AIM:To explore the effects of Jianpi Huatan formula on regulating T cells and helper T cells 17(Th17)cells in patients with polycystic ova-ry syndrome(PCOS)due to spleen deficiency and phlegm dampness syndrome,and conduct a model evaluation.METHODS:Ninety-two patients with spleen deficiency phlegm dampness syndrome(PCOS)admitted to our hospital from January 2023 to October 2024 were selected as the research sub-jects.Propensity score matching(PSM)method was used to match them in a 1:1 ratio,with 46 pa-tients in each group.The control group received conventional treatment,while the observation group received treatment with Jianpi Huatan for-mula on the basis of the control group.Compared and analyze the differences in clinical data and lab-oratory indicators between two groups;Compared the changes of sex hormone,glucose metabolism and TCM syndrome score before and after treat-ment in the two groups,and focused on the chang-es of regulatory T cells(Treg)and Th17 cells in the two groups before and after treatment;And used the Generalized Estimation Equation(GEE)model to analyze its improvement.Multiple linear regres-sion analysis was used to examine its correlation with the score of traditional Chinese medicine syn-drome.A time effect model of Jianpi Huatan formu-la for treating PCOS with spleen deficiency and phlegm dampness syndrome was established using a nonlinear mixed effects model.The fitting effect of the final model was evaluated through the good-ness of fit.Bootstrap was used to test and evaluate the stability of model parameters.Visual prediction testing was used to evaluate the predictive perfor-mance of the model.Typical time effect curves of traditional Chinese medicine symptom scores was simulated based on the final model for each base-line.RESULTS:After treatment,the total effective rate of the observation group was significantly high-er than that of the control group(χ2=4.842,P=0.028);Compared with before treatment,after 1months and 3 months of treatment,TC,TG,LDL-C,T,LH,FSH,AMH,FPG,FINS,HOMA-IR,the score of traditional Chinese medicine syndrome were sig-nificantly reduced,while E2 and HDL-C were signifi-cantly increased,and the improvement in the ob-servation group was significantly greater than that in the control group(P<0.05);The results of repeat-ed measures ANOVA showed significant difference-sin the time effects,inter group effects,and interac-tion effects of Treg,Th17,and Treg/Th17 between the two groups of patients(P<0.05).The GEE anal-ysis results showed that the improvement of Treg,Th17,and Treg/Th17 in the observation group were better than that in the control group(P<0.05);The results of multiple linear regression analysis showed that the levels of TC,TG,LDL-C,T,LH,FSH,AMH,FPG,FINS,HOMA-IR,Th17 were significantly positively correlated with TCM syndrome score,while the levels of E2,HDL-C,Treg,and Treg/Th17 were significantly negatively correlated with TCM syndrome score(P<0.05);The decrease in tradition-al Chinese medicine symptom score compared to baseline gradually increases over time,eventually reaching the pharmacological platform,which was consistent with the classic Emax model.After gradu-ally screening covariates,it was found that the baseline value of traditional Chinese medicine symptom score had a significant impact on the effi-cacy parameter Emax.The final model was Emax,i=15.42+1.21×(Baselinei-24.41).The goodness of fit results showed that the final model had a good fit-ting effect on the measured data.The model pa-rameters obtained from Bootstrap testing were very consistent with the original model,indicated that the model parameter estimation was robust.The visual prediction test results showed that the model had good predictive performance.The typi-cal efficacy time curve showed that the higher the baseline value of TCM symptom score,the greater the decrease in score.At 3 months of treatment,the TCM symptom score at each baseline basically decreased to below 10 points.CONCLUSION:The formula for strengthening the spleen and resolving phlegm can effectively improve the levels of Treg and Th17 in PCOS patients with spleen deficiency and phlegm dampness syndrome,and has good therapeutic effects,which is worthy of clinical appli-cation.

Objective:To investigate the prognostic factors for esophageal cancer after radical resection and the application value of machine learning prediction model.Methods:The retrospective cohort study was conducted. The clinicopatholigical data of 406 esophageal cancer patients who were admitted to Qilu Hospital of Shandong University from January 2018 to March 2022 were collected. There were 357 males and 49 females, aged (64±8)years. All patients underwent radical resection of esophageal cancer. The 406 patients were randomly divided into a training set of 285 cases and a validation set of 121 cases at a 7∶3 ratio based on a random number table. The training set was used to construct prediction model, and the validation set was used to validate prediction model. Patients were divided into high-risk group and low-risk group based on risk scores. Observation indicators: (1) follow-up of patients and analysis of influencing factors for prognosis; (2) construction and validation of machine learning prediction models. Comparison of measurement data with normal distribution between groups was conducted using the independent sample t test. Comparison of measurement data with skewed distribution between groups was conducted using the Mann-Whitney U test. Comparison of count data between groups was conducted using the chi-square test. Comparison of ordinal data between groups was conducted using the rank sum test. The Kaplan-Meier method was used to calculate survival rate and plot survival curve, and the Log-rank test was used for survival analysis. The Cox proportional hazard regression model was used for univariate and multivariate analyses. Independent influencing factors were included, and data processing, machine learning model construction, and visualization were performed using R packages including random survival forest (RSF), gradient boosting machine (GBM), least absolute shrinkage and selection operator Cox regression (LASSO-Cox), Cox proportional hazards model boosting (CoxBoost), survival support vector machine (survivalsvm), extreme gradient boosting (XGBoost), supervised principal component analysis (SuperPC), and Cox partial least squares regression (plsRcox). Receiver operating characteristic (ROC) curves were drawn, and sensitivity, specificity, and area under the curve (AUC) were calculated. The Delong test was used to assess the differences in AUC among different models in the training set, and the time-dependent ROC was used to compare the predictive performance of different models. Calibration curves were used to evaluate model accuracy, and decision curve analysis (DCA) was used to evaluate overall net benefit. Results:(1) Follow-up of patients and analysis of influencing factors for prognosis. All 406 patients were followed up postoperatively for 28(range, 6-36)months, with 1- and 3-year overall survival rate of 86.5% and 40.9%, respectively. The 285 patients in the training set were followed up postoperatively for 30(range, 6-36)months, with 1- and 3-year overall survival rate of 85.1% and 35.5%, respectively. The 121 patients in the validation set were followed up postoperatively for 25(range, 6-36)months, with 1- and 3-year overall survival rate of 87.0% and 43.2%, respectively. There was no significant difference in postoperative overall survival rate between the training set and the validation set ( χ2=3.20, P>0.05). Results of multivariate analysis showed that left thoracic surgical approach, preopera-tive neutrophil count, vascular invasion, perineural invasion, pathological T2-4 stage, pathological N2-3 stage, and postoperative pneumonia were independent risk factors affecting postoperative survival of 285 patients in the training set ( hazard ratio=1.466, 1.037, 1.482, 1.549, 5.268, 7.727, 22.202, 2.539, 2.686, 1.425, 95% confidence interval as 1.026-2.096, 1.003-1.073, 1.008-2.179, 1.105-2.170, 1.201-23.099, 1.833-32.576, 4.734-104.128, 1.577-4.087, 1.631-4.422, 1.018-1.994, P<0.05). (2) Construction and validation of machine learning prediction models. Independent risk factors affecting postoperative survival were included to construct RSF, GBM, LASSO-Cox, CoxBoost, survivalsvm, XGBoost, SuperPC, and plsRcox machine learning prediction models. Results of Delong test showed that there were significant differences in the AUC of RSF and GBM from the other six models ( P<0.05). Results of time-dependent ROC curve showed that all 8 machine learning predic-tion models had good discriminative ability in the training cohort, among which the RSF machine learning prediction model had the best predictive performance. Results of calibration curve showed that the RSF machine learning prediction model fitted well for predicting postoperative 1-, 2-, and 3-year overall survival in the training cohort, with high consistency with actual results. Results of decision curve analysis showed that within a threshold range of 0-0.80, the RSF machine learning prediction model provided a better overall net benefit. Further analysis showed that in the validation set, the AUC of RSF machine learning prediction model for postoperative 1-, 2-, and 3-year survival prediction were 0.786 (95% confidence interval as 0.609-0.962), 0.774 (95% confidence interval as 0.676-0.873), and 0.750 (95% confidence interval as 0.652-0.848), respectively. Results of calibration curve showed that the RSF machine learning prediction model fitted well for predicting postopera-tive 1-, 2-, and 3-year overall survival in the validation set, with high consistency with actual results. In the training set, the optimal cutoff value of the RSF machine learning prediction model risk score was 11.7. Patients with risk score ≥11.7 were classified as the high-risk group, and those with risk score <11.7 as the low-risk group. The median survival times of the two groups were 18.0 months and >36.0 months, respectively, showing a significant difference between them ( χ2=73.30, P<0.05). In the validation set, the optimal cutoff value of the RSF machine learning prediction model risk score was 11.7. Patients with risk score ≥11.7 were classified as the high-risk group, and those with risk score<11.7 as the low-risk group. The median survival times of the two groups were 17.0 months and>36.0 months for the high-risk and low-risk groups, respectively, showing a significant difference between them ( χ2=35.20, P<0.05). Conclusions:Left thoracic surgical approach, preoperative neutrophil count, vascular invasion, perineural invasion, pathological T2-4 stage, pathological N2-3 stage, and postoperative pneumonia are independent risk factors affecting survival of esophageal cancer patients after radical resection. The RSF machine learning prediction model constructed based on these factors can effectively distinguish the survival prognosis of high-risk and low-risk patients.

AIM:To explore the effects of Jianpi Huatan formula on regulating T cells and helper T cells 17(Th17)cells in patients with polycystic ova-ry syndrome(PCOS)due to spleen deficiency and phlegm dampness syndrome,and conduct a model evaluation.METHODS:Ninety-two patients with spleen deficiency phlegm dampness syndrome(PCOS)admitted to our hospital from January 2023 to October 2024 were selected as the research sub-jects.Propensity score matching(PSM)method was used to match them in a 1:1 ratio,with 46 pa-tients in each group.The control group received conventional treatment,while the observation group received treatment with Jianpi Huatan for-mula on the basis of the control group.Compared and analyze the differences in clinical data and lab-oratory indicators between two groups;Compared the changes of sex hormone,glucose metabolism and TCM syndrome score before and after treat-ment in the two groups,and focused on the chang-es of regulatory T cells(Treg)and Th17 cells in the two groups before and after treatment;And used the Generalized Estimation Equation(GEE)model to analyze its improvement.Multiple linear regres-sion analysis was used to examine its correlation with the score of traditional Chinese medicine syn-drome.A time effect model of Jianpi Huatan formu-la for treating PCOS with spleen deficiency and phlegm dampness syndrome was established using a nonlinear mixed effects model.The fitting effect of the final model was evaluated through the good-ness of fit.Bootstrap was used to test and evaluate the stability of model parameters.Visual prediction testing was used to evaluate the predictive perfor-mance of the model.Typical time effect curves of traditional Chinese medicine symptom scores was simulated based on the final model for each base-line.RESULTS:After treatment,the total effective rate of the observation group was significantly high-er than that of the control group(χ2=4.842,P=0.028);Compared with before treatment,after 1months and 3 months of treatment,TC,TG,LDL-C,T,LH,FSH,AMH,FPG,FINS,HOMA-IR,the score of traditional Chinese medicine syndrome were sig-nificantly reduced,while E2 and HDL-C were signifi-cantly increased,and the improvement in the ob-servation group was significantly greater than that in the control group(P<0.05);The results of repeat-ed measures ANOVA showed significant difference-sin the time effects,inter group effects,and interac-tion effects of Treg,Th17,and Treg/Th17 between the two groups of patients(P<0.05).The GEE anal-ysis results showed that the improvement of Treg,Th17,and Treg/Th17 in the observation group were better than that in the control group(P<0.05);The results of multiple linear regression analysis showed that the levels of TC,TG,LDL-C,T,LH,FSH,AMH,FPG,FINS,HOMA-IR,Th17 were significantly positively correlated with TCM syndrome score,while the levels of E2,HDL-C,Treg,and Treg/Th17 were significantly negatively correlated with TCM syndrome score(P<0.05);The decrease in tradition-al Chinese medicine symptom score compared to baseline gradually increases over time,eventually reaching the pharmacological platform,which was consistent with the classic Emax model.After gradu-ally screening covariates,it was found that the baseline value of traditional Chinese medicine symptom score had a significant impact on the effi-cacy parameter Emax.The final model was Emax,i=15.42+1.21×(Baselinei-24.41).The goodness of fit results showed that the final model had a good fit-ting effect on the measured data.The model pa-rameters obtained from Bootstrap testing were very consistent with the original model,indicated that the model parameter estimation was robust.The visual prediction test results showed that the model had good predictive performance.The typi-cal efficacy time curve showed that the higher the baseline value of TCM symptom score,the greater the decrease in score.At 3 months of treatment,the TCM symptom score at each baseline basically decreased to below 10 points.CONCLUSION:The formula for strengthening the spleen and resolving phlegm can effectively improve the levels of Treg and Th17 in PCOS patients with spleen deficiency and phlegm dampness syndrome,and has good therapeutic effects,which is worthy of clinical appli-cation.

OBJECTIVE To investigate the neuroprotective effects of Ditan Decoction(DTD)on ischemic stroke.METHODS A mouse middle cerebral artery occlusion(MCAO)model was used to induce cerebral ischemia and assess the role of DTD in post-stroke NVU injury.DTD was gavaged once a day for 3 days after MCAO.Transwell neutrophil chemotaxis assay was used to explore the role of DTD in the neutrophil chemotaxis.RESULTS In the MCAO model,DTD treatment significantly reduced infarct volume(P<0.01)and attenuated blood-brain barrier disruption,as evidenced by decreased IgG leakage and preserved laminin expression(P<0.05).Furthermore,DTD suppressed neutrophil infiltration into ischemic brain tissue,as demonstrated by reduced neutrophil elastase(P<0.01)and myeloperoxidase(P<0.05)levels.Mechanistically,DTD inhibited neutrophil chemotaxis in a dose-dependent manner and downregulated phosphodiesterase 4B(PDE4B),a key regulator of neutrophil migration(P<0.05).Molecular docking analysis i-dentified four active DTD components-apigenin,vitexin,chlorogenic acid,and orientin-with strong binding affinities to PDE4B(bind-ing energies<-5 kcal·mol-1),suggesting their potential role in mediating DTD's therapeutic effects.CONCLUSION These find-ings highlight DTD as a promising intervention for ischemic stroke,targeting NVU preservation and PDE4B-dependent neutrophil mod-ulation.

ObjectiveTo investigate the feasibility and safety of endoscopic retrograde cholangiopancreatography （ERCP） combined with oral cholangiopancreatography in the treatment of major duodenal papilla gallbladder polyps. MethodsA retrospective analysis was performed for the clinical data of eight patients with choledocholithiasis and gallbladder polyps who underwent ERCP and combined with oral cholangiopancreatography for major duodenal papilla cholecystectomy in Center of Digestive Endoscopy， Jilin People’s Hospital， from May 2022 to June 2024， and related data were collected， including the success rate of surgery， the technical success rate of gallbladder polyp removal， the superselective method of cystic duct， the time of operation， the time of gallbladder polyp removal， and surgical complications. ResultsBoth the success rate of surgery and the technical success rate of gallbladder polyp removal reached 100%， and of all eight patients， three patients used guide wire to enter the gallbladder under direct view， while five patients received oral cholangiopancreatography to directly enter the gallbladder. The time of operation was 51.88±12.34 minutes， and the time of gallbladder polyp removal was 23.13±10.94 minutes. The diameter of gallbladder polyp was 2‍ ‍—‍ ‍8 mm， and pathological examination showed inflammatory polyps in three patients， adenomatous polyps in one patient， and cholesterol polyps in four patients. There were no complications during or after surgery. The patients were followed up for 2‍ ‍—‍ ‍27 months after surgery， and no recurrence of gallbladder polyp was observed. ConclusionOral cholangiopancreatography is technically safe and feasible in endoscopic major duodenal papilla cholecystectomy.

Objective:To investigate the prognostic factors for esophageal cancer after radical resection and the application value of machine learning prediction model.Methods:The retrospective cohort study was conducted. The clinicopatholigical data of 406 esophageal cancer patients who were admitted to Qilu Hospital of Shandong University from January 2018 to March 2022 were collected. There were 357 males and 49 females, aged (64±8)years. All patients underwent radical resection of esophageal cancer. The 406 patients were randomly divided into a training set of 285 cases and a validation set of 121 cases at a 7∶3 ratio based on a random number table. The training set was used to construct prediction model, and the validation set was used to validate prediction model. Patients were divided into high-risk group and low-risk group based on risk scores. Observation indicators: (1) follow-up of patients and analysis of influencing factors for prognosis; (2) construction and validation of machine learning prediction models. Comparison of measurement data with normal distribution between groups was conducted using the independent sample t test. Comparison of measurement data with skewed distribution between groups was conducted using the Mann-Whitney U test. Comparison of count data between groups was conducted using the chi-square test. Comparison of ordinal data between groups was conducted using the rank sum test. The Kaplan-Meier method was used to calculate survival rate and plot survival curve, and the Log-rank test was used for survival analysis. The Cox proportional hazard regression model was used for univariate and multivariate analyses. Independent influencing factors were included, and data processing, machine learning model construction, and visualization were performed using R packages including random survival forest (RSF), gradient boosting machine (GBM), least absolute shrinkage and selection operator Cox regression (LASSO-Cox), Cox proportional hazards model boosting (CoxBoost), survival support vector machine (survivalsvm), extreme gradient boosting (XGBoost), supervised principal component analysis (SuperPC), and Cox partial least squares regression (plsRcox). Receiver operating characteristic (ROC) curves were drawn, and sensitivity, specificity, and area under the curve (AUC) were calculated. The Delong test was used to assess the differences in AUC among different models in the training set, and the time-dependent ROC was used to compare the predictive performance of different models. Calibration curves were used to evaluate model accuracy, and decision curve analysis (DCA) was used to evaluate overall net benefit. Results:(1) Follow-up of patients and analysis of influencing factors for prognosis. All 406 patients were followed up postoperatively for 28(range, 6-36)months, with 1- and 3-year overall survival rate of 86.5% and 40.9%, respectively. The 285 patients in the training set were followed up postoperatively for 30(range, 6-36)months, with 1- and 3-year overall survival rate of 85.1% and 35.5%, respectively. The 121 patients in the validation set were followed up postoperatively for 25(range, 6-36)months, with 1- and 3-year overall survival rate of 87.0% and 43.2%, respectively. There was no significant difference in postoperative overall survival rate between the training set and the validation set ( χ2=3.20, P>0.05). Results of multivariate analysis showed that left thoracic surgical approach, preopera-tive neutrophil count, vascular invasion, perineural invasion, pathological T2-4 stage, pathological N2-3 stage, and postoperative pneumonia were independent risk factors affecting postoperative survival of 285 patients in the training set ( hazard ratio=1.466, 1.037, 1.482, 1.549, 5.268, 7.727, 22.202, 2.539, 2.686, 1.425, 95% confidence interval as 1.026-2.096, 1.003-1.073, 1.008-2.179, 1.105-2.170, 1.201-23.099, 1.833-32.576, 4.734-104.128, 1.577-4.087, 1.631-4.422, 1.018-1.994, P<0.05). (2) Construction and validation of machine learning prediction models. Independent risk factors affecting postoperative survival were included to construct RSF, GBM, LASSO-Cox, CoxBoost, survivalsvm, XGBoost, SuperPC, and plsRcox machine learning prediction models. Results of Delong test showed that there were significant differences in the AUC of RSF and GBM from the other six models ( P<0.05). Results of time-dependent ROC curve showed that all 8 machine learning predic-tion models had good discriminative ability in the training cohort, among which the RSF machine learning prediction model had the best predictive performance. Results of calibration curve showed that the RSF machine learning prediction model fitted well for predicting postoperative 1-, 2-, and 3-year overall survival in the training cohort, with high consistency with actual results. Results of decision curve analysis showed that within a threshold range of 0-0.80, the RSF machine learning prediction model provided a better overall net benefit. Further analysis showed that in the validation set, the AUC of RSF machine learning prediction model for postoperative 1-, 2-, and 3-year survival prediction were 0.786 (95% confidence interval as 0.609-0.962), 0.774 (95% confidence interval as 0.676-0.873), and 0.750 (95% confidence interval as 0.652-0.848), respectively. Results of calibration curve showed that the RSF machine learning prediction model fitted well for predicting postopera-tive 1-, 2-, and 3-year overall survival in the validation set, with high consistency with actual results. In the training set, the optimal cutoff value of the RSF machine learning prediction model risk score was 11.7. Patients with risk score ≥11.7 were classified as the high-risk group, and those with risk score <11.7 as the low-risk group. The median survival times of the two groups were 18.0 months and >36.0 months, respectively, showing a significant difference between them ( χ2=73.30, P<0.05). In the validation set, the optimal cutoff value of the RSF machine learning prediction model risk score was 11.7. Patients with risk score ≥11.7 were classified as the high-risk group, and those with risk score<11.7 as the low-risk group. The median survival times of the two groups were 17.0 months and>36.0 months for the high-risk and low-risk groups, respectively, showing a significant difference between them ( χ2=35.20, P<0.05). Conclusions:Left thoracic surgical approach, preoperative neutrophil count, vascular invasion, perineural invasion, pathological T2-4 stage, pathological N2-3 stage, and postoperative pneumonia are independent risk factors affecting survival of esophageal cancer patients after radical resection. The RSF machine learning prediction model constructed based on these factors can effectively distinguish the survival prognosis of high-risk and low-risk patients.

OBJECTIVE To investigate the neuroprotective effects of Ditan Decoction(DTD)on ischemic stroke.METHODS A mouse middle cerebral artery occlusion(MCAO)model was used to induce cerebral ischemia and assess the role of DTD in post-stroke NVU injury.DTD was gavaged once a day for 3 days after MCAO.Transwell neutrophil chemotaxis assay was used to explore the role of DTD in the neutrophil chemotaxis.RESULTS In the MCAO model,DTD treatment significantly reduced infarct volume(P<0.01)and attenuated blood-brain barrier disruption,as evidenced by decreased IgG leakage and preserved laminin expression(P<0.05).Furthermore,DTD suppressed neutrophil infiltration into ischemic brain tissue,as demonstrated by reduced neutrophil elastase(P<0.01)and myeloperoxidase(P<0.05)levels.Mechanistically,DTD inhibited neutrophil chemotaxis in a dose-dependent manner and downregulated phosphodiesterase 4B(PDE4B),a key regulator of neutrophil migration(P<0.05).Molecular docking analysis i-dentified four active DTD components-apigenin,vitexin,chlorogenic acid,and orientin-with strong binding affinities to PDE4B(bind-ing energies<-5 kcal·mol-1),suggesting their potential role in mediating DTD's therapeutic effects.CONCLUSION These find-ings highlight DTD as a promising intervention for ischemic stroke,targeting NVU preservation and PDE4B-dependent neutrophil mod-ulation.

Objective To investigate the diagnostic value of Xpert MTB/RIF combined with metagenic high-throughput sequencing in bacterial negative tuberculosis.Methods Retrospective analysis of 70 patients diagnosed with bacterial negative pulmonary tuberculosis from December 2019 to May 2022 enrolled in the study.Xpert MTB/RIF testing,high-throughput metagenomic sequencing,and a combination of the two were performed,respectively.Solid culture and proportional methods were used to diagnose biochemical pathogens and analyze diagnostic efficacy.The receiver operating characteristic curve was drawn with the predicted value.Results The positive rate of Xpert MTB/RIF diagnosis was 75.57%,and the negative rate was 21.43%.The positive rate of high-throughput sequencing for metagenomics was 78.57%,while the negative rate was 21.43%;The positive rate of combined diagnosis was 88.57%,and the negative rate was 11.43%.The detection sensitivity of Xpert MTB/RIF was 77.55%,the specificity was 59.86%,and the area under the curve(AUC)was 0.827.The sensitivity of high-throughput sequencing for metagenomes was 77.47%,the specificity was 61.02%,and the AUC was 0.808.The sensitivity of the combined detection was 89.75%,the specificity was 89.57%,and the AUC was 0.925.The results showed that the diagnostic sensitivity and specificity of Xpert MTB/RIF combined with high-throughput metagenomic sequencing in bacterial negative pulmonary tuberculosis were higher than those of single detection(P<0.05).Conclusion Xpert MTB/RIF combined with metagenic high-throughput sequencing has good application value in the diagnosis of bacterial negative pulmonary tuberculosis,and is worthy of clinical application.

BACKGROUND:Sarcopenia refers to age-related progressive,systemic muscle mass reduction and/or muscle strength decline or muscle physiological function decline,which is related to the occurrence of a variety of adverse outcomes in older adults.Exercise is considered to be one of the main strategies for combating sarcopenia in older adults,but there is a lack of specific intervention methods of different exercise patterns to intervene in sarcopenia. OBJECTIVE:To elaborate the main influencing factors of sarcopenia and the research progress of different exercise methods to improve sarcopenia in older adults,providing reference and basis for combating sarcopenia in older adults. METHODS:Web of Science,PubMed,CNKI,VIP,WanFang databases were retrieved for relevant literature published from January 2000 to October 2023 using the keywords of"sarcopenia,sport,exercise intervention,resistant training,aerobic exercise,whole body vibration training,mixed training,physical performance,muscle strength,muscle mass"in Chinese and English,respectively.A total of 126 articles were included for review. RESULTS AND CONCLUSION:Resistance exercise is still the most effective way to prevent and treat senile sarcopenia,and the effect of high-intensity resistance exercise is more significant.However,in practical application,we should pay attention to the gradual increase of training load intensity.Aerobic exercise combined with resistance exercise is more effective to improve muscle mass and function in the elderly than a single exercise mode.It is suggested that older adults can carry out the transition of low-intensity aerobic exercise in the early stage and increase resistance exercise individually in the late stage.Whole body vibration training is a new treatment method for the prevention and treatment of senile sarcopenia,but particular attention should be paid to the effects of frequency,amplitude,and duration on patients during practical application.Multicomponent exercise combines different exercise modes,which can give full play to their respective advantages,so as to personalize exercise interventions.

Objective:To investigate the effects of compound Duzhong Jiangu Granules on joint function and gut microbiota in patients with Kashin-Beck disease.Methods:A single group pre- and post-experimental design was conducted, the patients with Kashin-Beck disease were selected as the subjects in Xunyi County, Xianyang City, Shaanxi Province; and treated with oral administration of compound Duzhong Jiangu Granules (12 g/bag, 1 bag/time, 3 times/day) for a period of 1 month. The improvement of joint function was evaluated using the joint dysfunction index scoring method before and after treatment. Morning stool samples of patients were collected and the changes in gut microbiota were analyzed before and after treatment using 16S rDNA sequencing technology.Results:A total of 87 patients with Kashin-Beck disease were included, including 44 males and 43 females; the age was (60.38 ± 7.12) years old, and the body mass index was (23.67 ± 3.59) kg/m 2. The comprehensive scores of joint dysfunction index for patients with Kashin-Beck disease before and after treatment were (7.27 ± 2.05) and (5.86 ± 2.01) points, respectively, and the difference was statistically significant ( t = 5.88, P < 0.001). The sequencing results of gut microbiota showed that there were statistically significant differences in the alpha diversity (chao1, observed species index) and beta diversity of gut microbiota in patients with Kashin-Beck disease before and after treatment ( Z = - 5.08, - 5.03, R = 0.09, P < 0.001). In the distribution of gut microbiota, Firmicutes was the dominant phylum, with relative abundances of 50.21% and 52.09% before and after treatment, respectively; the Bifidobacterium was the dominant bacterial genus, with relative abundances of 16.83% and 18.81% before and after treatment, respectively. At the genus level, a total of 17 gut microbiota genera were screened out, among which the relative abundances of Hafnia-Obesumbacterium, Gammaproteobacteria_unclassified, Acinetobacter, Pantoea, Leuconostoc, and Akkermanisia were significantly higher than before treatment ( Z = - 2.40, - 2.24, - 2.06, - 3.59, - 2.24, - 2.11, P < 0.05). The relative abundances of Dubosiella, Selenomonas, Anaeroplasma, Lachnospiraceae_ NK4A136_group, Rikenella, Prevotella, Megasphaera, Lactobacillus, Prevotella-9, Phascolarctobacterium, and Desulfovibrio were significantly lower than before treatment ( Z = - 9.38, - 2.61, - 2.18, - 8.43, - 2.45, - 2.46, - 2.49, - 7.29, - 2.29, - 2.55, - 2.08, P < 0.05). Conclusions:Compound Duzhong Jiangu Granules can effectively improve the joint function of patients with Kashin-Beck disease, and alter the diversity and richness of the gut microbiota community. It may reduce clinical symptoms in patients by regulating the structure of gut microbiota.