ObjectiveTo investigate the effects of zinc finger E-box binding homeobox 1 （ZEB1） on the proliferation， migration， and invasion of lung adenocarcinoma H322 cells， as well as its underlying molecular mechanisms. MethodsThe gene expression characteristics of the transcription factor ZEB1 in lung adenocarcinoma were analyzed using data from the GEO and TCGA public databases. RT-qPCR and Western blot were employed to measure mRNA and protein expression levels of ZEB1 in lung adenocarcinoma cell lines （H322， A549， 95-D） and normal human bronchial epithelial cells （BEAS-2B）. Lentiviral transduction was utilized to establish stable ZEB1-overexpressing （Oe-ZEB1） and vector control （Oe-NC） H322 cell lines. Cell proliferation was assessed using CCK-8， colony formation， and EdU assays， while apoptosis was evaluated by Hoechst33258/PI double staining. Wound healing and Transwell assays were performed to examine cell migration and invasion capabilities. Cell cycle distribution was determined by flow cytometry， and Western blot was used to analyze protein expression changes in relevant signaling pathways. ResultsThe findings from GEO and TCGA indicated that ZEB1 expression in lung adenocarcinoma varied with tumor malignancy grade. RT-qPCR and Western blot analyses revealed significantly higher ZEB1 expression in lung adenocarcinoma cell lines compared to BEAS-2B cells （P0.05）. Results from the CCK-8， colony formation， EdU， wound healing， and Transwell assays demonstrated that， compared with the un-transfected control （Control） group， Oe-ZEB1 H322 cells exhibited enhanced proliferation， migration， and invasion capabilities （P0.05）. Hoechst33258/PI double staining and flow cytometry analyses showed that， relative to the Control group， apoptosis was reduced in Oe-ZEB1 H322 cells （P0.05）. Additionally， a decreased proportion of cells in the G₁ phase and an increased proportion in the S phase were observed in Oe-ZEB1 cells， indicating accelerated cell cycle progression. Western blot analysis further revealed that， compared with the Control group， Oe-ZEB1 H322 cells exhibited upregulated expression of N-cadherin， mutant p53 （mutp53）， and Cyclin D1 （P0.05）， while expression levels of E-cadherin， murine double minute 2 （MDM2）， and p21 were downregulated （P0.05）. ConclusionOverexpression of ZEB1 promotes the proliferation， migration， and invasion of lung adenocarcinoma H322 cells and may facilitate cell cycle progression by modulating the MDM2/mutp53/p21 signaling pathway， thereby promoting the transition of cells from the G₀/G₁ phase to the S phase.

The advancement of microbiome technologies has facilitated a growing interest in the interactions between urinary microbiota and urinary stone formation. This article systematically reviews the technological evolution, and the reciprocal mechanism of microbiota involvement in lithogenesis and its clinical significance. It specifically highlights the changes within the urinary microbiota of calcium oxalate stone patients, notably a depletion of Lactobacillus populations and an enrichment of pathogenic taxa such as Enterobacteriaceae. Furthermore, it delineates the mechanisms whereby microbes participate in stone formation through metabolic regulation, physical adhesion, and inflammatory processes. The article also explores the influence exerted by distinct stone types on the structural composition and functional dynamics of the urinary microbiota. Integrated multi-omics analyses offer novel perspectives for unraveling the intricate microbiome-host interaction network. This approach is anticipated to facilitate significant advances in developing precision-focused prophylactic and therapeutic strategies for urinary stones from a microecological standpoint.

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: 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 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

Objective To investigate the correlation between the anterior ethmoidal artery(AEA)and anatomical variations of the anterior cranial base,and to analyze the predictive factors for AEA suspension.Methods Sinus CT imaging data of 159 patients undergoing endoscopic sinus sur-gery(ESS)were retrospectively analyzed.Mimics 21.0 software was utilized for three-dimensional reconstruction,measuring parameters of AEA and anterior cranial base anatomy and performing classi-fication.Pearson and Spearman correlation analyses were used to evaluate the correlations among vari-ous anatomical parameters and their classifications.Multivariate binary logistic regression analysis was performed to screen for independentpredictive factors of AEA suspension.Results The rates of AEA suspension differed significantly across different Keros classifications(P＜0.001),with an increase rate as the Keros classification level increased(P＜0.001).The transverse diameter,height and vol-ume of supraorbital ethmoid cells(SOEC),olfactory fossa depth,lateral lamella of the cribriform plate(LLCP)length and frontal sinus pneumatization classification grade were positively correlated with the distance from AEA to the cranial base(P＜0.05).Multivariate binary Logistic regression analysis showed that the presence of SOEC(OR=4.178,95％CI,2.517 to 6.935,P＜0.001),in-creased olfactory fossa depth(OR=1.433,95％CI,1.197 to 1.715,P＜0.001),and higher frontal sinus pneumatization classification grade(OR=1.621,95％CI,1.121 to 2.345,P=0.01)were independent predictive factors for AEA suspension.Conclusion Detailed preoperative CT imaging assessment,especially the analysis of SOEC,olfactory fossa depth and frontal sinus pneumatization classification,aids in accurately assessing the anatomical position of AEA,thereby effectively reduc-ing the risk of AEA injury,and improving the safety and success rate of surgery.

Objective To investigate the impact of blood sampling from different sites on coagula-tion results during systemic heparin anticoagulation period treated by continuous renal replacement therapy(CRRT).Methods Seventy-eight patients undergoing CRRT with systemic heparin anticoag-ulation were selected.Using a self-control method,blood samples were simultaneously collected from sampling port at input end of the CRRT extracorporeal circuit,the arterial pressure monitoring cathe-ter,and the peripheral vein of the patients.The four coagulation parameters were tested,and the differences in coagulation results among the blood sampling sites were compared.Results During CRRT with systemic heparin anticoagulation,the four coagulation parameters using blood samples col-lected from the sampling port at the input end of the CRRT extracorporeal circuit,the arterial pressure monitoring catheter,and the peripheral vein showed no statistically significant differences(P＞0.05).Subgroup analyses based on different hemofiltration machines and heparin doses also showed no statisti-cally significant differences(P＞0.05).Conclusion During CRRT,blood sampling from the sam-pling port at the input end of the circuit or the arterial pressure monitoring catheter can be used as an alternative to peripheral venous blood sampling,with no impact on coagulation results.

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.