The therapeutic effects of Yueju Pills on depression and cardiovascular diseases have been widely recognized. Previous studies have shown that the drug can significantly improve depressive-like behaviors induced by chronic unpredictable mild stress(CUMS) combined with atherosclerosis(AS). Given the complex pathogenesis of psychocardiac diseases, this study integrated metabolomics and network pharmacology to systematically elucidate the mechanism of Yueju Pills in alleviating depressive symptoms in psychocardiac diseases. The results demonstrate that, after Yueju Pill intervention, the levels of 9 abnormal metabolites in the hippocampus restore to normal ranges, primarily involving key pathways or signaling pathways, including the cyclic adenosine monophosphate(cAMP), mammalian target of rapamycin(mTOR), glycine/serine/threonine metabolism, and aminoacyl-tRNA biosynthesis. In a high-fat diet-induced CUMS ApoE~(-/-) mouse model, Yueju Pills significantly increases adenosine monophosphate(AMP) levels and decreases L-alanine and D-glyceric acid levels in the hippocampus. In conclusion, Yueju Pills exert antidepressant effects by regulating multiple metabolic axes, including glycine/serine/threonine metabolism and the cAMP, mTOR signaling pathways. Network pharmacology predictions reveal that the treatment of CUMS combined with AS by its core active components may be realized through modulating pathways concerning neuroinflammation and synaptic plasticity, including serine/threonine-protein kinase 1(AKT1), mitogen-activated protein kinase 1(MAPK1), and prostaglandin-endoperoxide synthase 2(PTGS2). This study provides a theoretical reference for the clinical application of Yueju Pills in alleviating the depressive symptoms of psychocardiac diseases.
Animals ; Network Pharmacology ; Mice ; Drugs, Chinese Herbal/administration & dosage* ; Metabolomics ; Male ; Depression/genetics* ; Humans ; Hippocampus/drug effects* ; Mice, Inbred C57BL ; Signal Transduction/drug effects*

Objective:To investigate the ability to diagnose, treat and manage kidney disease in Shanghai community health service centers.Methods:This was a cross-sectional survey. An online questionnaire survey was conducted in November 2023 among 248 Shanghai community health service centers and 2 140 general practitioners in Shanghai. The main topics of the institutional research were the kidney disease-related inspection items that medical institutions could carry out, the kidney disease diagnosis and treatment drugs, the kidney disease grass-roots management training, the opening of kidney disease clinics and the establishment of kidney disease standard diagnosis and treatment records. The main topics of the survey of general practitioners were general information, standardized diagnosis and management measures of kidney disease, knowledge based on the diagnosis and treatment guidelines of chronic kidney disease, and difficulties in standardized management of kidney disease.Results:Among the laboratory examination items in Shanghai community health service centers, the rates of routine urine (99.60%, 247 centers), renal function (95.16%, 236 centers) and urinary microalbumin (89.11%, 229 centers) were high. Among the imaging examinations, B-ultrasound of urinary system had the highest rate (92.34%, 229 centers). The preparation rate of kidney disease drugs varied widely among the centers, and the preparation rate of Chinese drugs such as Jinshuibao, nephritis Kangfu tablet and Shenshuaining was more than 90%. Sixty-six (26.61%) community health service centers had established kidney disease clinics. The overall accuracy rate of community general practitioners was 63.81% (13 656/21 400), of which the accuracy rate for diagnosis and screening method, referral indication and emergency dialysis indication was more than 85%, but the accuracy rate for drug treatment and careful medication was low at 28.93% (1 238/4 280) and 33.22% (711/2 140), respectively. There was a willingness for Community general practitioners to provide all aspects of life guidance for patients with kidney disease, but for patients with end-stage renal disease replacement therapy, there was a preference for this to be provided by the appropriate specialist.Conclusions:The community health service centers in Shanghai has already had the basic conditions for the management of kidney disease in terms of basic examination and testing equipment, drugs, etc. The community general practitioners have a certain knowledge of kidney disease, and the drug treatment needs to be strengthened.

Objective To understand the epidemiological characteristics and risk factors of hospital-acquired pneumonia in elderly diabetic patients. Methods Elderly patients with diabetes mellitus who were hospitalized in the hospital were selected from October 2020 to October 2023 as the research subjects. The epidemiological characteristics of hospital-acquired pneumonia were analyzed, and the risk factors affecting hospital-acquired pneumonia in elderly patients with diabetes mellitus were analyzed . Results There were 65 cases of hospital-acquired pneumonia in 388 elderly patients with diabetes mellitus, with an incidence of 16.75%, of which 56.92% were males and 43.08% were females. The proportion of patients aged≥80 years was higher than that of patients aged<80 years. There were no significant differences in gender, body mass index, education level, course of diabetes mellitus, smoking history, drinking history, hypertension, coronary heart disease and anemia between groups (P>0.05), but significant differences were shown in age, hospitalization time, tracheal invasive operation, types of antibacterial drug use and dysphagia between both groups (P<0.05). Logistic multivariate analysis showed that age≥80 years old, hospitalization time≥30 d, tracheal invasive operation, use of antibacterial drugs≥ 2 types, and dysphagia were independent risk factors for hospital-acquired pneumonia in elderly diabetic patients (P<0.05). Conclusion The risk of hospital-acquired pneumonia is high in elderly patients with diabetes mellitus. Patients with age≥80 years old, hospitalization time≥30 days, tracheal invasive operation, abuse of antibacterial drugs and dysphagia are high-risk population. It is necessary to take active intervention measures for such patients.

OBJECTIVE: Humans are exposed to complex mixtures of environmental chemicals and other factors that can affect their health. Analysis of these mixture exposures presents several key challenges for environmental epidemiology and risk assessment, including high dimensionality, correlated exposure, and subtle individual effects. METHODS: We proposed a novel statistical approach, the generalized functional linear model (GFLM), to analyze the health effects of exposure mixtures. GFLM treats the effect of mixture exposures as a smooth function by reordering exposures based on specific mechanisms and capturing internal correlations to provide a meaningful estimation and interpretation. The robustness and efficiency was evaluated under various scenarios through extensive simulation studies. RESULTS: We applied the GFLM to two datasets from the National Health and Nutrition Examination Survey (NHANES). In the first application, we examined the effects of 37 nutrients on BMI (2011-2016 cycles). The GFLM identified a significant mixture effect, with fiber and fat emerging as the nutrients with the greatest negative and positive effects on BMI, respectively. For the second application, we investigated the association between four pre- and perfluoroalkyl substances (PFAS) and gout risk (2007-2018 cycles). Unlike traditional methods, the GFLM indicated no significant association, demonstrating its robustness to multicollinearity. CONCLUSION GFLM framework is a powerful tool for mixture exposure analysis, offering improved handling of correlated exposures and interpretable results. It demonstrates robust performance across various scenarios and real-world applications, advancing our understanding of complex environmental exposures and their health impacts on environmental epidemiology and toxicology.
Humans ; Environmental Exposure/analysis* ; Linear Models ; Nutrition Surveys ; Environmental Pollutants ; Body Mass Index

Objective:To investigate the ability to diagnose, treat and manage kidney disease in Shanghai community health service centers.Methods:This was a cross-sectional survey. An online questionnaire survey was conducted in November 2023 among 248 Shanghai community health service centers and 2 140 general practitioners in Shanghai. The main topics of the institutional research were the kidney disease-related inspection items that medical institutions could carry out, the kidney disease diagnosis and treatment drugs, the kidney disease grass-roots management training, the opening of kidney disease clinics and the establishment of kidney disease standard diagnosis and treatment records. The main topics of the survey of general practitioners were general information, standardized diagnosis and management measures of kidney disease, knowledge based on the diagnosis and treatment guidelines of chronic kidney disease, and difficulties in standardized management of kidney disease.Results:Among the laboratory examination items in Shanghai community health service centers, the rates of routine urine (99.60%, 247 centers), renal function (95.16%, 236 centers) and urinary microalbumin (89.11%, 229 centers) were high. Among the imaging examinations, B-ultrasound of urinary system had the highest rate (92.34%, 229 centers). The preparation rate of kidney disease drugs varied widely among the centers, and the preparation rate of Chinese drugs such as Jinshuibao, nephritis Kangfu tablet and Shenshuaining was more than 90%. Sixty-six (26.61%) community health service centers had established kidney disease clinics. The overall accuracy rate of community general practitioners was 63.81% (13 656/21 400), of which the accuracy rate for diagnosis and screening method, referral indication and emergency dialysis indication was more than 85%, but the accuracy rate for drug treatment and careful medication was low at 28.93% (1 238/4 280) and 33.22% (711/2 140), respectively. There was a willingness for Community general practitioners to provide all aspects of life guidance for patients with kidney disease, but for patients with end-stage renal disease replacement therapy, there was a preference for this to be provided by the appropriate specialist.Conclusions:The community health service centers in Shanghai has already had the basic conditions for the management of kidney disease in terms of basic examination and testing equipment, drugs, etc. The community general practitioners have a certain knowledge of kidney disease, and the drug treatment needs to be strengthened.

Objective: To examine the association between serum vitamin D level and thyroid function indicators among elderly patients with type 2 diabetes mellitus (T2DM), so as to provide the evidence for the prevention and treatment of thyroid function abnormality among elderly patients with T2DM. Methods: Inpatients aged 60 years and older and admitted to the department of endocrinology of Zhejiang Hospital were selected as the study subjects. Gender, age, course of disease and other basic information were collected through questionnaire surveys. The serum 25-hydroxyvitamin D[25-(OH) D], thyroid stimulating hormone (TSH), free triiodothyronine (FT3), free thyroxine (FT4), total triiodothyronine (TT3), and total thyroxine (TT4) were measured. The correlation between serum vitamin D level and thyroid function indicators in elderly patients with T2DM was evaluated by a multiple linear regression model. Results: A total of 402 elderly patients with T2DM were surveyed, including 210 males (52.24%) and 192 females (47.76%), and had a median age of 70.00 (interquartile range, 12.00) years and a median course of disease of 14.00 (interquartile range, 14.00) years. There were 162 patients with insufficiency of vitamin D (40.30%) and 182 patients with deficiency (45.27%). The levels of TSH and glycated hemoglobin in the vitamin D deficiency group were (2.34±1.66) μIU/mL and (8.83±2.14) %, respectively, which were higher than those in the normal group [(1.74±1.10) μIU/mL and (8.11±1.75) %; P<0.05]. The levels of FT3 and FT3/FT4 in the vitamin D deficiency group were (2.86±0.48) μIU/mL and 2.85±0.71, respectively, which were lower than those in the vitamin D insufficiency group [(3.09±0.47) pg/mL and 3.14±0.81, P<0.05]. Multiple linear regression analysis showed a negative correlation between 25- (OH) D and TSH (β'=-0.159, P=0.001). Conclusion The vitamin D deficiency may be associated with the increase of TSH level among the elderly patients with T2DM.

AIM:To investigate the mechanism of chronic stress-induced myocardial injury in atherosclerotic(AS)mice.METHODS:Eight-week-old SPF-grade male ApoE-/-mice and C57BL/6J mice used in this study.The mice received dietary intervention for 10 weeks followed by pathological examination to test the successful AS modeling.After AS establishment,the mice were exposed to chronic unpredictable mild stress(CUMS)for 6 weeks and then divided into five groups:control,CUMS,AS-regular diet(AS-r)+CUMS,AS-high-fat diet(AS-h),and AS-h+CUMS.During CUMS,open-field test and sucrose preference test were performed on mice in all groups.Blood lipids were characterized using an automatic biochemical analyzer.Hematoxylin-eosin(HE)and oil red O staining were performed to evaluate pathological changes in the aortic root.Cardiac function was assessed using echocardiography.The serum concentration of myocardial injury markers and ATP content was detected by ELISA.Transmission electron microscopy was employed to observe the ul-trastructure of myocardial mitochondria.Myocardial mitochondrial oxygen consumption rate was determined using the Oxy-graph-2k high-resolution respiratory energy metabolism analyzer.Western blot was conducted to quantify the expression of B-cell lymphoma-2(Bcl-2),Bcl-2-associated X protein(Bax),and cleaved caspase-3.RESULTS:compared with the Control group,the total distance traveled,the number of entries into the central area,and the sucrose preference rate were significantly decreased in all CUMS groups(P<0.05).All AS groups exhibited varying levels of lipid deposition and endo-thelial damage in the aortic root,along with a significant reduction in cardiac function(P<0.05)and varying degrees of myocardial injury(P<0.05).In the AS-h+CUMS and AS-r+CUMS groups,myocardial mitochondrial structure was signifi-cantly disrupted.ATP content was significantly reduced(P<0.05),and the rates of oxygen consumption associated with mitochondrial respiratory chain complex I,mitochondrial respiratory chain complexes I+II,and the maximum respiratory capacity of the electron transport system were all significantly decreased(P<0.05).Moreover,the protein levels of Bax and cleaved caspase-3 were significantly increased(P<0.05),while that of Bcl-2 protein was significantly decreased(P<0.05).CONCLUSION:Chronic stress triggers mitochondrial non-steady-state load by disrupting myocardial structure and energy metabolism in AS mice,promoting myocardial cell apoptosis and myocardial injury.

Objective:To construct classification prediction models for gamma passing rate using radiomics-based machine learning approaches and data from multiple radiotherapy institutions and evaluate the models′ performance.Methods:The data from 572 volumetric-modulated arc therapy (VMAT) patients across three radiotherapy institutions (514 for training and 58 for testing)were retrospectively collected. Additionally, 45 VMAT plans were collected from a single institution as an independent external validation set. For all the data, a three-dimensional dose validation approach based on actual measurements of phantoms was utilized, and gamma analysis was performed at the 3%/2 mm criterion using a dose threshold of 10%, absolute doses, and global normalization. After radiomic features were extracted from dose files, feature selection was performed using the random forest (RF) method and RF combined with Shapley Additive exPlanation (SHAP). Then, feature subsets of varying sizes (10, 20, 30, 40, and 50) were selected based on feature rankings. Using these subsets as inputs, data training was conducted using the Extreme Gradient Boosting (XGBoost) algorithm. Finally, the models′ classification performance was assessed using the area under the curve (AUC) values and F1-score.Results:Under the 3%/2 mm criterion, all models performed the best in the case of 20 feature subsets. The optimal prediction model established based on the feature selection using RF exhibited AUC and F1-score of 0.88 and 0.89, respectively on the testing set and 0.82 and 0.90, respectively, on the validation set. The optimal prediction model built based on the feature selection using RF combined with SHAP yielded AUC and F1-score of 0.86 and 0.92 on the testing set and 0.87 and 0.89, respectively, on the validation set, along with superior robustness. Therefore, the second model possessed certain advantages over the first model.Conclusions:For multicenter dose verification result, it is feasible to construct a machine learning prediction model with high classification performance using radiomic features derived from dose files, combined with feature selection based on SHAP. This approach can assist in advancing the clinical applications and implementation of gamma passing rate prediction models.

Objective:To construct classification prediction models for gamma passing rate using radiomics-based machine learning approaches and data from multiple radiotherapy institutions and evaluate the models′ performance.Methods:The data from 572 volumetric-modulated arc therapy (VMAT) patients across three radiotherapy institutions (514 for training and 58 for testing)were retrospectively collected. Additionally, 45 VMAT plans were collected from a single institution as an independent external validation set. For all the data, a three-dimensional dose validation approach based on actual measurements of phantoms was utilized, and gamma analysis was performed at the 3%/2 mm criterion using a dose threshold of 10%, absolute doses, and global normalization. After radiomic features were extracted from dose files, feature selection was performed using the random forest (RF) method and RF combined with Shapley Additive exPlanation (SHAP). Then, feature subsets of varying sizes (10, 20, 30, 40, and 50) were selected based on feature rankings. Using these subsets as inputs, data training was conducted using the Extreme Gradient Boosting (XGBoost) algorithm. Finally, the models′ classification performance was assessed using the area under the curve (AUC) values and F1-score.Results:Under the 3%/2 mm criterion, all models performed the best in the case of 20 feature subsets. The optimal prediction model established based on the feature selection using RF exhibited AUC and F1-score of 0.88 and 0.89, respectively on the testing set and 0.82 and 0.90, respectively, on the validation set. The optimal prediction model built based on the feature selection using RF combined with SHAP yielded AUC and F1-score of 0.86 and 0.92 on the testing set and 0.87 and 0.89, respectively, on the validation set, along with superior robustness. Therefore, the second model possessed certain advantages over the first model.Conclusions:For multicenter dose verification result, it is feasible to construct a machine learning prediction model with high classification performance using radiomic features derived from dose files, combined with feature selection based on SHAP. This approach can assist in advancing the clinical applications and implementation of gamma passing rate prediction models.

Sclerostin, a protein secreted from osteocytes, negatively regulates the WNT signaling pathway by binding to the LRP5/6 co-receptors and further inhibits bone formation and promotes bone resorption. Sclerostin contributes to musculoskeletal system-related diseases, making it a promising therapeutic target for the treatment of WNT-related bone diseases. Additionally, emerging evidence indicates that sclerostin contributes to the development of cancers, obesity, and diabetes, suggesting that it may be a promising therapeutic target for these diseases. Notably, cardiovascular diseases are related to the protective role of sclerostin. In this review, we summarize three distinct types of inhibitors targeting sclerostin, monoclonal antibodies, aptamers, and small-molecule inhibitors, from which monoclonal antibodies have been developed. As the first-in-class sclerostin inhibitor approved by the U.S. FDA, the monoclonal antibody romosozumab has demonstrated excellent effectiveness in the treatment of postmenopausal osteoporosis; however, it conferred high cardiovascular risk in clinical trials. Furthermore, romosozumab could only be administered by injection, which may cause compliance issues for patients who prefer oral therapy. Considering these above safety and compliance concerns, we therefore present relevant discussion and offer perspectives on the development of next-generation sclerostin inhibitors by following several ways, such as concomitant medication, artificial intelligence-based strategy, druggable modification, and bispecific inhibitors strategy.