BACKGROUND:Alzheimer's disease has been associated with sarcopenia,but a causal relationship has not been established.Exploring the causal relationship between the two most common disability-burdening diseases in the aging population-Alzheimer's disease and sarcopenia-and their potential mediating factors holds certain implications for further alleviating the healthcare costs and socioeconomic burden for older adults in China.OBJECTIVE:To explore the potential causal relationship between Alzheimer's disease and sarcopenia in the general population using a Mendelian randomization study and to explore the role of body mass index in this context.METHODS:Two-sample Mendelian randomization analysis based on published genome-wide association studies(GWAS)were used to infer causality,and univariate Mendelian randomization and mediation analyses were used in the study design.Through the Integrative Epidemiology Unit(IEU)database,ieu-b-2 was selected as the Alzheimer's disease dataset(sample size:63 926),ieu-b-4816 as the body mass index dataset(99 998),ebi-a-GCST90000027 as the appendicular lean mass dataset(244 730),ukb-b-7478 as the left hand grip strength dataset(461 026),ukb-b-10215 as the right hand grip strength dataset(461 089)and ukb-b-4711 as the walking pace dataset(459 915).Inverse-variance weighting was used as the primary analysis method,and the results were validated by pleiotropy and heterogeneity analysis.The Steiger Directionality Test was performed to validate the reasonableness of the causal direction.RESULTS AND CONCLUSION:(1)The Mendelian randomization analyses provided evidence that Alzheimer's disease predicted the risk of appendicular lean mass[odds ratio(OR)=1.009;95％confidence interval(Cl),1.001-1.017;P=0.023),and walking pace(OR=1.010;95％Cl,1.003-1.017;P=0.008).No correlation with hand grip strength was observed.(2)Alzheimer's disease was negatively correlated with body mass index(OR=0.893;95％Cl,0.811-0.984;P=0.022);body mass index was positively correlated with appendicular lean mass(OR=1.084;95％Cl,1.031-1.141;P=0.002)and negatively correlated with walking pace(OR=0.975;95％Cl,0.969-0.980;P＜0.001).(3)Mediation analyses showed that the causal relationship between Alzheimer's disease and appendicular lean mass and walking pace was partially mediated by body mass index,with the proportion of mediations being 50.25％and 32.11％,respectively.(4)The results of this study suggest that based on large-scale population studies,genetic prediction of Alzheimer's disease is a potential risk factor for sarcopenia,in which body mass index plays an important mediating role.This suggests that in clinical practice,attention should be paid to the muscle condition of patients with Alzheimer's disease,and weight management should be implemented,as maintaining a body mass index within the normal high range may have a preventive effect on the occurrence of sarcopenia in patients with Alzheimer's disease.However,further research is needed to verify the applicability of this conclusion to other ethnic groups.This study utilized an international public database for analysis,providing a reference for research on the correlation between Alzheimer's disease and sarcopenia in the Chinese population.It also highlights the significant mediating role of body mass index,offering insights for further prevention and treatment of sarcopenia among Chinese individuals.

BACKGROUND:Alzheimer's disease has been associated with sarcopenia,but a causal relationship has not been established.Exploring the causal relationship between the two most common disability-burdening diseases in the aging population-Alzheimer's disease and sarcopenia-and their potential mediating factors holds certain implications for further alleviating the healthcare costs and socioeconomic burden for older adults in China.OBJECTIVE:To explore the potential causal relationship between Alzheimer's disease and sarcopenia in the general population using a Mendelian randomization study and to explore the role of body mass index in this context.METHODS:Two-sample Mendelian randomization analysis based on published genome-wide association studies(GWAS)were used to infer causality,and univariate Mendelian randomization and mediation analyses were used in the study design.Through the Integrative Epidemiology Unit(IEU)database,ieu-b-2 was selected as the Alzheimer's disease dataset(sample size:63 926),ieu-b-4816 as the body mass index dataset(99 998),ebi-a-GCST90000027 as the appendicular lean mass dataset(244 730),ukb-b-7478 as the left hand grip strength dataset(461 026),ukb-b-10215 as the right hand grip strength dataset(461 089)and ukb-b-4711 as the walking pace dataset(459 915).Inverse-variance weighting was used as the primary analysis method,and the results were validated by pleiotropy and heterogeneity analysis.The Steiger Directionality Test was performed to validate the reasonableness of the causal direction.RESULTS AND CONCLUSION:(1)The Mendelian randomization analyses provided evidence that Alzheimer's disease predicted the risk of appendicular lean mass[odds ratio(OR)=1.009;95％confidence interval(Cl),1.001-1.017;P=0.023),and walking pace(OR=1.010;95％Cl,1.003-1.017;P=0.008).No correlation with hand grip strength was observed.(2)Alzheimer's disease was negatively correlated with body mass index(OR=0.893;95％Cl,0.811-0.984;P=0.022);body mass index was positively correlated with appendicular lean mass(OR=1.084;95％Cl,1.031-1.141;P=0.002)and negatively correlated with walking pace(OR=0.975;95％Cl,0.969-0.980;P＜0.001).(3)Mediation analyses showed that the causal relationship between Alzheimer's disease and appendicular lean mass and walking pace was partially mediated by body mass index,with the proportion of mediations being 50.25％and 32.11％,respectively.(4)The results of this study suggest that based on large-scale population studies,genetic prediction of Alzheimer's disease is a potential risk factor for sarcopenia,in which body mass index plays an important mediating role.This suggests that in clinical practice,attention should be paid to the muscle condition of patients with Alzheimer's disease,and weight management should be implemented,as maintaining a body mass index within the normal high range may have a preventive effect on the occurrence of sarcopenia in patients with Alzheimer's disease.However,further research is needed to verify the applicability of this conclusion to other ethnic groups.This study utilized an international public database for analysis,providing a reference for research on the correlation between Alzheimer's disease and sarcopenia in the Chinese population.It also highlights the significant mediating role of body mass index,offering insights for further prevention and treatment of sarcopenia among Chinese individuals.

This study was aimed at investigating infections with Giardia and Cryptosporidium in Ochotona curzoniae in Zoige County,Sichuan Province.O.curzoniae were captured in five townships of Zoige County(Dazhasi,Axi,Hongxing,Tangke,and Maixi)between March and December of 2023.DNA from the gastrointestinal contents was subjected to nested PCR to amplify Giardia bg,gdh,and tpi genes,and the Cryptosporidium SSU rRNA gene.The sequences of PCR-PCR products were analyzed and compared.Phylogenetic trees were constructed to determine the protozoa species and genotypes.A total of 114 O.curzoniae animals were captured,among which 44 samples showed bg gene positivity,and 14 samples showed gdh gene positivity for Giardia.The total detection rate was 43.9％(50/114),and two assemblages were detected(assem-blage E and a new assemblage tentatively termed assemblage OC1);the positivity rate for Cryptosporidium was 7.0％(8/114),and three new genotypes were observed.Mixed infection with Cryptosporidium and Giardia was present in some sam-ples,with a detection rate of 3.5％(4/114).Giardia lamblia and Giardia sp.(REG-1,REG-2)were prevalent in O.curzoni-ae in Zoige County in Sichuan province;assemblage E was the dominant assemblage,and the new assemblage OC1 was pres-ent;and Cryptosporidium sp.(REG-1,REG-2,and REG-3)were identified.In summary,future monitoring of Giardia and Cryptosporidium should be further strengthened in Zoige to provide detailed data for promoting local public health.

Objective:To develop and validate machine learning models for predicting the cumulative live birth rate (CLBR) following in vitro fertilization (IVF) and to analyze key predictive features using SHAP values. Methods:This retrospective study included data from patients who underwent IVF-embryo transfer at the Department of Reproductive Medicine, Baoding Maternal and Child Health Hospital, between January 2017 and December 2022. Patients were categorized into two groups based on live birth outcome: the live birth group ( n=1 036) and the non-live birth group ( n=756). The dataset was randomly divided into a training set and a validation set in a ratio of 7∶3. Five algorithms were utilized for model development: logistic regression, random forest, extreme gradient boosting (XGBoost), support vector machine, and neural networks. Model performance was assessed using the area under the receiver operating characteristic (AUC) curve, F1 score, and calibration curves. Clinical decision curve analysis (DCA) was employed to evaluate the clinical utility of the models. SHAP values were used to interpret feature importance in the XGBoost model and enhance its explainability. Results:The XGBoost model demonstrated the best performance in predicting CLBR,with accuracy of 72.44%, AUC of 0.775, and F1 score of 0.654, accuracy and F1 score outperforming logistic regression (accuracy was 70.02%, F1 score was 0.585), random forest (accuracy was 71.69%, F1 score was 0.606), support vector machine (accuracy was 70.20%, F1 score was 0.607), and neural network (accuracy was 68.72%, F1 score was 0.560). The calibration curve of XGBoost closely aligned with the diagonal line, indicating that the predicted probabilities were very close to the actual outcomes, demonstrating good calibration. DCA indicated that the XGBoost model provided higher net benefits across a wide range of clinical decision thresholds. SHAP value analysis identified number of previous IVF failures, antral follicle count, anti-Müllerian hormone level, percentage of normal sperm morphology, and sperm DNA fragmentation index as key predictors of CLBR.Conclusion:The XGBoost model exhibits excellent predictive performance and calibration for CLBR, with SHAP values providing important insights into feature importance. This model has the potential to support the development of personalized treatment strategies in clinical practice. However, its generalizability needs to be validated using external datasets to ensure its applicability to diverse populations.

Di-(2-ethylhexyl)phthalate(DEHP)is an environmental pollutant commonly found in plastic products and has toxic effects on female reproductive system.DEHP can interfere with the synthesis of progesterone,testosterone and estradiol through female hypothalamic-pituitary-ovarian axis,aggravate insulin resistance and obesity by affecting glucose and lipid metabolism,and cause ovarian damage through inducing oxidative stress,excessive autophagy and pyroptosis of oocyte or granulosa cells.It can also alter epigenetic genes relating to follicular development and prevent follicles from mature.These factors are closely contribute to the pathogenesis of polycystic ovary syndrome.We systematically summarizes the mechanism of DEHP interfering with ovarian function and inducing polycystic ovary syndrome,in order to provide help for the prevention and treatment of female reproductive injury from environmental pollutant.

Objective:To develop and validate machine learning models for predicting the cumulative live birth rate (CLBR) following in vitro fertilization (IVF) and to analyze key predictive features using SHAP values. Methods:This retrospective study included data from patients who underwent IVF-embryo transfer at the Department of Reproductive Medicine, Baoding Maternal and Child Health Hospital, between January 2017 and December 2022. Patients were categorized into two groups based on live birth outcome: the live birth group ( n=1 036) and the non-live birth group ( n=756). The dataset was randomly divided into a training set and a validation set in a ratio of 7∶3. Five algorithms were utilized for model development: logistic regression, random forest, extreme gradient boosting (XGBoost), support vector machine, and neural networks. Model performance was assessed using the area under the receiver operating characteristic (AUC) curve, F1 score, and calibration curves. Clinical decision curve analysis (DCA) was employed to evaluate the clinical utility of the models. SHAP values were used to interpret feature importance in the XGBoost model and enhance its explainability. Results:The XGBoost model demonstrated the best performance in predicting CLBR,with accuracy of 72.44%, AUC of 0.775, and F1 score of 0.654, accuracy and F1 score outperforming logistic regression (accuracy was 70.02%, F1 score was 0.585), random forest (accuracy was 71.69%, F1 score was 0.606), support vector machine (accuracy was 70.20%, F1 score was 0.607), and neural network (accuracy was 68.72%, F1 score was 0.560). The calibration curve of XGBoost closely aligned with the diagonal line, indicating that the predicted probabilities were very close to the actual outcomes, demonstrating good calibration. DCA indicated that the XGBoost model provided higher net benefits across a wide range of clinical decision thresholds. SHAP value analysis identified number of previous IVF failures, antral follicle count, anti-Müllerian hormone level, percentage of normal sperm morphology, and sperm DNA fragmentation index as key predictors of CLBR.Conclusion:The XGBoost model exhibits excellent predictive performance and calibration for CLBR, with SHAP values providing important insights into feature importance. This model has the potential to support the development of personalized treatment strategies in clinical practice. However, its generalizability needs to be validated using external datasets to ensure its applicability to diverse populations.

Refined operational management is crucial to enhance hospital operational efficiency and achieve sustainable and high-quality development.A large tertiary comprehensive hospital,selected as an observation subject in this study,has har-nessed the value inherent in its extensive operational data,thereby constructing a data-driven operational management framework.Through measures such as developing an operational management system,setting up an operational data hub,and optimizing re-source allocation based on data modeling,this hospital has conducted comprehensive operational analysis at the hospital,depart-ment,and project levels,established a performance management system grounded in data,and enhanced risk prevention and con-trol capabilities,investigating the refined operational management.These efforts have led to incremental improvements in hospital quality,continuous enhancements in operational efficiency,and significant increases in patient and employee satisfaction.

Objective:To explore the effect of 3D printing extravascular stent implantation on the hemodynamics of the relevant large vessels in patients with nutcracker syndrome（NCS），and to discuss the clinical safety of this new procedure.Methods:A total of 30 NCS patients admitted to Tangdu Hospital Affiliated to Air Force Military Medical University from May to December 2023 were prospectively included. All the patients were received laparoscopic 3D printing extravascular stent implantation for NCS treatment. Whether the lumbago，abdominal pain，hematuria，proteinuria and varicocele had improved after the stent implantation were observed. The inner diameter，blood flow velocity of left renal vein，abdominal aorta，superior mesenteric artery and the diameter of inferior vena cava were measured by ultrasound 1 day before surgery and 8 days after surgery，respectively.In addition，the carotid-femoral pulse wave velocity（cfPWV）was measured by a newly developed method，by which the arterial stiffness could be detected based on Doppler ultrasound. The blood flow Doppler spectra of each subject were recorded at the right common carotid artery and the right common femoral artery，respectively，then based on that，the cfPWV was automatically calculated by this new technique. The above internal diameters，blood flow velocity and cfPWV were compared before and after the laparoscopic 3D printing extravascular stent implantation.Results:Among the 30 patients，the postoperative blood flow velocity at the left renal vein compression site was significantly lower than that before surgery［（50.7 ± 14.8）cm/s vs（122.1 ± 24.1）cm/s， P<0.001）］，and the clinical symptoms，including lumbago and abdominal pain，hematuria，proteinuria and varicocele were significantly improved（all P<0.05）. There were no significant changes in the internal diameter，blood flow velocity of abdominal aorta，superior mesenteric artery and the diameter of inferior vena cava，and cfPWV before and after operation（all P>0.05）. Conclusions:The 3D printing extravascular stent implantation through laparoscope can effectively improve left renal vein compression in patients with NCS，and cause no significant change in large arterial hemodynamics，suggesting that this surgical method is a safe and reliable treatment for patients with NCS.

Body weight abnormalities, including overweight, obesity, and underweight, have become a dual public health challenge in Chinese adults: overweight and obesity lead to a variety of chronic complications, while underweight increases the risks of malnutrition, sarcopenia, and organ dysfunction. To systematically address these issues, multidisciplinary experts in endocrinology, sports science, nutrition, and psychiatry from various regions have held multiple weight management seminars. Based on the latest epidemiological data and clinical evidence, they expanded the guideline to include assessment and intervention strategies for underweight, in addition to the core content of obesity management. This guideline outlines the etiological mechanisms, evaluation methods, and multidimensional management strategies for overweight and obesity, covering key areas such as diagnosis and assessment, medical nutrition therapy, exercise prescription, pharmacological intervention, and psychological support. It is intended to provide a scientific and standardized approach to weight management across the adult population, aiming to curb the rising prevalence of obesity, mitigate complications associated with abnormal body weight, and improve nutritional status and overall quality of life.

Objective:To investigate the disease burden, clinical characteristics and independent risk factors affecting in-hospital outcomes of children with congenital heart disease (CHD) combined with heart failure (HF) in China.Methods:(1) Descriptive study: based on the global burden of disease study 2021, available data on children under 15 years of age with CHD and HF in China from 1990 to 2021 were collected. The prevalence and trends in different age subgroups (<1 year, 1-<2 years, 2-<5 years, 5-<10 years, 10-<15 years) were analyzed, and the annual percentage change (EAPC) was estimated using linear regression. (2) Retrospective cohort study: a total of 1 062 children with CHD and HF from a multicenter study on pediatric HF in China were included. The children were divided into two groups:<2 years group and 2-<18 years group. Data on demographics, clinical features, diagnosis, treatments, and in-hospital outcomes were analyzed. Mann-Whitney U test and chi-square test were used for group comparisons.Multivariable Logistic regression was applied to identify factors influencing outcomes (in-hospital mortality and adverse cardiovascular events). Results:(1) From 1990 to 2021, the number of children with CHD and HF in China increased from 333 000 (95% uncertainty interval ( UI) 271 000-405 000) to 368 000 (95% UI 296 000-459 000), a growth of 10.8% (95% UI 5.0%-16.6%). Concurrently the prevalence rate increased from 104.5 (95% UI 85.1-127.3) per 100 000 to 142.0 (95% UI 114.0-176.8) per 100 000, a growth of 35.9% (95% UI 28.7%-43.0%), with an EAPC of 1.5% (95% CI 1.2%-1.8%). Although the number of cases in the<1 year and 1-<2 years groups decreased by 41.0% and 25.6%, respectively, the prevalence in all age groups showed an upward trend:<1 year EAPC 0.6% (95% CI 0.5%-0.7%); 1-<2 years EAPC 0.9% (95% CI 0.8%-1.0%); 2-<5 years EAPC 1.2% (95% CI 1.0%-1.4%); 5-<10 years EAPC 1.5% (95% CI 1.2%-1.8%); 10-<15 years EAPC 2.1% (95% CI 1.9%-2.3%). (2) The multicenter study revealed that among 1 062 hospitalized children, 528 (49.7%) were male and 534 (50.3%) were female, with the age at admission of 5.4 (2.2,18.2) months. The majority of the children (77.9%, 827/1 062) were under 2 years of age, whereas 22.1% (235/1 062) were aged between 2-<18 years. Children with complex congenital heart defects accounted for the highest proportion (48.6%, 516/1 062), while those with isolated CHD made up 31.5% (335/1 062). Statistically significant differences were observed in several variables in demographics, clinical features, diagnosis, treatments, and outcomes between the two age groups (all P<0.05). The use of renin-angiotensin-aldosterone system inhibitors (41.1%, 436/1 062) and beta-blockers (8.7%, 92/1 062) was lower in hospitalized children with CHD and HF. Logistic regression identified complex CHD ( OR=7.73, 95% CI 2.24-26.63; OR=3.17, 95% CI 1.92-5.23), pulmonary hyperperfusion ( OR=2.15, 95% CI 1.01-4.18; OR=2.00, 95% CI 1.35-2.97), left ventricular ejection fraction<55% ( OR=2.13, 95% CI 1.08-4.21; OR=2.80, 95% CI 1.45-5.56), arterial oxygen partial pressure ( OR=0.99, 95% CI 0.98-0.99; OR=0.99, 95% CI 0.98-0.99), and serum calcium levels ( OR=0.31, 95% CI 0.17-0.58; OR=0.42, 95% CI 0.28-0.62) as independent risk factors for in-hospital mortality and cardiovascular events. Conclusions:The disease burden of CHD combined with HF in China has shown a continuous upward trend from 1990 to 2021, with higher growth rates in older age groups. Complex CHD, pulmonary hyperperfusion, left ventricular ejection fraction <55%, arterial oxygen partial pressure, and serum calcium concentration are independent risk factors for in-hospital mortality and cardiovascular events.