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.

PURPOSE: The fixation method commonly employed worldwide for treating unstable fractures of the posterior pelvic ring is the percutaneous iliosacral screw technique. However, prolonged operation time and frequent fluoroscopies result in surgical risks. This study aimed to investigate whether a new triangulation method could reduce operative and fluoroscopy times and increase the accuracy of screw placement. METHODS: This study is a real-world retrospective cohort analysis that examined a patient cohort who underwent percutaneous iliosacral screw fixation between January 1, 2019 and December 31, 2022. Inclusion criteria were patients (1) diagnosed with posterior pelvic ring instability who underwent pelvic fracture closed reduction and percutaneous S1 transverse-penetrating iliosacral screw placement and (2) aged >18 years. Exclusion criteria were: (1) combined proximal femoral fractures, (2) severe soft tissue injury in the surgical area, (3) incomplete imaging data, and (4) declining to provide written informed consent by the patient. The patients were divided into 2 groups according to the screw insertion method: conventional and triangulation methods. Screw placement and fluoroscopy times recorded by the C-arm were compared between the 2 methods. The accuracy of screw placement was evaluated by Smith grading on postoperative CT. Normality tests were conducted to assess the distribution of the quantitative variables and the Chi-square test was used to compare the qualitative variables. RESULTS: The study included a total of 94 patients diagnosed with posterior pelvic ring instability, who underwent percutaneous iliosacral screw placement. The patients were divided into 2 groups: 46 patients treated with the conventional surgical method and 48 patients received the triangulation method. The operation time (61.13±9.69 vs. 35.77±6.27) min and fluoroscopy frequency times (52.15±9.29 vs. 24.40±4.04) of the triangulation method were significantly reduced (p<0.001). CONCLUSIONS The use of a triangular positioning technique for the surface positioning of percutaneous iliosacral screws could reduce the operative time and fluoroscopy frequency. And the screw placement accuracy using this new method was comparable to that using other conventional methods.
Humans ; Retrospective Studies ; Bone Screws ; Pelvic Bones/surgery* ; Male ; Female ; Fracture Fixation, Internal/methods* ; Fractures, Bone/surgery* ; Adult ; Middle Aged ; Fluoroscopy ; Aged ; Sacrum/surgery* ; Operative Time

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: 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 the differential expression of EIF5A2 in intrahepatic cholangiocarcinoma cell lines RBE,HCCC9810,and HUCCT1,and its effects on HCCC9810 cell migration and invasion,epithelial mesenchymal transition,and PI3K/AKT signaling pathway.Methods:The differential expression of EIF5A2 in RBE,HCCC9810,and HUCCT1 cell lines was detected using WB method.The HCCC9810 cell line,with the highest expression of EIF5A2,was selected for this experiment.The expression of EIF5A2 in HCCC9810 cell line was silenced by transient transfection of small interfering RNA.The best silencing effect of small interfering RNA was screened by WB.Scratch assay and Tran-swell migration invasion assay were used to detect the effect of silencing EIF5A2 on the migration and invasion ability of HCCC9810 cells.WB was used to detect the effect of silencing EIF5A2 on PI3K/AKT signaling pathway and epithelial mesenchymal transition in HCCC9810 cells.Results:The WB results showed that EIF5A2 had the highest expression in the HCCC9810 cell line,and siRNA1 had the best silencing effect on EIF5A2 in the HCCC9810 cell line.Scratch assay and Transwell migration invasion assay results showed that silencing EIF5A2 in the HCCC9810 cell line resulted in a decrease in cell invasion and metastasis ability(P＜0.05).At the same time,the expression of p-PI3K and p-AKT in the PI3K/AKT signaling pathway was significantly decreased(P＜0.05),while the epithelial cell marker E-cadherin expression increased(P＜0.05)and the stromal cell marker N-cadherin expression decreased(P＜0.05).Conclusion:EIF5A2 may promote epi-thelial mesenchymal transition and enhance the migration and invasion ability of intrahepatic cholangiocarcinoma cells through the PI3K/AKT signaling pathway.