Childhood simple obesity has become a significant public health issue in China. Modern medicine primarily relies on lifestyle interventions and often suffers from poor long-term compliance， while pharmacological options are limited and associated with potential adverse effects. Traditional Chinese Medicine （TCM） has a long history in the prevention and management of this condition， demonstrating eight distinct advantages， including systematic theoretical foundation， diversified therapeutic approaches， definite therapeutic efficacy， high safety profile， good patient compliance， comprehensive intervention strategies， emphasis on prevention， and stepwise treatment protocols. Additionally， TCM is characterized by six distinctive features： the use of natural medicinal substances， non-invasive external therapies， integration of medicinal dietetics， simple exercise regimens， precise syndrome differentiation， and diverse dosage forms. By combining internal and external treatments， TCM facilitates individualized regimen adjustment and holistic regulation， demonstrating remarkable effects in improving obesity-related metabolic indicators， regulating constitutional imbalance， and promoting healthy behaviors. However， challenges remain， such as inconsistent operational standards， insufficient high-quality clinical evidence， and a gap between basic research and clinical application. Future efforts should focus on accelerating the standardization of TCM diagnosis and treatment， conducting multicenter randomized controlled trials， and fostering interdisciplinary integration， so as to enhance the scientific validity and international recognition of TCM in the prevention and treatment of childhood obesity.

ObjectiveTo investigate the therapeutic effects and mechanism of Shenqi Dihuang decoction （SQDHD） on diabetic kidney disease （DKD）， with a focus on its impact on arachidonic acid-related ferroptosis. MethodsSixty C57BL/6 mice were allocated into a normal group （n=10） and a modeling group （n=50）， with 43 mice successfully modeled. The successfully modeled mice were further allocated into model， low-， medium-， and high-dose （4.68， 9.36， and 18.72 g·kg^-1， respectively） SQDHD， and dapagliflozin （0.13 mg·kg^-1） groups. The drug treatment groups were administrated with corresponding agents by gavage， and the normal and model groups were administrated with equal volumes of normal saline by gavage. An electronic balance and a glucometer were used to monitor the body weight and fasting blood glucose level from the tail tip， respectively. Serum creatinine （Scr） and blood urea nitrogen （BUN） levels were measured by enzyme-linked immunosorbent assay （ELISA）. Histopathological changes in the renal tissue were assessed by hematoxylin-eosin staining， Masson staining， and periodic acid-Schiff （PAS） staining. The fluorescence intensity of reactive oxygen species （ROS） in frozen sections was observed by an inverted fluorescence microscope to evaluate the levels of ferrous ions （Fe²⁺） and lipid peroxidation in the renal tissue. Immunofluorescence staining of glutathione peroxidase 4 （GPX4） and acyl-CoA synthetase long-chain family member 4 （ACSL4） in the renal tissue was performed to detect their localization and expression. Western blot was employed to assess the expression levels of key ferroptosis proteins such as GPX4 and cystine/glutamate antiporter （xCT）， as well as the arachidonic acid metabolic pathway-related proteins， including ACSL4， lysophosphatidylcholine acyltransferase 3 （LPCAT3）， and arachidonate 15-lipoxygenase （ALOX15）. Real-time PCR was employed to measure the mRNA levels of key ferroptosis proteins， including solute carrier family 7 member 11 （SLC7A11） and GPX4， as well as arachidonic acid metabolism-related factors （ACSL4， LPCAT3， and ALOX15） in the renal tissue. ResultsCompared with the normal group， DKD model mice exhibited a decrease in body weight （P<0.01）， increases in levels of blood glucose （P<0.01）， 24-hour urinary protein， Scr， and BUN （P<0.01）， along with severe pathological changes， such as mesangial cell proliferation， basement membrane thickening， tubular atrophy， and interstitial inflammatory cell infiltration. In addition， the modeling elevated the levels of Fe²⁺， MDA， LPO， and ROS （P<0.01）， lowered the GPX4 and xCT levels （P<0.01）， raised the ACSL4， LPCAT3， and ALOX15 levels （P<0.01）， down-regulated the mRNA levels of GPX4 and SLC7A11 （P<0.01）， and up-regulated the mRNA levels of ACSL4， LPCAT3， and ALOX15 （P<0.01） in the renal tissue. Compared with the model group， low-， medium-， and high-dose SQDHD groups and the dapagliflozin group showed an increase in body weight （P<0.01）， decreases in levels of blood glucose （P<0.01）， 24-hour urinary protein， and Scr （P<0.01）， alleviated pathological changes in glomeruli and tubules， and reduced degree of glomerular and tubular fibrosis. The high-dose SQDHD group and the dapagliflozin group showed reductions in Fe²⁺， MDA， LPO， and ROS levels （P<0.01）. The medium- and high-dose SQDHD groups and the dapagliflozin group exhibited increased levels of GPX4 and xCT （P<0.01）， decreased levels of ACSL4， LPCAT3， and ALOX15 （P<0.05， P<0.01）， and down-regulated mRNA levels of ACSL4， LPCAT3， and ALOX15 （P<0.01）. ConclusionSQDHD ameliorates DKD by inhibiting ferroptosis potentially by reducing iron ion levels， inhibiting lipid peroxidation， up-regulating GPX4 expression， and down-regulating ACSL4 expression. This study provides new insights and a theoretical basis for the treatment of DKD with traditional Chinese medicine and identifies potential targets for developing novel therapeutics for DKD.

ObjectiveTo investigate the therapeutic effects and mechanism of Shenqi Dihuang decoction （SQDHD） on diabetic kidney disease （DKD）， with a focus on its impact on arachidonic acid-related ferroptosis. MethodsSixty C57BL/6 mice were allocated into a normal group （n=10） and a modeling group （n=50）， with 43 mice successfully modeled. The successfully modeled mice were further allocated into model， low-， medium-， and high-dose （4.68， 9.36， and 18.72 g·kg^-1， respectively） SQDHD， and dapagliflozin （0.13 mg·kg^-1） groups. The drug treatment groups were administrated with corresponding agents by gavage， and the normal and model groups were administrated with equal volumes of normal saline by gavage. An electronic balance and a glucometer were used to monitor the body weight and fasting blood glucose level from the tail tip， respectively. Serum creatinine （Scr） and blood urea nitrogen （BUN） levels were measured by enzyme-linked immunosorbent assay （ELISA）. Histopathological changes in the renal tissue were assessed by hematoxylin-eosin staining， Masson staining， and periodic acid-Schiff （PAS） staining. The fluorescence intensity of reactive oxygen species （ROS） in frozen sections was observed by an inverted fluorescence microscope to evaluate the levels of ferrous ions （Fe²⁺） and lipid peroxidation in the renal tissue. Immunofluorescence staining of glutathione peroxidase 4 （GPX4） and acyl-CoA synthetase long-chain family member 4 （ACSL4） in the renal tissue was performed to detect their localization and expression. Western blot was employed to assess the expression levels of key ferroptosis proteins such as GPX4 and cystine/glutamate antiporter （xCT）， as well as the arachidonic acid metabolic pathway-related proteins， including ACSL4， lysophosphatidylcholine acyltransferase 3 （LPCAT3）， and arachidonate 15-lipoxygenase （ALOX15）. Real-time PCR was employed to measure the mRNA levels of key ferroptosis proteins， including solute carrier family 7 member 11 （SLC7A11） and GPX4， as well as arachidonic acid metabolism-related factors （ACSL4， LPCAT3， and ALOX15） in the renal tissue. ResultsCompared with the normal group， DKD model mice exhibited a decrease in body weight （P<0.01）， increases in levels of blood glucose （P<0.01）， 24-hour urinary protein， Scr， and BUN （P<0.01）， along with severe pathological changes， such as mesangial cell proliferation， basement membrane thickening， tubular atrophy， and interstitial inflammatory cell infiltration. In addition， the modeling elevated the levels of Fe²⁺， MDA， LPO， and ROS （P<0.01）， lowered the GPX4 and xCT levels （P<0.01）， raised the ACSL4， LPCAT3， and ALOX15 levels （P<0.01）， down-regulated the mRNA levels of GPX4 and SLC7A11 （P<0.01）， and up-regulated the mRNA levels of ACSL4， LPCAT3， and ALOX15 （P<0.01） in the renal tissue. Compared with the model group， low-， medium-， and high-dose SQDHD groups and the dapagliflozin group showed an increase in body weight （P<0.01）， decreases in levels of blood glucose （P<0.01）， 24-hour urinary protein， and Scr （P<0.01）， alleviated pathological changes in glomeruli and tubules， and reduced degree of glomerular and tubular fibrosis. The high-dose SQDHD group and the dapagliflozin group showed reductions in Fe²⁺， MDA， LPO， and ROS levels （P<0.01）. The medium- and high-dose SQDHD groups and the dapagliflozin group exhibited increased levels of GPX4 and xCT （P<0.01）， decreased levels of ACSL4， LPCAT3， and ALOX15 （P<0.05， P<0.01）， and down-regulated mRNA levels of ACSL4， LPCAT3， and ALOX15 （P<0.01）. ConclusionSQDHD ameliorates DKD by inhibiting ferroptosis potentially by reducing iron ion levels， inhibiting lipid peroxidation， up-regulating GPX4 expression， and down-regulating ACSL4 expression. This study provides new insights and a theoretical basis for the treatment of DKD with traditional Chinese medicine and identifies potential targets for developing novel therapeutics for DKD.

Objective:To develop and validate risk prediction models for hypertension comorbidity in community-dwelling patients with type 2 diabetes mellitus(T2DM).Methods:The health records of 2 979 T2DM patients from two community health service centers in Fengtai District of Beijing from January 2023 to January 2024 were collected,including 2 591 cases from Fangzhuang Center(model development group) and 388 cases from Youanmen Center(external validation group). Patients in model development group were randomly assigned in a training set( n=1 813) and an internal validation set(778 cases) at a ratio of 7∶3. The risk factors associated with hypertention comorbidity in T2DM patients were identified with LASSO regression analysis,based on which risk prediction models was developed using six machine learning algorithms,including logistic regression(LR),classification and regression tree(CART),random forest(RF),extreme gradient boosting(XGB),support vector machine(SVM) and artificial neural network(ANN). The internal and external validations of the prediction models were conducted. Results:Among 2 979 patients with T2DM,2 158(72.44%) had concurrent hypertension,with 1 572 in the development set,280 in the internal validation set,306 in the external validation set. The LASSO regression identified 14 risk factors: age,educational level,occupation,medical insurance type,alcohol consumption,exercise frequency,BMI,SBP,TG/HDL-C,METS-IR,FBG,eGFR,duration of T2DM,and dyslipidemia. The nomogram model based on 14 predictive factors was constructed with XGB algorithm showed the best performance in predicting risk of hypertention for T2DM patients,showing the highest area under the curve(AUC) of 0.694(95% CI: 0.524-0.810) and effective calibration(Brier Score=0.121). Decision curve analysis confirmed the clinical utility of the predictive model. Conclusion:The risk prediction models based on machine learning algorithms have been developed in the study,which show good prediction perfomance for hiypertention comorbidity in community-dwelling T2DM patients.

Objective:To investigate depressive symptoms and associated factors among precariously em-ployed individuals(PEI).Methods:A total of 11 031 residents were selected by a combination of stratified sam-pling and quota sampling from 120 cities nationwide according to the proportion of the seventh national population census in 2021,including 1 829 PEI.New general self-efficacy scale,short-form of family health scale,12-item short-form health literacy questionnaire,Patient Health Questionaire-9items used to evaluate self-efficacy,family health,health literacy and depressive symptoms.among people with precarious employment.Results:The prevalence of depressive symptoms in PEI was 21.0％.Male gender(OR=1.30)was risk factors for depressive symptoms in PEI.Age 46 to 55 years(OR=0.39),junior middle school graduate(OR=0.46),and high family health(OR=0.90)were protective factors for depressive symptoms in PEI.The structural equation model showed that the family health scores were negatively correlated with the depression scores(β=-0.13,P＜0.001),while family health scores were positively correlated with health literacy scores(β=0.10,P＜0.01)and self-efficacy scores(β=0.66,P＜0.001).The self-efficacy scores were negatively correlated with the depression scores(β=-0.11,P＜0.001).Conclusion:The prevalence of depressive symptoms in PEI is high.Young age,male gender,high educa-tional level and lower family health are the risk factors of depression,while health literacy and self-efficacy play protective roles.

Objective To establish the HPLC analysis method for ketoconazole related substances,and to provide technical support for the improvement of the quality standard and the purity of ketoconazole.Methods Gradient elution conditions were optimized based on the chromatographic parameters outlined in the 2024"British Pharmacopoeia"for ketoconazole cream.A C18 column(4.6 mm×150 mm,3 μm)was used to facilitate the elution process using a gradient of acetonitrile and acetate buffer,and detection performed at a wavelength of 230 nm.Results The known impurities and potentially genotoxic impurities in the material drugs of ketoconazole can be more effectively separated using the established methods,which are both robust and highly sensitive.The content of the two potentially geno toxic impurities,six known impurities and unknown impurities in the 21 batches of ketoconazole sourced from five raw material pharmaceutical companies is below the specified limit requirements.Conclusion This method contributes to improving the quality of ketoconazole ingredients,ensuring the safety of ketoconazole preparations,and better supporting the regulatory supervision.

Objective:To investigate depressive symptoms and associated factors among precariously em-ployed individuals(PEI).Methods:A total of 11 031 residents were selected by a combination of stratified sam-pling and quota sampling from 120 cities nationwide according to the proportion of the seventh national population census in 2021,including 1 829 PEI.New general self-efficacy scale,short-form of family health scale,12-item short-form health literacy questionnaire,Patient Health Questionaire-9items used to evaluate self-efficacy,family health,health literacy and depressive symptoms.among people with precarious employment.Results:The prevalence of depressive symptoms in PEI was 21.0％.Male gender(OR=1.30)was risk factors for depressive symptoms in PEI.Age 46 to 55 years(OR=0.39),junior middle school graduate(OR=0.46),and high family health(OR=0.90)were protective factors for depressive symptoms in PEI.The structural equation model showed that the family health scores were negatively correlated with the depression scores(β=-0.13,P＜0.001),while family health scores were positively correlated with health literacy scores(β=0.10,P＜0.01)and self-efficacy scores(β=0.66,P＜0.001).The self-efficacy scores were negatively correlated with the depression scores(β=-0.11,P＜0.001).Conclusion:The prevalence of depressive symptoms in PEI is high.Young age,male gender,high educa-tional level and lower family health are the risk factors of depression,while health literacy and self-efficacy play protective roles.

Objective To establish the HPLC analysis method for ketoconazole related substances,and to provide technical support for the improvement of the quality standard and the purity of ketoconazole.Methods Gradient elution conditions were optimized based on the chromatographic parameters outlined in the 2024"British Pharmacopoeia"for ketoconazole cream.A C18 column(4.6 mm×150 mm,3 μm)was used to facilitate the elution process using a gradient of acetonitrile and acetate buffer,and detection performed at a wavelength of 230 nm.Results The known impurities and potentially genotoxic impurities in the material drugs of ketoconazole can be more effectively separated using the established methods,which are both robust and highly sensitive.The content of the two potentially geno toxic impurities,six known impurities and unknown impurities in the 21 batches of ketoconazole sourced from five raw material pharmaceutical companies is below the specified limit requirements.Conclusion This method contributes to improving the quality of ketoconazole ingredients,ensuring the safety of ketoconazole preparations,and better supporting the regulatory supervision.

Objective:To develop and validate risk prediction models for hypertension comorbidity in community-dwelling patients with type 2 diabetes mellitus(T2DM).Methods:The health records of 2 979 T2DM patients from two community health service centers in Fengtai District of Beijing from January 2023 to January 2024 were collected,including 2 591 cases from Fangzhuang Center(model development group) and 388 cases from Youanmen Center(external validation group). Patients in model development group were randomly assigned in a training set( n=1 813) and an internal validation set(778 cases) at a ratio of 7∶3. The risk factors associated with hypertention comorbidity in T2DM patients were identified with LASSO regression analysis,based on which risk prediction models was developed using six machine learning algorithms,including logistic regression(LR),classification and regression tree(CART),random forest(RF),extreme gradient boosting(XGB),support vector machine(SVM) and artificial neural network(ANN). The internal and external validations of the prediction models were conducted. Results:Among 2 979 patients with T2DM,2 158(72.44%) had concurrent hypertension,with 1 572 in the development set,280 in the internal validation set,306 in the external validation set. The LASSO regression identified 14 risk factors: age,educational level,occupation,medical insurance type,alcohol consumption,exercise frequency,BMI,SBP,TG/HDL-C,METS-IR,FBG,eGFR,duration of T2DM,and dyslipidemia. The nomogram model based on 14 predictive factors was constructed with XGB algorithm showed the best performance in predicting risk of hypertention for T2DM patients,showing the highest area under the curve(AUC) of 0.694(95% CI: 0.524-0.810) and effective calibration(Brier Score=0.121). Decision curve analysis confirmed the clinical utility of the predictive model. Conclusion:The risk prediction models based on machine learning algorithms have been developed in the study,which show good prediction perfomance for hiypertention comorbidity in community-dwelling T2DM patients.

Objective:We aimed to develop a novel visualized model based on nomogram to predict postoperative overall survival.Methods:This was a multicenter, retrospective, observational cohort study, including participants with histologically confirmed gastric and colorectal cancer who underwent radical surgery from 11 medical centers in China from August 1, 2015 to June 30, 2018. Baseline characteristics, histopathological data and nutritional status, as assessed using Nutrition Risk Screening 2002 (NRS 2002) score and the scored Patient-Generated Subjective Global Assessment, were collected. The least absolute shrinkage and selection operator regression and Cox regression were used to identify variables to be included in the predictive model. Internal and external validations were performed.Results:There were 681 and 127 patients in the training and validation cohorts, respectively. A total of 188 deaths were observed over a median follow-up period of 59 (range: 58 to 60) months. Two independent predictors of NRS 2002 and Tumor-Node-Metastasis (TNM) stage were identified and incorporated into the prediction nomogram model together with the factor of age. The model's concordance index for 1-, 3- and 5-year overall survival was 0.696, 0.724, and 0.738 in the training cohort and 0.801, 0.812, and 0.793 in the validation cohort, respectively.Conclusions:In this study, a new nomogram prediction model based on NRS 2002 score was developed and validated for predicting the overall postoperative survival of patients with gastric colorectal cancer. This model has good differentiation, calibration and clinical practicability in predicting the long-term survival rate of patients with gastrointestinal cancer after radical surgery.