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.

[摘 要] 目的：探讨KH型剪切调节蛋白（KHSRP）靶向调控JAK1/STAT3信号轴对食管胃结合部腺癌（AEG）细胞增殖、迁移和侵袭及移植瘤生长与肺转移的影响。方法：收集2017年1月至2018年12月间在淮河医院确诊的64例AEG组织和癌旁组织标本及临床资料，采用免疫组化法观察AEG组织和癌旁组织中KHSRP的表达水平。qPCR法检测AEG细胞OE-19、TE-7、BIC-1、FLO-1、SK-GT-4、BE-3与正常食管上皮细胞Het-1A中KHSRP的表达差异。通过慢病毒载体对KHSRP进行敲减和过表达处理，分别转染OE-19与TE-7细胞、FLO-1与SK-GT-4细胞，实验分为sh-NC组、sh-KHSRP组和Vector组、KHSRP过表达组（KHSRP组）。采用qPCR法检测敲减或过表达效率，CCK-8法、Transwell小室法分别检测敲减或过表达KHSRP对AEG细胞增殖、迁移和侵袭的影响。构建小鼠异种移植瘤和肺转移模型，观察KHSRP对移植瘤体内生长和转移的作用。WB法验证KHSRP靶向JAK/STAT信号通路。细胞拯救实验验证KHSRP是否通过调节JAK1/STAT3信号通路促进AEG细胞的恶性进程。结果：与癌旁组织相比，AEG组织中KHSRP表达水平显著增高（P < 0.05或P < 0.01）。细胞功能实验分析显示，KHSRP过表达在体外均显著促进AEG细胞增殖、迁移和侵袭（P < 0.05或P < 0.01）。动物实验结果显示，KHSRP在裸鼠体内具有促进AEG细胞移植瘤生长与肺转移的作用（P < 0.05或P < 0.01）。在敲减KHSRP后，JAK/STAT信号通路中JAK1、STAT3磷酸化水平均明显降低，过表达KHSRP后情况则均反之（P < 0.05或P < 0.01）。细胞拯救实验显示，KHSRP可以逆转敲减JAK1/STAT3对细胞增殖、迁移和侵袭的抑制作用（P < 0.05或P < 0.01）。结论：KHSRP通过激活JAK1/STAT3信号通路调控AEG细胞转移的恶性进程，KHSRP有望成为AEG临床治疗的潜在靶点。

OBJECTIVE: To evaluate the efficacy and safety of Shexiang Tongxin Dropping Pill (STDP) in treating stable angina patients with phlegm-heat and blood-stasis syndrome by exercise duration and metabolic equivalents. METHODS: This multicenter, randomized, double-blind, placebo-controlled clinical trial enrolled stable angina patients with phlegm-heat and blood-stasis syndrome from 22 hospitals. They were randomized 1:1 to STDP (35 mg/pill, 6 pills per day) or placebo for 56 days. The primary outcome was the exercise duration and metabolic equivalents (METs) assessed by the standard Bruce exercise treadmill test after 56 days of treatment. The secondary outcomes included the total angina symptom score, Chinese medicine (CM) symptom scores, Seattle Angina Questionnaire (SAQ) scores, changes in ST-T on electrocardiogram and adverse events (AEs). RESULTS: This trial enrolled 309 patients, including 155 and 154 in the STDP and placebo groups, respectively. STDP significantly prolonged exercise duration with an increase of 51.0 s, compared to a decrease of 12.0 s with placebo (change rate: -11.1% vs. 3.2%, P<0.01). The increase in METs was significantly greater in the STDP group than in the placebo group (change: -0.4 vs. 0.0, change rate: -5.0% vs. 0.0%, P<0.01). The improvement of total angina symptom scores (25.0% vs. 0.0%), CM symptom scores (38.7% vs. 11.8%), reduction of nitroglycerin consumption (100.0% vs. 11.3%), and all domains of SAQ, were significantly greater with STDP than placebo (all P<0.01). The changes in Q-T intervals at 28 and 56 days from baseline were similar between the two groups (both P>0.05). Twenty-five participants (16.3%) with STDP and 16 (10.5%) with placebo experienced AEs (P=0.131), with no serious AEs observed. CONCLUSION STDP could improve exercise tolerance in patients with stable angina and phlegm-heat and blood stasis syndrome, with a favorable safety profile. (Registration No. ChiCTR-IPR-15006020).
Humans ; Double-Blind Method ; Drugs, Chinese Herbal/adverse effects* ; Male ; Female ; Middle Aged ; Angina, Stable/physiopathology* ; Aged ; Syndrome ; Treatment Outcome ; Placebos ; Tablets

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*

Epstein-Barr virus(EBV)is the first virus discovered to be associated with human tumors,and its association with gastric cancer has received widespread attention in recent years.In 2014,The Cancer Genome Atlas initially classified gastric cancer into four subtypes based on molecular characteristics,with EBV positive individuals grouped into a distinct subtype known as EBV-positive gastric cancer,which has unique molecular and clinicopathological characteristics.While EBV infection in malignant gastric epithelial cells and the induction of host genetic mutations and epigenetic abnormalities were generally considered as important bases for EBV carcinogenesis,the mechanisms un-derlying the relationship between EBV infection and gastric cancer risk remain unclear.We here summarize the existing epidemiological data supporting associations between different indicators of EBV infection and gastric cancer and provide an overview of the potential roles and mechanisms of EBV in gastric cancer carcinogenesis,which is expected to offer significant guidance for future studies elucidating the rela-tionship between EBV infection and gastric cancer development.

Objective:To understand the iodine nutritional status of Hohhot residents, and to provide a basis for scientific prevention and control of iodine deficiency disease and reasonable iodine supplementation.Methods:The stratified cluster sampling method was used to conduct a survey on the iodine nutritional status of residents in 9 banners (counties and districts) of Hohhot City in 2022. Each flag (county, district) was divided into 5 sampling areas according to the east, west, south, north and center directions, and 1 school was selected from each area, 40 children aged 8 to 10 years old were selected from each school (balanced in terms of age, half of both sexes), and 100 pregnant women (balanced in the early, middle and late stages of pregnancy) were selected from each flag (county, district), and the salt samples of household salt were collected as well as random urine samples for the determination of salt iodine and urine iodine content. In 3 randomly selected banners (counties and districts), 40 children were selected from each banner (county and district) and their thyroid volume was measured using ultrasound.Results:A total of 2 710 salt samples of edible salt were collected, including 32 samples of non-iodized salt, with a median salt iodine of 21.6 mg/kg, an iodized salt coverage rate of 98.7% (2 675/2 710), and a qualified iodized salt consumption rate of 92.8% (2 515/2 710). There are 1 803 salt samples from children's homes, with a median salt iodization of 21.9 mg/kg, the iodized salt coverage rate was 99.1% (1 786/1 803), and the consumption rate of qualified iodized salt was 93.1% (1 678/1 803). Nine hundred and eight salt samples of edible salt were collected from the homes of pregnant women. The median salt iodine was 21.4 mg/kg, the iodized salt coverage rate was 98.0% (890/907), and the consumption rate of qualified iodized salt was 92.2% (837/907). The median urinary iodine for children and pregnant women was 198.5 and 169.9 μg/L. The rate of goiter in children was 3.6% (22/602).Conclusion:In 2022, residents of Hohhot are at the appropriate level of iodine, salt iodine coverage, urinary iodine for children and pregnant women, and goiter rate for children are all in line with the standard for elimination of iodine deficiency diseases, and iodine deficiency diseases are in a state of continuous elimination.

Objective:To investigate the diagnostic efficacy of targeted biopsy combined with regional systematic biopsy in prostate cancer(PCa)in patients with prostate imaging reporting and data system v2.1(PI-RADS v2.1)4-5.Methods:From January 2023 to October 2023,patients who underwent prostate biopsy for the first time with total prostate specific antigen(tPSA)≤20 ng/mL and had a multi-parametric magnetic resonance imaging(mpMRI)PI-RADS of 4-5 in Peking University First Hospital were prospectively collected.All the patients underwent transrectal ultrasound-guided cognitive fusion tar-geted biopsy(3 cores)followed by systematic biopsy(12 cores).Various hypothetical biopsy schemes were defined based on different biopsy sites.The detection effectiveness of targeted biopsy combined with regional systematic biopsy and other biopsy schemes for prostate cancer were compared using Cochran's Q and McNemar tests.Results:A total of 255 patients were enrolled,of whom 204(80.0％)were de-tected with prostate adenocarcinoma and 187(73.3％)were clinically significant with prostate cancer(csPCa).The detection rate of PCa with targeted biopsy was significantly lower than that of targeted biopsy combined with 12-core system biopsy(77.3％vs.80.0％,P=0.016),and 71.4％(5/7)of the missed patients was csPCa.There was no significant difference in the detection rate between targeted biopsy combined with 4-core regional system biopsy and 12-core system biopsy(P＞0.999),and 1 case of csPCa and clinically insignificant prostate cancer(cisPCa)were missed.There was no significant difference in the detection rate of PCa between targeted combined regional system biopsy and targeted combined lateral or traditional 6-core system biopsy and the number of cores were reduced.Missed diag-nosis of targeted biopsy was correlated with the maximum diameter of the lesion(OR=0.086,95％CI:0.013-0.562,P=0.010).For the patients with PI-RADS 5,only 1 case of PCa was missed in 122 cases by targeted biopsy alone.For patients with PI-RADS 4,6 PCa cases were missed among the 133 patients with targeted biopsy alone,and 1 case of csPCa and cisPCa were missed by targeted biopsy com-bined with regional system biopsy.The statistics of positive core counts for different biopsy schemes indi-cated that targeted combined regional systematic biopsy had a higher proportion of positive cores second only to targeted biopsy alone.Conclusion:Targeted biopsy combined with regional systematic biopsy has high diagnostic efficacy in patients with PI-RADS 4-5 and can be considered as one of the improved schemes for combined biopsy.Targeted biopsy alone is also a feasible option for patients for patients with a PI-RADS score of 5.