ObjectiveTo explore the mechanism of Shenmai injection in improving cisplatin resistance in non-small cell lung cancer （NSCLC） based on the endoplasmic reticulum stress through protein kinase R-like endoplasmic reticulum kinase （PERK）/activated transcription factor 4 （ATF4）/C/EBP homologous protein （CHOP） signaling pathway. MethodsBALB/c nude mice bearing cisplatin-resistant human lung cancer cell line （A549/cisplatin） were randomly divided into four groups： Blank control group （0.9% sodium chloride）， cisplatin group （5 µg·g^-1cisplatin）， Shenmai injection group （5.2 mg·g^-1 Shenmai injection）， and combination therapy group （5.2 mg·g^-1 Shenmai injection +5 µg·g^-1cisplatin）. The drug intervention lasted for 4 weeks， and the changes in body weight and tumor volume were monitored. Hematoxylin-eosin （HE） staining was performed to observe tumor tissue pathology. Transmission electron microscopy （TEM） was used to assess the morphology of the endoplasmic reticulum. Immunohistochemical assay was conducted to measure the positive expressions of PERK， ATF4， and CHOP in tumor tissues. Western blot quantified the protein expression of immunoglobulin heavy chain binding protein （BIP）， PERK， phosphorylated PERK （p-PERK）， eukaryotic translation initiation factor 2α （eIF2α）， phosphorylated eIF2α （p-eIF2α）， ATF4， CHOP， B-cell lymphoma -2 （Bcl-2）， and Bcl-2 Associated X protein （Bax）. A549/cis cells were divided into blank group： Blank control group （normal culture medium）， cisplatin group （23.3 µmol·L^-1 cisplatin）， Shenmai Injection group （20 g·L^-1 Shenmai injection）， and combination therapy group （20 g·L^-1 Shenmai injection+23.3 µmol·L^-1 cisplatin）. Cell counting kit-8 （CCK-8） method was used to detect cell viability， TEM was used to observe the morphology of endoplasmic reticulum， and Western blot was used to detect endoplasmic reticulum stress and apoptosis-related proteins. ResultsCompared with the cisplatin group， the combination therapy group showed increased body weight （P<0.05）， decreased tumor volume （P<0.05）， and expanded endoplasmic reticulum in tumor cells. The positive expressions of PERK， ATF4， and CHOP increased （P<0.05）. Western blot revealed elevated protein expression levels of BIP， p-PERK/PERK， p-eIF2α/eIF2α， ATF4， CHOP， and Bax （P<0.05）， while Bcl-2 expression decreased （P<0.05）. As shown in the in vitro experiment， compared with the cisplatin group， the combination therapy group exhibited a reduced cell survival rate （P<0.05）. TEM revealed increased endoplasmic reticulum dilation and vesicular degeneration. Western blotting showed increased protein levels of BIP， p-PERK/PERK， p-eIF2α/eIF2α， ATF4， CHOP and Bax （P<0.05）， with decreased Bcl-2 expression （P<0.05）. ConclusionShenmai injection combined with cisplatin has a synergistic antitumor effect in NSCLC， which may be attributed to the activation of endoplasmic reticulum stress response mediated by the PERK/eIF2α/ATF4/CHOP signaling pathway and the induction of tumor cell apoptosis.

Objective: Machine learning (ML) has been reported to have better predictive capability than traditional statistical techniques. The aim of this study was to assess the efficacy of ML algorithms and logistic regression (LR) for predicting depressive symptoms during the COVID-19 pandemic. Methods: Analyses were carried out in a national cross-sectional study involving 21,916 participants. The ML algorithms in this study included random forest (RF), support vector machine (SVM), neural network (NN), and gradient boosting machine (GBM) methods. The performance indices were sensitivity, specificity, accuracy, precision, F1-score, and area under the receiver operating characteristic curve (AUC). Results: LR and NN had the best performance in terms of AUCs. The risk of overfitting was found to be negligible for most ML models except for RF, and GBM obtained the highest sensitivity, specificity, accuracy, precision, and F1-score. Therefore, LR, NN, and GBM models ranked among the best models. Conclusion Compared with ML models, LR model performed comparably to ML models in predicting depressive symptoms and identifying potential risk factors while also exhibiting a lower risk of overfitting.

ObjectiveAutism spectrum disorder (ASD) is a neurodevelopmental condition characterized by difficulties with communication and social interaction, restricted and repetitive behaviors. Previous studies have indicated that individuals with ASD exhibit early and lifelong attention deficits, which are closely related to the core symptoms of ASD. Basic visual attention processes may provide a critical foundation for their social communication and interaction abilities. Therefore, this study explores the behavior of children with ASD in capturing attention to changes in topological properties. MethodsOur study recruited twenty-seven ASD children diagnosed by professional clinicians according to DSM-5 and twenty-eight typically developing (TD) age-matched controls. In an attention capture task, we recorded the saccadic behaviors of children with ASD and TD in response to topological change (TC) and non-topological change (nTC) stimuli. Saccadic reaction time (SRT), visual search time (VS), and first fixation dwell time (FFDT) were used as indicators of attentional bias. Pearson correlation tests between the clinical assessment scales and attentional bias were conducted. ResultsThis study found that TD children had significantly faster SRT (P<0.05) and VS (P<0.05) for the TC stimuli compared to the nTC stimuli, while the children with ASD did not exhibit significant differences in either measure (P>0.05). Additionally, ASD children demonstrated significantly less attention towards the TC targets (measured by FFDT), in comparison to TD children (P<0.05). Furthermore, ASD children exhibited a significant negative linear correlation between their attentional bias (measured by VS) and their scores on the compulsive subscale (P<0.05). ConclusionThe results suggest that children with ASD have difficulty shifting their attention to objects with topological changes during change detection. This atypical attention may affect the child’s cognitive and behavioral development, thereby impacting their social communication and interaction. In sum, our findings indicate that difficulties in attentional capture by TC may be a key feature of ASD.

Compounds isolated from Epimedium include the total flavonoids of Epimedium , icariin, and its metabolites (icaritin, icariside I, and icariside II), which have similar molecular structures. Modern pharmacological research and clinical practice have proved that Epimedium and its active components have a wide range of pharmacological effects, especially in improving sexual function, hormone regulation, anti-osteoporosis, immune function regulation, anti-oxidation, and anti-tumor activity. To date, we still need a comprehensive source of knowledge about the pharmacological effects of Epimedium and its bioactive compounds on the male reproductive system. However, their actions in other tissues have been reviewed in recent years. This review critically focuses on the Epimedium , its bioactive compounds, and the biochemical and molecular mechanisms that modulate vital pathways associated with the male reproductive system. Such intrinsic knowledge will significantly further studies on the Epimedium and its bioactive compounds that protect the male reproductive system and provide some guidances for clinical treatment of related male reproductive disorders.
Male ; Epimedium/chemistry* ; Humans ; Genitalia, Male/drug effects* ; Flavonoids/therapeutic use* ; Animals

Objective: Machine learning (ML) has been reported to have better predictive capability than traditional statistical techniques. The aim of this study was to assess the efficacy of ML algorithms and logistic regression (LR) for predicting depressive symptoms during the COVID-19 pandemic. Methods: Analyses were carried out in a national cross-sectional study involving 21,916 participants. The ML algorithms in this study included random forest (RF), support vector machine (SVM), neural network (NN), and gradient boosting machine (GBM) methods. The performance indices were sensitivity, specificity, accuracy, precision, F1-score, and area under the receiver operating characteristic curve (AUC). Results: LR and NN had the best performance in terms of AUCs. The risk of overfitting was found to be negligible for most ML models except for RF, and GBM obtained the highest sensitivity, specificity, accuracy, precision, and F1-score. Therefore, LR, NN, and GBM models ranked among the best models. Conclusion Compared with ML models, LR model performed comparably to ML models in predicting depressive symptoms and identifying potential risk factors while also exhibiting a lower risk of overfitting.

Objective: Machine learning (ML) has been reported to have better predictive capability than traditional statistical techniques. The aim of this study was to assess the efficacy of ML algorithms and logistic regression (LR) for predicting depressive symptoms during the COVID-19 pandemic. Methods: Analyses were carried out in a national cross-sectional study involving 21,916 participants. The ML algorithms in this study included random forest (RF), support vector machine (SVM), neural network (NN), and gradient boosting machine (GBM) methods. The performance indices were sensitivity, specificity, accuracy, precision, F1-score, and area under the receiver operating characteristic curve (AUC). Results: LR and NN had the best performance in terms of AUCs. The risk of overfitting was found to be negligible for most ML models except for RF, and GBM obtained the highest sensitivity, specificity, accuracy, precision, and F1-score. Therefore, LR, NN, and GBM models ranked among the best models. Conclusion Compared with ML models, LR model performed comparably to ML models in predicting depressive symptoms and identifying potential risk factors while also exhibiting a lower risk of overfitting.

Objective: Machine learning (ML) has been reported to have better predictive capability than traditional statistical techniques. The aim of this study was to assess the efficacy of ML algorithms and logistic regression (LR) for predicting depressive symptoms during the COVID-19 pandemic. Methods: Analyses were carried out in a national cross-sectional study involving 21,916 participants. The ML algorithms in this study included random forest (RF), support vector machine (SVM), neural network (NN), and gradient boosting machine (GBM) methods. The performance indices were sensitivity, specificity, accuracy, precision, F1-score, and area under the receiver operating characteristic curve (AUC). Results: LR and NN had the best performance in terms of AUCs. The risk of overfitting was found to be negligible for most ML models except for RF, and GBM obtained the highest sensitivity, specificity, accuracy, precision, and F1-score. Therefore, LR, NN, and GBM models ranked among the best models. Conclusion Compared with ML models, LR model performed comparably to ML models in predicting depressive symptoms and identifying potential risk factors while also exhibiting a lower risk of overfitting.

Objective To investigate the role of the neutrophil-to-lymphocyte ratio(NLR)in predicting the in-hospital mortality risk of patients with acute aortic dissection(AAD)in multicenter hospitals.Methods A multicenter retrospective cohort study was conducted.Clinical data were collected from 2642 AAD patients who were hospitalized in five teaching hospitals:Tongji Hospital Affiliated to Tongji Medical College of Huazhong University of Science and Technology,Henan Provincial People's Hospital,Fuwai Central China Cardiovascular Hospital,the Third Affiliated Hospital of Xinxiang Medical University,and the Second Affiliated Hospital of Chongqing Medical University between August 2010 and December 2021.According to the quartiles of serum NLRlevels,the patients were divided into four groups:first quartile(Q1,n=660),second quartile(Q2,n=661),third quartile(Q3,n=661),and fourth quartile(Q4,n=660).The clinical characteristics and biochemical indicators of each group were compared.Partial correlation analysis was used to assess the relationship between NLR and cardiovascular parameters.Restricted cubic splines,Kaplan-Meier survival analysis,and Cox regression models were employed to evaluate the association between NLR levels and in-hospital mortality risk in AAD patients.Results The median age of all patients was 54[interquartile range(IQR):46-63]years,including 2096 males and 546 females.Compared with Q1-Q3 groups,patients inQ4group had a lower incidence of smoking history and diabetes history,and were more likely to have DeBakey type Ⅰ AAD(P＜0.05).Additionally,the levels of aspartate aminotransferase,high-density lipoprotein cholesterol,creatinine,and D-dimer in Q4 group were higher,while the levels of triglycerides and C-reactive protein(CRP)were lower(P＜0.01).The results of partial correlation analysis showed that the plasma NLR level was positively correlated with D-dimer(r=0.43,P＜0.01)and creatinine(r=0.16,P＜0.01).The restricted cubic spline function in the Cox model revealed a significant non-linear relationship between the plasma NLR level and clinical outcomes in AAD patients(P＜0.01).Kaplan-Meier survival analysis indicated that patients in Q4 group had the highest in-hospital mortality rate compared with Q1-Q3 groups(P＜0.0001).Furthermore,multivariate Cox regression analysis demonstrated that compared with Q1 group,the hazard ratio(HR)of NLR in Q4 group was 1.77(95％CI 1.33-2.37,P＜0.001),which was an independent risk factor for the primary endpoint events.Conclusion A higher plasma NLR level is significantly associated with the occurrence of cardiovascular events in AAD patients,and this association remains significant even after adjusting for potential confounding factors such as the multicenter visiting hospitals.

Objective To establish and evaluate a nomogram prediction model for spontaneous peritonitis in HBV-related primary liver cancer.Methods A retrospective study was conducted on 1298 patients with HBV-related primary liver cancer hospitalized in the Kunming Third People's Hospital from January 2012 to December 2022.General data and serological indicators were collected,and patients were divided into infection group(n=262)and control group(n=1036)based on the occurrence of spontaneous peritonitis.Univariate and LASSO regression analyses were used to screen variables,followed by binary logistic regression to analyze the influencing factors of spontaneous peritonitis in HBV-related primary liver cancer patients,leading to the establishment of a nomogram prediction model.Finally,the Hosmer-lemeshow(H-L)goodness of fit test,receiver operating characteristic(ROC)curve,calibration curve,decision curve analysis(DCA)and clinical impact curve(CIC)were utilized to evaluate the fit degree,accuracy,calibration,and clinical practicability of the nomogram prediction model.Results Single factor analysis revealed significant differences between infection group and control group in portal vein cancer thrombus(PVTT),Child-Pugh grade,China Liver Cancer Staging(CNLC)stage,alcohol consumption history,smoking history,white blood cell count(WBC),neutrophil count(NE),hemoglobin(Hb),fibrinogen(FIB),abnormal prothrombin(PIVKA-Ⅱ),aspartate aminotransferase(AST),alanine aminotransferase(ALT),total protein(TP),prealbumin(PA),γ-glutamyltransferase(GGT),alkaline phosphatase(ALP),cholinesterase(CHE),total bile acid(TBA),total cholesterol(TC),low density lipoprotein(LDL),creatinine(Cr),HBV DNA,CD3+T cells count,CD4+T cells count,CD8+T cells count,CD4+T cells/CD8+T cells ratio,procalcitonin(PCT),serum amyloid A(SAA),interleukin-6(IL-6),high-sensitivity C-reactive protein(hs-CRP),alpha-fetoprotein(AFP),and IL-4(P＜0.05).LASSO regression analysis identified 5 variables:Child-Pugh grade,PVTT,WBC,CHE and hs-CRP.Binary logistic regression analysis indicated that Child-Pugh grade(Grade B:OR=5.780,95％CI 3.271-10.213,P＜0.001;Grade C:OR=14.818,95％CI 7.697-28.526,P＜0.001),PVTT(OR=2.893,95％CI 2.037-4.108,P＜0.001),WBC(OR=1.088,95％CI 1.031-1.148,P=0.002),and hs-CRP(OR=1.005,95％CI 1.001-1.010,P=0.026)were the independent risk factors of spontaneous peritonitis in HBV-related primary liver cancer patients.Using these 4 variables,a nomogram prediction model was constructed and evaluated.The P-value of the H-L goodness of fit test was 0.760.Moreover,the area under ROC curve(AUC)was 0.866,with a sensitivity of 0.870 and a specificity of 0.716.The average absolute error of the calibration curve is 0.022.DCA and CIC analyses demonstrated that the nomogram prediction model possessed some clinical utility.Conclusion The nomogram prediction model for spontaneous peritonitis in HBV-related primary liver cancer patients,constructed using Child-Pugh grade,PVTT,WBC and hs-CRP,exhibits a high fitting degree and accuracy,with the prediction probability highly consistent with the actual occurrence probability,and possesses certain clinical practicability.

Objective To evaluate the efficacy,safety,and cost-effectiveness of berberine-based quadruple therapy vs.the clarithromycin-based quadruple therapy for Helicobacter pylori(H.pylori)eradication in treatment-na?ve patients.Methods This was a single-center,prospective,open-label randomized controlled trial.A total of 404 treatment-naive patients with H.pylori infection who visited the Outpatient Department of Gastroenterology,the First Medical Center of Chinese PLA General Hospital from September 2021 to May 2024 were enrolled.The patients were randomly assigned in a 1:1 ratio to two groups:berberine quadruple therapy group(berberine+amoxicillin+esomeprazole+colloidal bismuth pectin;n=202)and clarithromycin quadruple therapy group(clarithromycin+amoxicillin+esomeprazole+colloidal bismuth pectin;n=202).Both groups received a 14-day treatment course.The H.pylori eradication rate,incidence of adverse reactions,medication compliance,and treatment costs were compared between the two groups.Results By intention-to-treat(ITT)analysis,eradication rate did not differ significantly between the two groups[89.1%(180/202)in berberine quadruple therapy group vs.89.6%(181/202)in clarithromycin quadruple therapy group,P=0.872].The per-protocol(PP)analysis also showed no significant difference in the eradication rate between the two groups[90.4%(179/198)vs.91.3%(178/195),P=0.763].The incidence of adverse reactions in berberine quadruple therapy group was significantly lower than that in clarithromycin quadruple therapy group[18.2%(36/198)vs.38.5%(75/195),P<0.001].Specifically,the incidence of taste disturbance in berberine quadruple therapy group was significantly lower than that in clarithromycin quadruple therapy group(3.0%vs.15.4%,P<0.001).There was no statistically significant difference in medication compliance between the two groups[98.5%(195/198)in berberine quadruple therapy group vs.97.9%(191/195)in clarithromycin quadruple therapy group,P=0.688].The fixed direct medical cost per patient was significantly lower in berberine quadruple therapy group than that in clarithromycin quadruple therapy group(402.08 yuan vs.693.94 yuan).Conclusions The berberine-based quadruple therapy is as effective as traditional clarithromycin-based quadruple therapy for eradicating H.pylori,with the advantages of a lower incidence of adverse reactions and lower cost.It represents a safe,effective,and economical treatment option worthy of further promotion and application.