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.

Background Work-related musculoskeletal disorders (WMSDs) are a major occupational health concern, particularly among workers exposed to adverse ergonomic conditions. Manganese production involves heavy physical demands, yet research on WMSDs among manganese workers remains limited. Objective To investigate the prevalence and influencing factors of WMSDs among manganese workers in a manganese enterprise in Guangxi. Methods A cross-sectional survey was conducted from May to June 2024 on workers at a manganese factory in Guangxi. The Chinese Musculoskeletal Disorders Questionnaire was used to collect information on demographic characteristics, distribution of musculoskeletal symptoms, and work-related exposures. χ² test was applied to compare differences in positive WMSDs rates across groups, and logistic regression analysis was performed to identify associated factors. Results A total of 1476 workers were enrolled in the study after pre-determined inclusion and exclusion criteria. The overall prevalence of WMSDs was 34.15%. The most commonly affected body regions were the lower back (17.28%), neck (16.67%), and shoulders (13.82%). The results of logistic regression analysis indicated that female, older age, and education level of college or above were associated with a higher risk of WMSDs (P<0.05). Awkward working postures were significantly associated with WMSDs in corresponding body regions; in particular, awkward postures of the neck, upper limbs, trunk, and lower limbs were related to an increased risk of WMSDs in multiple body sites (P<0.05). In addition, poor lighting conditions, high workplace temperature, frequent or sustained arm support during work, and high job demands were associated with an increased risk of overall or site-specific WMSDs (P<0.05). Conclusion The high prevalence of WMSDs among manganese workers is closely associated with demographic characteristics, working postures, and work environment and organizational factors. Targeted ergonomic interventions focusing on high-risk body regions and key ergonomic exposures are warranted to reduce the risk of WMSDs among manganese workers.

Objective To analyze survival outcomes and influencing factors among patients with colorectal cancer in Yunnan Province. Methods Clinical data were retrospectively collected from 4 892 patients with colorectal cancer. Survival data were obtained through follow-up. Overall survival (OS) was calculated by using the Kaplan-Meier method. Univariate analysis was performed by applying the log-rank test. Meanwhile, multivariate analysis employed the Cox proportional hazards regression model. Results The 1-, 3-, 5-, and 10-year OS rates for the entire cohort were 91.90%, 74.40%, 64.40%, and 28.70%, respectively. Univariate analysis revealed that age, ethnicity, region, differentiation grade, TNM stage, clinical stage, metastatic status, histological type, and treatment modality (chemotherapy, radiotherapy, and surgery) were associated with patient prognosis (all P<0.05). Multivariate analysis identified age (HR=1.250), region (HR=1.262), differentiation grade (HR=0.761), clinical stage (HR=3.128), and treatment modality (chemotherapy, HR=0.644; radiotherapy, HR=1.605; surgery, HR=0.384) as independent factors affecting survival prognosis in patients with colorectal cancer (all P<0.001). Conclusion Age, region, clinical stage, and treatment modality are independent factors influencing survival among patients with colorectal cancer in Yunnan Province. In clinical practice, these factors should be integrated to develop individualized prevention and treatment strategies, thereby improving patient outcomes.

Hemodynamic information is important for timely intervention of cardiovascular diseases and improving long-term prognosis.Based on cardiac MR(CMR),hemodynamic force(HDF)analysis could reflect the interaction between blood flow and myocardium,hence early evaluate abnormal hemodynamics and detect mild ventricular dysfunction.The method of CMR HDF and relative research progresses in cardiovascular diseases were reviewed in this article.

BACKGROUND:A large number of cell and animal tests have confirmed the effect of mesenchymal stem cells on improving ovarian function,and some clinical trials have been completed and their effectiveness has been preliminarily confirmed,bringing hope to women with premature ovarian failure.OBJECTIVE:To summarize and analyze the mechanism,research progress,and related clinical trials of mesenchymal stem cells from different sources in the treatment of premature ovarian failure in recent years,so as to provide a theoretical basis for further research and clinical application of mesenchymal stem cell therapy for premature ovarian failure.METHODS:Using "mesenchymal stem cells,premature ovarian failure" as keywords in Chinese and English,the relevant literature was searched in CNKI,WanFang Data,Chinese Medical Database,and PubMed database,and finally 72 articles that met the requirements were included for review.RESULTS AND CONCLUSION:Currently,there are seven types of commonly used mesenchymal stem cells for premature ovarian failure,which are umbilical cord mesenchymal stem cells,bone marrow mesenchymal stem cells,placental mesenchymal stem cells,menstrual blood mesenchymal stem cells,amniotic mesenchymal stem cells,amniotic fluid mesenchymal stem cells,and adipose mesenchymal stem cells.The mechanisms include inhibiting apoptosis and promoting proliferation,anti-inflammatory and inhibiting oxidative stress,homing,promoting angiogenesis,anti-fibrosis,parasecretory,immune regulation,autophagy,and improving microenvironment.Cell and animal experiments have proven that different sources of mesenchymal stem cells can have better intervention effect on premature ovarian failure through various mechanisms,and delay the progress of premature ovarian failure to a certain extent.If it can be successfully applied to the clinic in the future,it can alleviate the psychological and physical pain of patients to a great extent.However,due to the lack of comprehensive and accurate evidence in clinical studies such as stem cell source,administration mode and dose,adverse reactions,etc.,further studies are still needed to confirm it in the future,and its long-term safety needs to be further observed.

Objective To analyze the potential biological relationship between Alzheimer disease(AD)and chronic periodontitis(CP)by bioinformatics.Methods We analyzed two datasets of AD and CP from the Gene Expression Omnibus database,and differentially expressed genes(DEGs)were identified from these datasets.Weighted gene co-expression network analysis(WGCNA)was used to identify the most relevant modules.Functional enrichment analysis of shared genes was performed,and a protein-protein interaction(PPI)network was constructed.MCODE was used to identify key modules,and machine learning was used to discover key genes.In addition,Gene Set Enrichment Analysis(GSEA)and CIBERSORT were used to investigate KLRB1-related molecular pathways and immune cell distribu-tion.Results The study identified 18 genes common to both AD and CP.MCODE analysis revealed five pivotal genes,machine learning identified 13 genes for AD,eight for CP,and KLRB1 was determined as a common gene for AD and CP.GSEA demonstrated the intricate involvement of these genes in disease-related pathways.Immunocellular analysis underscored a significant association between KLRB1 and γδT cells.Conclusion This research highlighted KLRB1 as a key gene for AD and CP,providing new insights into their molecular linkages.

Objective:To investigate the mediating effect of psychological flexibility between recurrence risk perception and health behavior in stroke patients.Methods:From July to December 2024, 233 stroke patients at the First Affiliated Hospital of Zhengzhou University were selected using convenience sampling. Electronic questionnaires were used to collect patients' general information, perception of stroke recurrence risk, health behavior, and psychological flexibility.Results:The scores for recurrence risk perception, psychological flexibility, and health behavior of 233 stroke patients were (39.75±4.39), (47.45±4.19), and (54.04±3.78), respectively. Health behavior were positively correlated with recurrence risk perception ( r=0.495, P<0.01), and negatively correlated with psychological flexibility ( r=-0.367, P<0.01). Psychological flexibility partially mediated the relationship between recurrence risk perception and health behavior, with an effect value of 0.080 and an effect proportion of 17.5% (0.080/0.458) . Conclusions:Recurrence risk perception not only directly predicts health behavior in stroke patients but also indirectly influences their health behavior through psychological flexibility. Healthcare providers should enhance recurrence risk perception among stroke patients and incorporate the improvement of psychological flexibility as part of intervention strategies to improve patients' health behavior.

Objective:To observe the changes in serum estradiol (E 2) levels in pregnant women of different gestational weeks and their predictive value for early intrauterine pregnancy outcomes. Methods:A retrospective study was conducted involving 375 pregnant women who were treated at the Affiliated Yangming Hospital of Ningbo University (Yuyao People's Hospital) from September 2021 to September 2023. The clinical data were categorized based on pregnancy outcomes into three groups: a normal pregnancy group ( n = 150), a threatened miscarriage with continued pregnancy group ( n = 150), and a miscarriage group ( n = 75). The serum estradiol (E 2) levels at different gestational weeks were compared among the three groups: 5 to < 6 weeks (35-41 days), 6 to < 7 weeks (42-48 days), and 7 to 8 weeks (49-55 days). The predictive value of serum E 2 levels for early intrauterine pregnancy outcomes across different gestational weeks was analyzed using Receiver Operating Characteristic (ROC) curves. Results:In the normal pregnancy group, the serum E 2 levels at different gestational weeks were as follows: (1 691.87 ± 532.21) pmol/L for 5 to < 6 weeks, (2 376.64 ± 788.36) pmol/L for 6 to < 7 weeks, and (3 576.30 ± 1,190.06) pmol/L for 7 to 8 weeks. These values were significantly higher than those in the threatened miscarriage with continued pregnancy group [(1 409.28 ± 473.49) pmol/L, (1 893.13 ± 563.15) pmol/L, (2 035.79 ± 612.47) pmol/L, t = 5.15, 11.68, 6.60, all P < 0.05] and the miscarriage group [(906.49 ± 338.09) pmol/L, (923.63 ± 365.39) pmol/L, (950.27 ± 378.89) pmol/L, t = 16.19, 15.45, 21.50, all P < 0.05]. The serum E 2 levels at different gestational weeks in the threatened miscarriage with continued pregnancy group were significantly higher than those in the miscarriage group ( t = 7.48, 10.81, 8.89, all P < 0.05). Both the normal pregnancy group and the threatened miscarriage with continued pregnancy group showed an increasing trend in serum E 2 levels with advancing gestational weeks ( t = 6.74, 18.55, 7.58, 9.82, 11.81, 2.24, all P < 0.05). In contrast, the serum E 2 levels in the miscarriage group also increased with advancing gestational weeks, but the differences were not statistically significant ( P > 0.05). The results from the receiver operating characteristic curve analysis indicated that the areas under the curve for predicting early intrauterine pregnancy outcomes based on serum E 2 levels at different gestational weeks were 0.857, 0.810, and 0.839, demonstrating excellent diagnostic efficacy. Conclusions:Dynamic monitoring of serum E 2 levels is beneficial for predicting early intrauterine pregnancy outcomes and providing guidance for clinical diagnosis and treatment.

Hemodynamic information is important for timely intervention of cardiovascular diseases and improving long-term prognosis.Based on cardiac MR(CMR),hemodynamic force(HDF)analysis could reflect the interaction between blood flow and myocardium,hence early evaluate abnormal hemodynamics and detect mild ventricular dysfunction.The method of CMR HDF and relative research progresses in cardiovascular diseases were reviewed in this article.