ObjectiveTo investigate the protective effect of Rehmanniae Radix iridoid glycosides （RIG） on the kidney tissue of streptozotocin （STZ）-induced diabetic mice and explore the underlying mechanism. MethodsTwelve of 72 male C57BL/6J mice were randomly selected as the normal group， and the remaining 60 mice were fed with a high-fat diet for six weeks combined with injection of 60 mg·kg^-1 STZ for 4 days to model type 2 diabetes mellitus. The successfully modeled mice were randomized into model， metformin （250 mg·kg^-1）， catalpol （100 mg·kg^-1）， low-dose RIG （RIG-L， 200 mg·kg^-1） and high-dose RIG （RIG-H， 400 mg·kg^-1） groups （n=11）. Mice in each group were administrated with corresponding drugs， while those in the normal group and model group were administrated with the same dose of distilled water by gavage once a day. After 8 weeks of intervention， an oral glucose tolerance test （OGTT） was performed， and the area under the curve （AUC） was calculated. After mice were sacrificed， both kidneys were collected. The body weight， kidney weight， and fasting blood glucose （FBG） were measured. Biochemical assays were performed to measure the serum levels of triglycerides （TG）， total cholesterol （TC）， serum creatinine （SCr）， and blood urea nitrogen （BUN）. Enzyme-linked immunosorbent assay （ELISA） was employed to determine the serum level of fasting insulin （FINS）， and the insulin sensitivity index （ISI） and homeostatic model assessment for insulin resistance （HOMA-IR） were calculated. The pathological changes in kidneys of mice were observed by hematoxylin-eosin staining and Masson staining. The immunohistochemical method （IHC） was employed to assess the expression of interleukin-1 （IL-1）， interleukin-6 （IL-6）， tumor necrosis factor-α（TNF-α）， transforming growth factor-β₁ （TGF-β₁）， and collagen-3 （ColⅢ） in the kidney tissue. The protein levels of TGF-β₁， cell signal transduction molecule 3 （Smad3）， matrix metalloproteinase-9 （MMP-9）， and ColⅢ in kidneys of mice were determined by Western blot. ResultsCompared with the normal group， the model group showcased decreased body weight and ISI （P<0.01）， increased kidney weight， FBG， AUC， FINS， HOMA-IR， TC， TG， SCr， and BUN （P<0.01）， glomerular hypertrophy， capsular space narrowing， and collagen deposition in the kidney， up-regulated protein levels of IL-1， IL-6， TNF-α， TGF-β₁， ColⅢ， and Smad3 （P<0.01）， and down-regulated protein level of MMP-9 （P<0.01） in the kidney tissue. Compared with the model group， the treatment groups had no significant difference in the body weight and decreased kidney weight （P<0.05， P<0.01）. The FBG level declined in the RIG-H group after treatment for 4-8 weeks and in the metformin， catalpol， and RIG-L groups after treatment for 6-8 weeks （P<0.01）. The AUC in the RIG-L， RIG-H， and metformin groups decreased （P<0.05， P<0.01）. The levels of TC， SCr， and BUN in the serum of mice in each treatment group became lowered （P<0.05， P<0.01）. The level of TG declined in the RIG-L， RIG-H， and metformin groups （P<0.05， P<0.01）. The serum level of FINS declined in the catalpol， RIG-L， and metformin groups （P<0.01）. Compared with the model group， the treatment groups showed decreased HOMA-IR （P<0.01）， increased ISI （P<0.01）， alleviated pathological changes in the kidney tissue， and down-regulated expression of IL-1 and TGF-β₁. In addition， the protein levels of IL-6， TNF-α， and ColⅢ in the RIG-H and metformin groups and IL-6 and TNF-α in the RIG-L group were down-regulated （P<0.05， P<0.01）， and the protein levels of IL-6， TNF-α， and ColⅢ in the catalpol group and ColⅢ in the RIG-L group showed a decreasing trend without statistical difference. The protein levels of TGF-β₁， Smad3， and ColⅢ in the RIG-H and metformin groups were down-regulated （P<0.01）. Compared with that in the model group， the protein level of MMP-9 was up-regulated in each treatment group （P<0.01）. ConclusionRIG can improve the renal structure and function of diabetic mice by regulating the TGF-β₁/Smads signaling pathway.

Objective:To investigate the influencing factors of adult dental fluorosis in drinking water-borne endemic fluorosis areas of Inner Mongolia Autonomous Region.Methods:A case-control study was conducted in January 2024 to select adult fluorosis patients (case group) and healthy individuals (control group) from the drinking water-borne endemic fluorosis areas in Helinger County, Hohhot City, Inner Mongolia Autonomous Region as the survey subjects. Urine samples were collected to determine urinary fluoride concentration. A questionnaire survey was conducted. SPSS 25.0 software was used for χ 2 test and multivariate logistic regression analysis. Restricted cubic spline (RCS) was used to analyze the association between urinary fluoride concentration and the risk of dental fluorosis in adults. Results:A total of 161 individuals were included in the survey, including 100 in the case group and 61 in the control group. The results of univariate analysis showed that there were statistically significant differences in the distribution of gender, smoking, and urinary fluoride concentration between the case group and the control group (χ 2 = 7.54, 5.02, 9.69, P < 0.05). The results of multivariate logistic regression analysis indicated that gender ( OR = 0.36, 95% CI: 0.18 - 0.73, P = 0.005) and urinary fluoride concentration ( OR = 3.08, 95% CI: 1.46 - 6.67, P = 0.003) were the influencing factors of adult fluorosis. RCS analysis showed a significant linear dose-response relationship between the risk of dental fluorosis and urinary fluoride concentration ( Poverall trend = 0.001, Pnonlinear = 0.071). When the urinary fluoride concentration was greater than 1.57 mg/L, the risk of dental fluorosis increased with the increase of urinary fluoride concentration. Conclusion:Gender and urinary fluoride concentration are the risk factors of dental fluorosis in adults in drinking water-borne endemic fluorosis areas of Inner Mongolia Autonomous Region.

Cancer incidence in China has been rising steadily,with a particularly heavy burden from several high-prevalence malignancies.Medical examination for cancer plays a critical role in the early detection of cancer,precancerous lesions,and precursor conditions,thereby facilitating timely diagnosis and intervention.Such examination also addresses the growing demand for person-alized cancer screening services among diverse population groups.The development of evidence-based,context-specific cancer screening guidelines is essential to enhance the standardization,quality,and equity of preventive screening practices across the country,ultimately improving out-comes in early cancer detection and treatment.Guided by the Department of Medical Emergency Response of the National Health Commission,the Guidelines for Medical Examination for Cancer in Health Examination Agency(2025 Edition)were developed under the leadership of the National Cancer Center.A multidisciplinary panel of experts formulated the guidelines in accordance with the principles and methodology of the World Health Organization Handbook for Guideline Deve-lopment.The guidelines provide evidence-based recommendations on key clinical domains:target cancers and populations,overall screening workflow,screening protocols,diagnostic technolo-gies,result interpretation,follow-up procedures,and quality control.The primary objective is to standardize cancer screening practices in health examination agency and strengthen China's ca-pacity for prevention and control of high-burden cancers.

This paper used the Experimental Teaching Management Center as an example to explore the transformation practices of experimental technicians in job responsibilities, team structure, teaching and research capabilities, performance mechanisms, and collaborative education systems. The article systematically analyzed the advantages of the experimental technical team in resource openness, technical support, large instrument management, and multi-team collaboration, and summarized the practical performance through data on project approvals, competition awards, research achievements, and student growth. Additionally, it identified key challenges, including insufficient training, incomplete incentive mechanisms, and the need for improved resource coordination. To address these challenges, the study recommends the continuous enhancement of personnel capacity building, the reform of assessment systems, and the reinforcement of cross-departmental collaboration. This study provides a reference for medical schools to construct the practical path of experimental technical teams participating in the cultivation of innovative and entrepreneurial talents.

BACKGROUND:Percutaneous kyphoplasty was a common surgical procedure for the treatment of osteoporotic vertebral compression fracture.However,there was no research to confirm whether percutaneous kyphoplasty could effectively correct the local kyphoplasty of the spine in patients over 80 years old with osteoporotic vertebral compression fracture.OBJECTIVE:To investigate the effect of percutaneous kyphoplasty on local kyphosis in super-aging patients with osteoporotic vertebral compression fracture.METHODS:Single-segment osteoporotic vertebral compression fracture patients treated with percutaneous kyphoplasty at the Department of Spinal Surgery,Peking University People's Hospital,from March 2016 to August 2022,were selected as the research cohort,and the follow-up data of patients in hospital and out-patient were collected.According to patients'age,patients were divided into the advanced age group(60-79 years old,n=126)and the super-aged group(＞80 years old,n=52).According to gender,body mass index,basic diseases(hypertension,diabetes,and cardiovascular diseases),fracture segments and the presence or absence of preoperative intravertebral cleft,the two groups of patients were matched 1:2 by propensity score matching.The lumbar CT values,injection amount of bone cement,preoperative and postoperative vertebral height,preoperative collapse rate of the vertebral body,preoperative and postoperative Cobb angle,recovery rate of Cobb angle,distance between the bone cement and anterior edge of the vertebral body,sagittal position of cement filling,contact between the bone cement and endplate,distance between the bone cement and vertebral endplates,bone cement distribution score,bone cement leakage,and vertebral refracture were compared between the two groups.RESULTS AND CONCLUSION:(1)After matching the propensity score,115 patients were included,with 71 patients in the advanced age group and 44 patients in the super-aged group.There was no statistically significant difference in baseline data,including gender,body mass index,hypertension ratio,diabetes ratio,cardiovascular disease ratio,fracture section,and preoperative intravertebral cleft,between the two groups(P＞0.05).The postoperative Cobb angle of the super-aged patients was significantly smaller than that of the elderly patients(P＜0.05).There was no significant difference in lumbar CT values,injection amount of bone cement,preoperative and postoperative vertebral height,preoperative collapse rate of the vertebral body,preoperative Cobb angle,recovery rate of Cobb angle,postoperative distance between the bone cement and anterior edge of the vertebral body,sagittal position of cement filling,contact between the bone cement and endplate,distance between the bone cement and vertebral endplates,bone cement distribution score,bone cement leakage,and vertebral refracture ratio between the two groups(P＞0.05).(2)These findings indicate that percutaneous kyphoplasty can effectively correct local kyphosis of the spine in super-aging patients with osteoporotic vertebral compression fractures.

Parkinson's disease (PD), a chronic and common neurodegenerative disease, is characterized by the progressive loss of dopaminergic neurons in the dense part of the substantia nigra and abnormal aggregation of alpha-synuclein. Type 2 diabetes mellitus (T2DM) is a metabolic disease characterized by chronic insulin resistance and deficiency in insulin secretion. Extensive evidence has confirmed shared pathogenic mechanisms underlying PD and T2DM, such as oxidative stress caused by insulin resistance, mitochondrial dysfunction, inflammation, and disorders of energy metabolism. Conventional drugs for treating T2DM, such as metformin and glucagon-like peptide-1 receptor agonists, affect nerve repair. Even drugs for treating PD, such as levodopa, can affect insulin secretion. This review summarizes the relationship between PD and T2DM and related therapeutic drugs from the perspective of insulin signaling pathways in the brain.
Humans ; Parkinson Disease/drug therapy* ; Diabetes Mellitus, Type 2/pathology* ; Signal Transduction/physiology* ; Receptor, Insulin/metabolism* ; Animals ; Insulin Resistance/physiology* ; Insulin/metabolism*

Cardiotoxicity is a critical issue in drug development that poses serious health risks, including potentially fatal arrhythmias. The human ether-à-go-go related gene (hERG) potassium channel, as one of the primary targets of cardiotoxicity, has garnered widespread attention. Traditional cardiotoxicity testing methods are expensive and time-consuming, making computational virtual screening a suitable alternative. In this study, we employed machine learning techniques utilizing molecular fingerprints and descriptors to predict the cardiotoxicity of compounds, with the aim of improving prediction accuracy and efficiency. We used four types of molecular fingerprints and descriptors combined with machine learning and deep learning algorithms, including Gaussian naive Bayes (NB), random forest (RF), support vector machine (SVM), K-nearest neighbors (KNN), eXtreme gradient boosting (XGBoost), and Transformer models, to build predictive models. Our models demonstrated advanced predictive performance. The best machine learning model, XGBoost Morgan, achieved an accuracy (ACC) value of 0.84, and the deep learning model, Transformer_Morgan, achieved the best ACC value of 0.85, showing a high ability to distinguish between toxic and non-toxic compounds. On an external independent validation set, it achieved the best area under the curve (AUC) value of 0.93, surpassing ADMETlab3.0, Cardpred, and CardioDPi. In addition, we explored the integration of molecular descriptors and fingerprints to enhance model performance and found that ensemble methods, such as voting and stacking, provided slight improvements in model stability. Furthermore, the SHapley Additive exPlanations (SHAP) explanations revealed the relationship between benzene rings, fluorine-containing groups, NH groups, oxygen in ether groups, and cardiotoxicity, highlighting the importance of these features. This study not only improved the predictive accuracy of cardiotoxicity models but also promoted a more reliable and scientifically interpretable method for drug safety assessment. Using computational methods, this study facilitates a more efficient drug development process, reduces costs, and improves the safety of new drug candidates, ultimately benefiting medical and public health.

OBJECTIVE: To explore the feasibility of incorporating simple bedside indicators into death predictive model for elderly critically ill patients based on interpretability machine learning algorithms, providing a new scheme for clinical disease assessment. METHODS: Elderly critically ill patients aged ≥ 65 years who were hospitalized in the intensive care unit (ICU) of Tacheng People's Hospital of Ili Kazak Autonomous Prefecture from June 2017 to May 2020 were retrospectively selected. Basic parameters including demographic characteristics, basic vital signs and fluid intake and output within 24 hours after admission, as well acute physiology and chronic health evaluation II (APACHE II), Glasgow coma score (GCS) and sequential organ failure assessment (SOFA) were also collected. According to outcomes in hospital, patients were divided into survival group and death group. Four datasets were constructed respectively, namely baseline dataset (B), including age, body temperature, heart rate, pulse oxygen saturation, respiratory rate, mean arterial pressure, urine output volume, infusion volume, and crystal solution volume; B+APACHE II dataset (BA), B+GCS dataset (BG), and B+SOFA dataset (BS). Then three machine learning algorithms, Logistic regression (LR), extreme gradient boosting (XGboost) and gradient boosting decision tree (GBDT) were used to develop the corresponding mortality predictive models within four datasets. The feature importance histogram of each prediction model was drawn by SHapley additive explanation (SHAP) method. The area under curve (AUC), accuracy and F1 score of each model were compared to determine the optimal prediction model and then illuminate the nomogram. RESULTS: A total of 392 patients were collected, including 341 in the survival group and 51 in the death group. There were statistically significant differences in heart rate, pulse oxygen saturation, mean arterial pressure, infusion volume, crystal solution volume, and etiological distribution between the two groups. The top three causes of death were shock, cerebral hemorrhage, and chronic obstructive pulmonary disease. Among the 12 prognostic models trained by three machine learning algorithms, overall performance of prognostic models based on B dataset was behind, whereas the LR model trained by BA dataset achieved the best performance than others with AUC of 0.767 [95% confidence interval (95%CI) was 0.692-0.836], accuracy of 0.875 (95%CI was 0.837-0.903) and F1 score of 0.190. The top 3 variables in this model were crystal solution volume with first 24 hours, heart rate and mean arterial pressure. The nomogram of the model showed that the total score between 150 and 230 were advisable. CONCLUSION The interpretable machine learning model including simple bedside parameters combined with APACHE II score could effectively identify the risk of death in elderly patients with critically illness.
Humans ; Critical Illness ; Machine Learning ; Aged ; Algorithms ; Intensive Care Units ; Retrospective Studies ; APACHE ; Prognosis ; Organ Dysfunction Scores ; Hospital Mortality ; Male ; Female

OBJECTIVES: This study aimed to explore the active components, potential targets, and mechanism of Cnidii Fructus in the treatment of periodontitis with osteoprosis through network pharmacology, molecular docking, and molecular dynamics simulation technology. METHODS: The main chemical constituents and targets of Cnidii Fructus were screened using the TCMSP and SwissTargetPrediction databases, as well as literature reports. Targets of periodontitis and osteoporosis were predicted using different databases. The intersection targets of Cnidii Fructus, periodontitis, and osteoporosis were obtained using Venny 2.1. The protein-protein interaction network was formed on the STRING platform. Cytoscape 3.9.1 was used to construct the active component-intersection target interaction network, perform the topological analysis, and screen key targets and core active components. Furthermore, the Metascape database was used to perform gene ontology (GO) function and Kyoto encyclopedia of genes and genomes (KEGG) pathway enrichment analysis on the intersection targets. The top five key targets and core active components were selected as receptor proteins and ligand small molecules. Discovery Studio 2019 was used to dock ligands and receptors and visualize the docking results. Molecular dynamics simulation was conducted using Gromacs2022.3 to assess the stability of the interactions between the core active components and the main targets. RESULTS: A total of 20 potential active ingredients of Cnidii Fructus were screened, and 116 targets of Cnidii Fructus were obtained for treating periodontitis and osteoporosis. GO and KEGG analyses of the 116 targets showed that Cnidii Fructus may play a therapeutic role through the phosphoinositide 3-kinase-protein kinase B (PI3K-Akt) and advanced glycation end products-receptor for advanced glycation end products (AGE-RAGE) signaling pathways. Molecular docking showed that the core constituents were well bound to the main targets. Molecular dynamics simulations confirmed the stability of the Diosmetin-AKT1 complex system. CONCLUSIONS The preliminary discovery of the potential molecular pharmacological mechanism of Cnidii Fructus extract in the targeted treatment of periodontitis with osteoporosis through a multi-component, multitarget, and multi-pathway approach can serve as a theoretical foundation for future drug-development research and clinical application.
Molecular Docking Simulation ; Molecular Dynamics Simulation ; Network Pharmacology ; Periodontitis/complications* ; Drugs, Chinese Herbal/chemistry* ; Osteoporosis/complications* ; Humans ; Protein Interaction Maps ; Cnidium/chemistry*

Objective:To identify proteins associated with the risk of gastric cancer (GC) and build a protein risk score for risk prediction of GC based on proteomic analysis.Methods:Gastric mucosal proteomics data were used to construct Dataset One, comprising 94 GC cases and 230 individuals with different stages of gastric mucosal lesions. The GC cases were recruited from the National Upper Gastrointestinal Cancer Early Detection (UGCED) Program in Linqu, Shandong Province, as well as clinical patients from the Fifth Medical Center, General Hospital of PLA, and Peking University Cancer Hospital. Non-cancer individuals were enrolled from the National UGCED Program in Linqu and community screening programs at the Dongfang Hospital. All participants were pathologically confirmed. Multivariate logistic regression analysis was employed to identify gastric mucosal proteins significantly associated with GC risk. Subsequently, plasma proteomics data from the UK Biobank Pharma Proteomics Project (UKB-PPP) were used to construct Dataset Two, including 40 baseline GC cases and 47 933 non-cancer individuals, and Dataset Three, comprising 138 incident GC cases and 47 933 non-cancer individuals during a prospective follow-up period. In Dataset Two, multivariate logistic regression analysis was conducted to assess associations between plasma protein levels and baseline GC risk. In Dataset Three, multivariate Cox regression analysis was used to examine associations with the risk of incident GC. A poly-protein risk score (PRS) was developed using a weighted summation method based on protein effect sizes from Dataset Two. Its associations with GC risk and the progression of gastric mucosal lesions were evaluated using linear regression trend tests.Results:A total of 324, 47 973 and 48 071 participants were included in Datasets One, Two, and Three, respectively. Across the three datasets, the proportions of males and individuals aged>60 years were higher in the GC group than in the non-GC group (all P values<0.05). The follow-up period in Dataset Three had a M ( P 25, P 75) of 14.47 (13.7, 15.2) years, with a median of 7.4 (4.6, 11.3) years for those who progressed to GC. Based on Dataset One, 2 524 tissue-differential proteins associated with GC risk were identified through multivariate logistic regression analysis adjusted for age and sex. Among these, seven proteins were consistently associated with GC risk across tissue and plasma levels in Datasets Two and Three, with consistent directions of association. Five proteins (MRC1, APOL1, BST2, PON2, and GGH) were positively associated with GC risk, while two (GSN and CLEC3B) were negatively associated. Analysis of the PRS based on these seven proteins showed that for each standard deviation increase in the tissue-derived PRS, the risk of GC increased by 6.26 times (95% CI: 4.02-9.75). In Dataset Two, each standard deviation increase in the plasma-derived PRS was associated with a 2.13-fold increase in GC risk (95% CI: 1.68-2.69). In the prospective cohort of Dataset Three, individuals in the high PRS group had a 2.27-fold higher risk of GC compared to the low PRS group (95% CI: 1.50-3.45). Moreover, each standard deviation increase in the plasma PRS was associated with a 57% higher risk of GC ( HR=1.57, 95% CI: 1.34-1.84). Additionally, the tissue-derived PRS showed an increasing trend with the progression of gastric mucosal lesions. Conclusion:The tissue and plasma proteomics identified seven individual proteins that may indicate the risk of developing gastric cancer, showing the potential as biomarkers for aiding in the screening of gastric cancer.