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.

Mitochondria are organelles in eukaryotic cells that play a crucial role in cellular energy me-tabolism,oxidative stress,heat production,and signal transduction.Mitochondrial transplantation(MT)is currently one of the most advanced techniques for treating mitochondrial dysfunction and anti-aging re-search.This study aimed to explore the feasibility and effectiveness of xenogeneic MT by transplanting mitochondria from yak(Bos grunniens),domestic cattle(Bos taurus),and horse(Equus caballus)into mice(Mus musculus).The results demonstrated that mitochondria from yak,domestic cattle,and horse could be successfully transplanted into mice and maintained in various tissues and organs of the mice for at least 14 days,as confirmed by confocal imaging,digital PCR,and DNA sequencing.MT mice exhibi-ted positive biological effects,including increased ATP content and mitochondrial DNA copy number(P＜0.05),with the maximum effect observed on day 7,which was sustained until day 14.Reactive oxygen species(ROS)levels in MT mice significantly increased at 2 hours post-injection(P＜0.05),then grad-ually decreased towards baseline levels by day 7 and day 14(P＞0.05).These findings support the effec-tiveness of xenogeneic MT and suggest that the effects can be maintained for up to 14 days.This study provides scientific evidence for future clinical applications.

Brain imaging abnormalities are present in schizophrenia (SZ) and bipolar disorder (BD), demonstrating disease-specific changes, yet they also share similarities in certain brain regions or functional characteristics, with SZ potentially exhibiting more extensive brain damage compared to BD. Structural magnetic resonance imaging (MRI) studies demonstrated widespread gray matter reductions in SZ, particularly in the prefrontal and temporal lobes. In BD, gray matter thickening was observed in the prefrontal lobes during manic episodes, while a reduction in gray matter was noted in the amygdala and hippocampus during depressive episodes. Both SZ and BD exhibited increased ventricular volume and reduced overall brain volume. Functional MRI studies revealed reduced functional connectivity in the prefrontal and temporal lobes in SZ, with decreased global and local efficiency in brain regions such as the hippocampus and cingulate gyrus. BD showed enhanced connectivity in the anterior cingulate gyrus and the default mode network (DMN). Both SZ and BD demonstrated altered functional connectivity in areas such as the striatum, salience network, central executive network and DMN. Diffusion tensor imaging studies showed decreased fractional anisotropy (FA) in the corpus callosum of SZ, with a decrease in FA in the left fronto-occipital fasciculus in BD. Both SZ and BD exhibited reduced FA in the uncinate fasciculus and corpus callosum. Magnetic resonance spectroscopy revealed decreased concentrations of glutathione, N-acetylaspartate (NAA) and inositol in the anterior cingulate gyrus of SZ. In BD, glutathione and inositol concentrations were elevated in the anterior cingulate gyrus, while NAA levels decreased during depressive episodes and increased during remission. Both SZ and BD showed increased levels of glutamate and gamma-aminobutyric acid in the prefrontal cortex. This article provides a review of the current evidence on the differences and similarities in multimodal magnetic resonance brain imaging between SZ and BD, aiming to offer a reference for future exploration of neuroimaging biomarkers and the neurobiological mechanisms of SZ and BD.

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.

Brain imaging abnormalities are present in schizophrenia (SZ) and bipolar disorder (BD), demonstrating disease-specific changes, yet they also share similarities in certain brain regions or functional characteristics, with SZ potentially exhibiting more extensive brain damage compared to BD. Structural magnetic resonance imaging (MRI) studies demonstrated widespread gray matter reductions in SZ, particularly in the prefrontal and temporal lobes. In BD, gray matter thickening was observed in the prefrontal lobes during manic episodes, while a reduction in gray matter was noted in the amygdala and hippocampus during depressive episodes. Both SZ and BD exhibited increased ventricular volume and reduced overall brain volume. Functional MRI studies revealed reduced functional connectivity in the prefrontal and temporal lobes in SZ, with decreased global and local efficiency in brain regions such as the hippocampus and cingulate gyrus. BD showed enhanced connectivity in the anterior cingulate gyrus and the default mode network (DMN). Both SZ and BD demonstrated altered functional connectivity in areas such as the striatum, salience network, central executive network and DMN. Diffusion tensor imaging studies showed decreased fractional anisotropy (FA) in the corpus callosum of SZ, with a decrease in FA in the left fronto-occipital fasciculus in BD. Both SZ and BD exhibited reduced FA in the uncinate fasciculus and corpus callosum. Magnetic resonance spectroscopy revealed decreased concentrations of glutathione, N-acetylaspartate (NAA) and inositol in the anterior cingulate gyrus of SZ. In BD, glutathione and inositol concentrations were elevated in the anterior cingulate gyrus, while NAA levels decreased during depressive episodes and increased during remission. Both SZ and BD showed increased levels of glutamate and gamma-aminobutyric acid in the prefrontal cortex. This article provides a review of the current evidence on the differences and similarities in multimodal magnetic resonance brain imaging between SZ and BD, aiming to offer a reference for future exploration of neuroimaging biomarkers and the neurobiological mechanisms of SZ and BD.

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.

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.