is a common cerebrovascular disease,and its pathogenesis is closely associated with brain cell death due to insufficiency of cerebral blood supply.In recent years,cell apoptosis has become a research hotspot in the pathogenesis of IS.The B-cell lymphoma-2(Bcl-2)family proteins associated with cell apoptosis are the key regulators for apoptosis and are mainly in-volved in the intrinsic apoptotic pathway,and they regulate cell apoptosis by mediating the two pathways of mitochondrial membrane permeability and Ca2? overload,thereby delaying the progression of IS.Starting from the anti-apoptotic mechanisms of Bcl-2 family proteins,this article summarizes the research advances in traditional Chinese medicine for the prevention and treatment of IS by regu-lating B-cell lymphoma-2 family proteins from the aspects of the structural features of Bcl-2 family proteins,their anti-apoptotic role in IS,and traditional Chinese medicine regulation.The results show that the anti-apoptotic strategies in IS mainly focus on the regula-tion of Bcl-2 and Bax proteins,and monomers of Chinese herbs,traditional Chinese medicine extracts,and compound traditional Chi-nese medicine prescriptions were used for treatment,which further confirms the great potential of traditional Chinese medicine in the prevention and treatment of IS and provides a theoretical basis for future experimental research and clinical treatment.

Objective To explore the reproductive health literacy of young female sailors,investigate the influence of long voyage on the health,and propose strategies on health education.Methods A total of 49 young female sailors were enrolled.Their healthy status was evaluated by annual medical records.The questionnaire survey was also conducted to evaluate general status related to reproductive health,knowledge pertaining to reproductive health,and health status after long voyage.Results Some female sailors had symptoms of thyroid diseases,such as hypothyroidism(32.65%),benign thyroid nodules(59.18%),and suspected papillary carcinoma(in one sailor,2.04%).Ultrasonography revealed breast nodules in 5 subjects(10.00%),mammary gland hyperplasia in 46 subjects(93.88%)and reproductive benign diseases in 6 subjects(12.24%).The physical and mental states significantly changed in the late stage of long voyage.The changes in work and living environment were the main factors affecting the emotional and psychological status in the long voyage.The discomfort in menstrual cycle piled up with the above-mentioned changes during a long voyage,and the menstruation significantly affected the duty of 36.73%of female sailors during a long voyage.Female sailors had little knowledge about the prevention and examination of breast cancer and thyroid cancer.Conclusion The living and work environment during a long voyage may affect the health of the thyroid,mammary gland and reproductive system of young female sailors.Active health education via online and offline ways is helpful to improve the health literacy and self-care of female sailors,subsequently enhancing the combat effectiveness.

OBJECTIVES: Accurate identification of risk factors for precocious puberty is essential for clinical diagnosis and management, yet the performance of natural language processing methods applied to unstructured electronic medical record (EMR) data remains to be fully evaluated. This study aims to assess the performance of a prompt engineering method for extracting individual risk factors of precocious puberty from EMRs. METHODS: Based on the capacity and role-insight-statement-personality-experiment (CRISPE) prompt framework, both simple and optimized prompts were designed to guide the large language model GLM-4-9B in extracting 10 types of risk factors for precocious puberty from 653 EMRs. Accuracy, precision, recall, and F1-score were used as evaluation metrics for the information extraction task. RESULTS: Under simple and optimized prompt conditions, the overall accuracy, precision, recall, and F1-score of the model were 84.18%, 98.09%, 81.99%, and 89.32% versus 97.15%, 98.31%, 98.16%, and 98.23%, respectively. The optimized prompts achieved more stable performance across age (<9 years vs ≥9 years) and visit-time (<2023 vs ≥2023) subgroups compared with simple prompts. The accuracy range for extracting each risk factor was 60.03%-97.24%, while with optimized prompts, the range improved to 92.19%-99.85%. The largest performance improvement occurred for "beverage intake" (60.03% vs 92.19%), and the smallest for "maternal age of menarche" (97.24% vs 99.23%). In comparing distributions among simple prompts, optimized prompts, and ground truth, statistically significant differences were observed for snack intake, beverage intake, soy milk intake, honey intake, supplement use, tonic use, sleep quality, and sleeping with the light on (all P<0.001), while exercise (P=0.966) and maternal menarche age (P=0.952) showed no significant differences. CONCLUSIONS Compared with simple prompts, optimized prompts substantially improved the extraction performance of individual risk factors for precocious puberty from EMRs, underscoring the critical role of prompt engineering in enhancing large language model performance.
Humans ; Puberty, Precocious/epidemiology* ; Risk Factors ; Electronic Health Records ; Female ; Child ; Natural Language Processing