OBJECTIVE To investigate the mechanism of pachymic acid on renal injury in pregnancy induced hypertension （PIH） rats by regulating the silent information regulator transcript 1/peroxisome proliferator-activated receptor γ coactivator-1α （Sirt1/PGC-1α） pathway. METHODS Pregnant SD rats were prepared by co-caging and PIH model was induced using N-nitro-L- arginine methyl ester （L-NAME） method. PIH rats were randomly divided into model group， L-pachymic acid （low-dose pachymic acid， 10 mg/kg） group， H-pachymic acid （high-dose pachymic acid， 20 mg/kg） group， and H-pachymic acid+EX527 （20 mg/kg pachymic acid+10 mg/kg EX527） group， with 6 rats in each group. Another 6 normal pregnant rats were selected as blank group. Each group was given relevant medicine or solvent intragastrically or intraperitoneally daily， once a day， for 28 consecutive days. After the last administration， 24 h urinary protein and tail artery systolic blood pressure （SBP） were measured in pregnant rats from each group， along with the levels of serum creatinine （Scr）， blood urea nitrogen （BUN），uric acid （UA）， and cystatin C （Cys-C）. The contents of superoxide dismutase （SOD）， malondialdehyde （MDA）， glutathione peroxidase （GSH-Px）， and 8-hydroxy-2′-deoxyguanosine （8-OHdG） in renal tissue， as well as the mRNA and protein expression levels of Sirt1 and PGC-1α， were also determined. Meanwhile， renal histopathological changes in rats from each group were evaluated using hematoxylin-eosin （HE） staining and periodic acid-Schiff （PAS） staining. RESULTS Compared with model group， L-pachymic acid group and H-pachymic acid group exhibited significant decreases in 24 h urine protein quantification， tail artery SBP， Scr， BUN， UA， Cys-C levels， glomerulosclerosis index score of renal tissue， renal tubular injury score， the percentage of PAS positive area， MDA and 8-OHdG （P＜0.05）. Conversely， the contents of SOD and GSH-Px， along with the mRNA and protein expression levels of Sirt1 and PGC-1α， were significantly increased （P＜0.05）. Moreover， these improvements were more pronounced in H-pachymic acid group （P＜0.05）. Compared with H-pachymic acid group， the aforementioned indicators in pregnant rats from the H-pachymic acid+EX527 group showed significant reversal （P＜0.05）. CONCLUSIONS Pachymic acid significantly ameliorates renal injury induced by PIH in rats， potentially through activation of the Sirt1/PGC-1α pathway.

Objective To explore the correlation of MET status in patients with advanced prostatic acinar adenocarcinoma with the clinical pathological parameters and prognosis. Methods The specimen from 135 patients with advanced prostatic acinar adenocarcinoma was included. The expression of c-MET protein was detected via immunohistochemistry, and MET gene amplification was assessed by fluorescence in situ hybridization. The relationships of c-MET expression and gene amplification with clinicopathological features and prognosis were analyzed. Results The positive expression rate of c-MET was 52.60% (71/135). Compared with the c-MET expression in adjacent tissues, that in tumor tissues showed lower heterogeneous expression. Among the cases, 1.71% (2/117) exhibited MET gene polyploidy, but no gene amplification was detected. Positive c-MET expression was significantly correlated with high Gleason scores and grade groups (P=0.0073). However, no significant relationship was found between c-MET expression and progression-free survival or post-treatment t-PSA levels. Conclusion c-MET protein is expressed at a relatively low level in advanced prostatic acinar adenocarcinoma, suggesting that c-MET may not be a viable target for ADC drug development in prostatic acinar adenocarcinoma.

OBJECTIVE: Several epidemiological observational studies have related particulate matter (PM) exposure to Inflammatory bowel disease (IBD), but many confounding factors make it difficult to draw causal links from observational studies. The objective of this study was to explore the causal association between PM _2.5 exposure, its absorbance, and IBD. METHODS: We assessed the association of PM _2.5 and PM _2.5 absorbance with the two primary forms of IBD (Crohn's disease [CD] and ulcerative colitis [UC]) using Mendelian randomization (MR) to explore the causal relationship. We conducted two-sample MR analyses with aggregated data from the UK Biobank genome-wide association study. Single-nucleotide polymorphisms linked with PM _2.5 concentrations or their absorbance were used as instrumental variables (IVs). We used inverse variance weighting (IVW) as the primary analytical approach and four other standard methods as supplementary analyses for quality control. RESULTS: The results of MR demonstrated that PM _2.5 had an adverse influence on UC risk (odds ratio [ OR] = 1.010; 95% confidence interval [ CI] = 1.001-1.019, P = 0.020). Meanwhile, the results of IVW showed that PM _2.5 absorbance was also causally associated with UC ( OR = 1.012; 95% CI = 1.004-1.019, P = 0.002). We observed no causal relationship between PM _2.5, PM _2.5 absorbance, and CD. The results of sensitivity analysis indicated the absence of heterogeneity or pleiotropy, ensuring the reliability of MR results. CONCLUSION Based on two-sample MR analyses, there are potential positive causal relationships between PM _2.5, PM _2.5 absorbance, and UC.
Humans ; Mendelian Randomization Analysis ; Particulate Matter/analysis* ; Polymorphism, Single Nucleotide ; Inflammatory Bowel Diseases/genetics* ; Air Pollutants/analysis* ; Crohn Disease/genetics* ; Colitis, Ulcerative/genetics* ; Genome-Wide Association Study ; Risk Factors ; Environmental Exposure

Hepatic fibrosis is the common key pathological link of various chronic liver diseases and can progress to malignant diseases such as liver cirrhosis and hepatocellular carcinoma； however， there is still a lack of effective targeted therapeutic drugs at present. Traditional Chinese medicine （TCM） has a marked clinical effect in the prevention and treatment of hepatic fibrosis， yet its precise clinical application and global promotion are greatly limited by the complex components of compound prescriptions and unclear mechanism of action. In recent years， multimodal high-throughput omics technology has achieved rapid development， providing strong technical support for elaborating on the scientific connotation of TCM in the treatment of complex diseases due to its advantages of systematic profiling， big-data analytics， and precise target prediction. In particular， integrated transcriptomic， proteomic， and metabolomic strategies comprehensively elucidate key signaling networks， cellular phenotypic transitions， and extracellular matrix metabolic homeostasis modulated by TCM compounds and monomers and assist in the screening and assessment of effective component groups and novel biomarkers. This article systematically reviews the advances in basic research on TCM prevention and treatment of hepatic fibrosis based on multi-omics technologies in the past five years， summarizes the “drug-target-pathway-phenotype” regulatory network， and elaborates on the core mechanisms of TCM in regulating hepatic stellate cell activation and reversing hepatic fibrosis. Future studies should further delve into the interdisciplinary integration and dynamic analytical methodologies of multi-omics technologies， precisely identify the core regulatory target networks modulated by TCM， and systematically unravel the scientific connotation of compatibility rule in compound prescriptions， in order to provide a theoretical basis for developing efficient targeted drugs for hepatic fibrosis and individualized diagnosis and treatment strategies.

Objective To develop models for predicting response to chemotherapy combined with immunotherapy in patients with esophageal squamous carcinoma with various machine learning algorithms,and then select the optimal model.Methods A retrospective study was performed for 174 patients with esophageal squamous cell carcinoma undergoing chemotherapy combined with immunotherapy admitted in Department of Thoracic Surgery of the First Affiliated Hospital of Army Medical University from January 2022 to December 2023.The CT scans and clinical information were collected before treatment.They were randomly divided into a training set(n=122)and a testing set(n=52)in a ratio of 7∶3.CT radiomic features were extracted and selected,and then 5 machine-learning algorithms were employed to establish the prediction models,including radiomics model and clinical-radiomics model.Five-fold cross-validation was conducted on the training set,and the performance of the prediction models was evaluated on the testing set using receiver operating characteristic(ROC)curve and the F1 score.The best-performing model was further explained using local interpretable model-agnostic explanations(LIME)algorithm.Results Among the 174 patients,115(66.1%)achieved clinical remission.From the clinical information and CT images,1 clinical features and 10 radiomic features were identified.The area under of ROC curve(AUC)for the radiomics and clinical-radiomics models was 0.750(95%CI:0.616～0.883),and 0.766(95%CI:0.637～0.895),respectively.The F1 score of the optimal clinical-radiomics model was 0.829.LIME algorithm indicated that this best model demonstrated reliability in predicting individual samples.Conclusion The clinical-radiomics prediction model based on machine learning algorithm performs well,and can provide a reference for doctors'clinical decision-making by predicting the response to chemotherapy combined with immunotherapy in patients with esophageal squamous cell carcinoma.

Objective To evaluate the impact of traditional Chinese medicine(TCM)syndromes on percutaneous microwave ablation(MWA)treatment in patients with colorectal cancer liver metastases(CRLM)and to construct a multidimensional prediction model prognostic based on TCM syndrome classification.Methods Clinical data were collected from 86 CRLM patients who underwent MWA at Zhongshan Hospital of Traditional Chinese Medicine between October 2012 and October 2023.The clinical information covered TCM syndrome types of qi-blood deficiency and spleen-kidney yang deficiency,tumor characteristics,and treatment parameters.Logistic regression was used to screen feature variables for constructing the prognostic predication model,and cross validation was performed to evaluate the prognostic predictive performance.Results(1)Among the 86 CRLM patients,31(36.05%)had spleen-kidney yang deficiency syndrome,while 55(63.95%)had qi-blood deficiency syndrome.(2)Compared to the patients with spleen-kidney yang deficiency syndrome,patients with qi-blood deficiency syndrome exhibited significantly long progression-free survival(PFS)and high progression-free survival rate(P＜0.01),while no significant differences were presented in the incidences of postoperative adverse events such as pain,fever,infection,liver function impairment,or ascites/pleural effusion among the patients with the two syndrome types(P＞0.05).(3)Multivariate logistic regression analysis revealed that TCM syndrome type,maximum diameter of metastatic liver tumor,timing of liver metastasis,and Ki-67 expression rate of primary tumors were the independent prognostic factors for 1-year PFS(P＜0.05 or P＜0.01).(4)The constructed model demonstrated high predictive accuracy for PFS,with an area under the receiver operating characteristic(ROC)curve(AUC)of 0.905(95%CI:0.830-0.980).Conclusion TCM syndromes have significant influence on the prognosis of CRLM patients undergoing MWA,and the patients with qi-blood deficiency syndrome have favorable prognosis.This study pioneers a multidimensional prognostic prediction model integrating TCM syndromes for MWA-treated CRLM patients,highlighting the unique value of TCM syndromes in assessing disease progression and predicting prognosis.

Diabetic nephropathy (DN) is one of the complications of diabetes mellitus,which is accom-panied by severe microvascular lesions.and is also the most common inducing factor of end-stage chronic kid-ney disease.The traditional treatment of DN cannot fundamentally cure diabetes and its complications,and new treatment methods need to be explored urgently.At present,the diagnosis and treatment strategy of using mesenchymal stem cells-derived exosomes (MSCs-exo) has become a research hotspot in the early diagnosis and clinical treatment of DN.A number of studies have confirmed that MSCs-exo has high efficacy and safety in the treatment of DN,and MSCs-exo plays a renoprotective role in diabetic nephropathy models by transfer-ring its own contents to the damaged tissue.This article focuses on the research progress and therapeutic po-tential of mesenchymal stem cell-derived exosomes in regulating autophagy in diabetic nephropathy,in order to provide new ideas and methods for the treatment of DN.

Objective To evaluate the performance of magnetic resonance imaging(MRI)multi-sequence feature imputation and fusion mutual model based on sequence deletion in differentiating high-grade glioma(HGG)from low-grade glioma(LGG).Methods We retrospectively collected multi-sequence MR images from 305 glioma patients,including 189 HGG patients and 116 LGG patients.The region of interest(ROI)of T1-weighted images(T1WI),T2-weighted images(T2WI),T2 fluid attenuated inversion recovery(T2_FLAIR)and post-contrast enhancement T1WI(CE_T1WI)were delineated to extract the radiomics features.A mutual-aid model of MRI multi-sequence feature imputation and fusion based on sequence deletion was used for imputation and fusion of the feature matrix with missing data.The discriminative ability of the model was evaluated using 5-fold cross-validation method and by assessing the accuracy,balanced accuracy,area under the ROC curve(AUC),specificity,and sensitivity.The proposed model was quantitatively compared with other non-holonomic multimodal classification models for discriminating HGG and LGG.Class separability experiments were performed on the latent features learned by the proposed feature imputation and fusion methods to observe the classification effect of the samples in two-dimensional plane.Convergence experiments were used to verify the feasibility of the model.Results For differentiation of HGG from LGG with a missing rate of 10%,the proposed model achieved accuracy,balanced accuracy,AUC,specificity,and sensitivity of 0.777,0.768,0.826,0.754 and 0.780,respectively.The fused latent features showed excellent performance in the class separability experiment,and the algorithm could be iterated to convergence with superior classification performance over other methods at the missing rates of 30%and 50%.Conclusion The proposed model has excellent performance in classification task of HGG and LGG and outperforms other non-holonomic multimodal classification models,demonstrating its potential for efficient processing of non-holonomic multimodal data.

Objective To evaluate the performance of magnetic resonance imaging(MRI)multi-sequence feature imputation and fusion mutual model based on sequence deletion in differentiating high-grade glioma(HGG)from low-grade glioma(LGG).Methods We retrospectively collected multi-sequence MR images from 305 glioma patients,including 189 HGG patients and 116 LGG patients.The region of interest(ROI)of T1-weighted images(T1WI),T2-weighted images(T2WI),T2 fluid attenuated inversion recovery(T2_FLAIR)and post-contrast enhancement T1WI(CE_T1WI)were delineated to extract the radiomics features.A mutual-aid model of MRI multi-sequence feature imputation and fusion based on sequence deletion was used for imputation and fusion of the feature matrix with missing data.The discriminative ability of the model was evaluated using 5-fold cross-validation method and by assessing the accuracy,balanced accuracy,area under the ROC curve(AUC),specificity,and sensitivity.The proposed model was quantitatively compared with other non-holonomic multimodal classification models for discriminating HGG and LGG.Class separability experiments were performed on the latent features learned by the proposed feature imputation and fusion methods to observe the classification effect of the samples in two-dimensional plane.Convergence experiments were used to verify the feasibility of the model.Results For differentiation of HGG from LGG with a missing rate of 10%,the proposed model achieved accuracy,balanced accuracy,AUC,specificity,and sensitivity of 0.777,0.768,0.826,0.754 and 0.780,respectively.The fused latent features showed excellent performance in the class separability experiment,and the algorithm could be iterated to convergence with superior classification performance over other methods at the missing rates of 30%and 50%.Conclusion The proposed model has excellent performance in classification task of HGG and LGG and outperforms other non-holonomic multimodal classification models,demonstrating its potential for efficient processing of non-holonomic multimodal data.