[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。

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 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*

AIM: To observe the effect of fudosteine on induced sputum cell components and lung function in patients with stable neutrophil-dominated COPD. METHODS: From October 2019 to October 2022, 53 patients with stable COPD were selected and divided into fudosteine group and placebo group. The placebo group was treated with routine treatment, and the fudosteine group was treated with fudosteine on the basis of routine treatment. The two groups were treated for 6 months. The clinical symptoms [Saint George's Respiratory Questionnaire (SGRQ), COPD Assessment Test (CAT) and Modified British Medical Research Council Dyspnea scale (MMRC), Breathlessness, Cough, and Sputum Scale (BCSS)], lung function index, induced sputum cytology analysis and other related examination results were recorded in detail before and after treatment. RESULTS: (1) Compared with the baseline, the forced expiratory volume in one second (FEV1), forced vital capacity (FVC), and the ratio of FEV1 to FVC (FEV1/FVC) of the two groups were improved after treatment, and the differences were statistically significant (P<0.05). However, after treatment, there was no significant difference in pulmonary function between the two groups except for the percentage of carbon monoxide diffusion in the predicted value (DLCO%pre) (DLCO%pre in the fudosteine group was higher than that in the placebo group). (2) After treatment, the total number of induced sputum cells and neutrophil counts in the fudosteine group were lower than those in the placebo group. Compared with the number of cells in each component at baseline, the total number of induced sputum cells and neutrophil count in the fudosteine group were significantly lower (P< 0.05). CONCLUSION: Fudosteine treatment in patients with stable neutrophil-dominated COPD can improve lung function, reduce the total number of induced sputum cells and the total number of neutrophils, thereby improving airway inflammation.

OBJECTIVE To compare the efficacy and safety of lacosamide （LCM） and carbamazepine （CAR） as monotherapy in the treatment of adult patients with newly diagnosed epilepsy. METHODS By methods of retrospective analysis， 84 adult patients with newly diagnosed epilepsy， were admitted to the Department of Neurology， Huaihe Hospital of Henan University during Sept. 2020-Jun. 2022， were divided into the control group （40 cases， receiving CAR treatment） and the observation group （44 cases， receiving LCM treatment） according to different medication regimens. Total response rate， epilepsy seizure frequency， blood lipid levels， and the occurrence of adverse events （AEs） of patients were compared between the 2 groups. RESULTS In the first month after treatment， there was no statistically significant difference in the total response rate between the observation group （63.64%） and the control group （55.00%， P＞0.05）； the frequency of epilepsy seizure in both groups was significantly reduced compared to before treatment （P＜0.05）， but there was no statistically significant difference between 2 groups （P＞0.05）. In the third month after treatment， the total response rate of the observation group （90.91%） was significantly higher than control group （67.50%， P＜0.05）； the frequencies of epilepsy seizure in both groups were significantly reduced compared to before treatment， and the observation group was significantly lower than the control group （P＜0.05）. In the third month after treatment， the levels of total cholesterol （TC）， triglyceride （TG） and low-density lipoprotein cholestrol （LDL-C） in the control group and the level of LDL-C in the observation group were significantly higher than before treatment， and the levels of TC， TG and LDL-C in the observation group were significantly lower than those in the control group （P＜0.05）. There was no statistically significant difference in the incidence of AEs between the observation group （15.91%） and the control group （17.50%， P＞0.05）. CONCLUSIONS Both LCM and CAR have certain effects in the treatment of newly diagnosed epilepsy in adults， which can reduce the frequency of epilepsy seizure in patients and have comparable safety. Meanwhile， LCM has better long-term efficacy than CAR in treating newly diagnosed epilepsy in adults， and its impact on the patient’s blood lipid is smaller than CAR.

OBJECTIVE To prepare and characterize curcumin nanomicelles （hereinafter referred to as Cur/mPEG-PBLA micelles）， and to evaluate the in vitro hepatoprotective activity against alcohol liver disease （ALD）. METHODS Cur/mPEG-PBLA micelles were prepared with the dialysis method using methoxy-poly（ethylene glycol）-poly（β-benzyl-L-aspartate） （mPEG-PLGA） as the carrier. The appearance and microscopic morphology of Cur/mPEG-PBLA micelles were observed， and particle size， polydispersity index， Zeta potential， encapsulation efficiency and drug loading content were all detected. The in vitro release， pH stability， thermal stability， dilution stability， storage stability， plasma stability tests， and hemolysis experiments were all performed. The cell model of ALD was established with anhydrous ethanol intervention using human liver cancer cells and normal liver cells as objects， Cur reference solution as reference， to evaluate in vitro preventive and ameliorative effects of Cur/mPEG- PBLA micelles on ALD. RESULTS The prepared Cur/mPEG-PBLA micelles exhibited a pale-yellow milky light， with a spherical shape and uniform distribution. The average particle size was about 140 nm， and the polydispersity index was less than 0.3. Zeta potential was （－8.15±0.05） mV； the encapsulation efficiency was （73.26±3.16）%， and the drug loading content was （4.87± 0.42）%. The cumulative release of Cur reference substance was close to 80% at 10 h； the cumulative release of Cur/mPEG-PBLA micelles at 8 h was 28.94% and only 48.25% at 48 h. pH stability and thermal stability of Cur/mPEG-PBLA micelles were better than those of Cur reference solution； Cur/mPEG-PBLA micelles showed good dilution stability， storage stability and plasma stability， and would not cause hemolysis. Cur reference solution and Cur/mPEG-PBLA micelles had varying degrees of in vitro preventive and ameliorative effects on ALD in two types of cells； after 48 h of application， the above effects of Cur/mPEG-PBLA micelles were significantly better than those of Cur reference solution at the same mass concentration （P＜0.05）. CONCLUSIONS Cur/mPEG-PBLA micelles can improve pH stability and thermal stability of Cur， delayits degradation rate， and have better in vitro hepatoprotective activity against ALD.