ObjectiveThis paper aims to explore the risk factors for chronic atrophic gastritis （CAG） with turbidity toxin accumulation syndrome and establish a prediction model. MethodsClinical data of 180 patients with CAG who participated in the "clinical study of Xianglian Huazhuo Particles blocking CAG cancer transformation" of Hebei Sheng Zhong Yi Yuan from July 2021 to March 2022 were collected. After confounding factors were controlled by propensity score matching， patients were divided into a training set （namely dev） and a validation set （namely vad） in a seven to three ratio. The risk factors for CAG with turbidity toxin accumulation syndrome in the training set were investigated by using univariate Logistic regression analysis and least absolute shrinkage and selection operator （namely Lasso） regression algorithms. Subsequently， a model， named model 1se， was developed by using the training set data to predict the risk factors for CAG with turbidity toxin accumulation syndrome. The accuracy of the prediction model was assessed by using various methods， including the receiver operating characteristic （ROC） curve， Hosmer-Lemeshow test （H-L）， calibration plot， and decision curve analysis （DCA）. ResultsAge， body mass index （BMI）， family history of cancer， job and life satisfaction， yellow and greasy fur with slippery pulse， and heavy body sensation were independent risk factors of the model. The prediction model showed excellent predictive value for both the training and validation sets. ConclusionThe established prediction model for CAG with turbidity toxin accumulation syndrome has high discrimination and excellent calibration， which could provide an excellent clinical basis for disease diagnosis and individualized treatment of patients.

ObjectiveThis paper aims to explore the risk factors for chronic atrophic gastritis （CAG） with turbidity toxin accumulation syndrome and establish a prediction model. MethodsClinical data of 180 patients with CAG who participated in the "clinical study of Xianglian Huazhuo Particles blocking CAG cancer transformation" of Hebei Sheng Zhong Yi Yuan from July 2021 to March 2022 were collected. After confounding factors were controlled by propensity score matching， patients were divided into a training set （namely dev） and a validation set （namely vad） in a seven to three ratio. The risk factors for CAG with turbidity toxin accumulation syndrome in the training set were investigated by using univariate Logistic regression analysis and least absolute shrinkage and selection operator （namely Lasso） regression algorithms. Subsequently， a model， named model 1se， was developed by using the training set data to predict the risk factors for CAG with turbidity toxin accumulation syndrome. The accuracy of the prediction model was assessed by using various methods， including the receiver operating characteristic （ROC） curve， Hosmer-Lemeshow test （H-L）， calibration plot， and decision curve analysis （DCA）. ResultsAge， body mass index （BMI）， family history of cancer， job and life satisfaction， yellow and greasy fur with slippery pulse， and heavy body sensation were independent risk factors of the model. The prediction model showed excellent predictive value for both the training and validation sets. ConclusionThe established prediction model for CAG with turbidity toxin accumulation syndrome has high discrimination and excellent calibration， which could provide an excellent clinical basis for disease diagnosis and individualized treatment of patients.

OBJECTIVES: Esophageal squamous cell carcinoma (ESCC) is characterized by complex pathogenesis and poor prognosis. In recent years, epithelial-mesenchymal transition (EMT) in tumor initiation and progression has attracted increasing attention. Enhancer of zeste homolog 2 (EZH2), which is aberrantly expressed in various tumors, may be closely related to the EMT process. This study aims to examine the expression and correlation of EZH2 and EMT markers in ESCC cells and tissues, evaluate the effects of EZH2 knockdown on ESCC cell proliferation, invasion, and migration, and explore how EZH2 contributes to the malignant biological behavior of ESCC. METHODS: Bioinformatics analyses were used to assess EZH2 expression levels in ESCC. Small interfering RNA was used to knock down EZH2 in ESCC cell lines EC109 and EC9706. Cell proliferation, invasion, and migration were evaluated using cell counting kit-8 (CCK-8), wound healing, and Transwell assays. Protein and mRNA expression levels of EZH2, E-cadherin (E-cad), and vimentin (Vim) were detected by Western blotting and real time fluorogenic quantitative PCR (RT-qPCR), respectively. Immunohistochemical (IHC) staining was performed on 70 ESCC tissue samples and 40 paired adjacent normal tissues collected from the First Affiliated Hospital of Shihezi University between 2010 and 2016 to assess the expression of EZH2, E-cad, and Vim, and to analyze their associations with clinicopathological feature and patient prognosis. RESULTS: Bioinformatics analysis showed that EZH2 was highly expressed in ESCC (P<0.001), and high EZH2 expression was associated with worse prognosis (P<0.001). CCK-8, wound healing, and Transwell assays demonstrated that EZH2 knockdown significantly suppressed the proliferation, invasion, and migration of ESCC cells (P<0.001). In addition, Vim expression was significantly reduced, while E-cad expression was significantly increased at both protein and mRNA levels in EZH2-silenced cells (all P<0.05). IHC staining analysis revealed higher expression of EZH2 and Vim and lower expression of E-cad in ESCC tissues compared to adjacent normal tissues. Kaplan-Meier survival analysis showed that low expression of EZH2 and Vim and high expression of E-cad were associated with longer survival (all P<0.05). CONCLUSIONS EZH2 promotes malignant biological behavior in ESCC by mediating EMT. Elevated EZH2 expression is associated with poor prognosis in ESCC patients.
Humans ; Enhancer of Zeste Homolog 2 Protein/physiology* ; Esophageal Squamous Cell Carcinoma/pathology* ; Epithelial-Mesenchymal Transition/genetics* ; Esophageal Neoplasms/metabolism* ; Cell Proliferation ; Cell Line, Tumor ; Cell Movement ; Cadherins/genetics* ; Vimentin/genetics* ; Male ; Female ; Middle Aged ; Neoplasm Invasiveness ; Prognosis ; RNA, Small Interfering/genetics* ; Gene Expression Regulation, Neoplastic

Benzylisoquinoline alkaloids (BIAs) are a structurally diverse group of plant metabolites renowned for their pharmacological properties. However, sustainable sources for these compounds remain limited. Consequently, researchers are focusing on elucidating BIA biosynthetic pathways and genes to explore alternative sources using synthetic biology approaches. CYP80B, a family of cytochrome P450 (CYP450) enzymes, plays a crucial role in BIA biosynthesis. Previously reported CYP80Bs are known to catalyze the 3'-hydroxylation of (S)-N-methylcoclaurine, with the N-methyl group essential for catalytic activity. In this study, we successfully cloned a full-length CYP80B gene (StCYP80B) from Stephania tetrandra (S. tetrandra) and identified its function using a yeast heterologous expression system. Both in vivo yeast feeding and in vitro enzyme analysis demonstrated that StCYP80B could catalyze N-methylcoclaurine and coclaurine into their respective 3'-hydroxylated products. Notably, StCYP80B exhibited an expanded substrate selectivity compared to previously reported wild-type CYP80Bs, as it did not require an N-methyl group for hydroxylase activity. Furthermore, StCYP80B displayed a clear preference for the (S)-configuration. Co-expression of StCYP80B with the CYP450 reductases (CPRs, StCPR1, and StCPR2), also cloned from S. tetrandra, significantly enhanced the catalytic activity towards (S)-coclaurine. Site-directed mutagenesis of StCYP80B revealed that the residue H205 is crucial for coclaurine catalysis. Additionally, StCYP80B exhibited tissue-specific expression in plants. This study provides new genetic resources for the biosynthesis of BIAs and further elucidates their synthetic pathway in natural plant systems.
Cytochrome P-450 Enzyme System/chemistry* ; Benzylisoquinolines/chemistry* ; Hydroxylation ; Plant Proteins/chemistry* ; Alkaloids/metabolism* ; Stephania tetrandra/genetics*

Objective:To explore the safety and effectiveness of ileal bypass with stent to prevent anastomotic leakage in Da Vinci robot anus-preserving surgery for low rectal cancer.Methods:From Dec 2021 to Sep 2024, the clinical data of 55 patients undergoing robot anus-preserving surgery and stent ileal bypass for low rectal cancer in the First Affiliated Hospital of Ningbo University was analyzed retrospectively.Results:All the 55 patients successfully completed the operation. Among them, 47 patients underwent low anterior resection and 8 patients did sphincter resection. The total operation time was (207±37)min, the operation time of ileal bypass was (24±3)min, and the intraoperative blood loss was (32±18)ml. After operation, the time of stent disintegration was (23.93± 2.87) days, and the time of catheter removal was (29.55 ± 3.21) days. By postoperative TNM staging, there were 12 cases in T1 stage, 26 cases in T2 and 17 cases in T3. The number of lymph node dissection was (16.84±4.50), 15 cases being positive for lymph node metastasis, 40 cases being negtive for lymph node metastasis, 36 cases were stage Ⅰ, 4 cases were stage Ⅱ and 15 cases were stage Ⅲ. The median follow-up time was 20 (2-35) months. Delayed anastomotic leakage occurred in 1 patient and anastomotic stenosis occurred in 2 patients.Conclusion:Stent ileal bypass is safe and effective to prevent anastomotic leakage in Da Vinci robot anus-preserving surgery for low rectal cancer.

AIM:To investigate the immunosuppressive effects of M2-like tumor-associated macrophages(M2-TAMs)on CD8+T cells within the tumor microenvironment of esophageal cancer.METHODS:Multiplex fluores-cence immunohistochemistry was used to analyze the distribution of immune cells in esophageal cancer tissues.An in vitro co-culture system was established,and flow cytometry along with Calcein-AM/PI staining was employed to assess the im-pact of M2-TAMs on CD8+T cell function.The GEPIA database was utilized to evaluate the prognostic significance of PDCD1 expression in esophageal cancer patients and to analyze the correlations between gene expressions.Immunohisto-chemistry(IHC)was performed to detect the expression of TGF-β1 in esophageal cancer tissues.Flow cytometry and en-zyme-linked immunosorbent assay(ELISA)were used to measure PD-1,IFN-γ and TNF-α expression in CD8+T cells fol-lowing treatment with a TGF-β1 inhibitor.RESULTS:Compared with early-stage(stage I)esophageal cancer patients,the patients with advanced disease(stages Ⅱ to Ⅳ)exhibited dynamic changes in the infiltration of CD4+T cells,CD8+T cells,Tregs,and M2-TAMs within tumor tissues,with significant correlations observed among these cell populations(P<0.05).The distribution of M2-TAMs and Tregs was positively correlated with poor prognosis(P<0.05),while that of CD8+T cells was negatively correlated(P<0.05).In contrast,CD4+T cell infiltration showed no significant association with clinical outcomes(P>0.05).Co-culture of CD8+T cells with M2-TAMs resulted in significant downregulation of CD107a,granzyme B,IFN-γ and TNF-α expression(P<0.01).Additionally,M2-TAM-treated CD8+T cells co-cultured with esophageal cancer cells led to reduced apoptosis of cancer cells.High expression of PDCD1 was significantly associated with poor prognosis(P<0.05),and significant correlations were observed between CD8A and PDCD1 expression,as well as between TGF-β1 and CD274 gene expression(P<0.01).TGF-β1 was also significantly associated with CD163+macro-phage infiltration and the progression of esophageal cancer(P<0.05).Treatment with a TGF-β1 inhibitor in the M2-TAM and CD8+T cell co-culture system significantly down-regulated PD-1 expression and increased the secretion of IFN-γ and TNF-α(P<0.01).CONCLUSION:The TGF-β1 derived from M2-TAMs inhibits the antitumor activity of CD8+T cells in the esophageal cancer microenvironment,suggesting potential therapeutic targets for overcoming immunosuppression in esophageal cancer.

Objective To compare the safety and efficacy of directional atherectomy with anti-restenosis therapy(DAART,drug-coated balloon)versus conventional balloon angioplasty(bare-metal stent,BMS)in elderly patients with femoropopliteal artery disease.Methods A retrospec-tive cohort study was conducted on 116 elderly patients undergoing endovascular intervention due to femoropopliteal artery disease in our hospital between May 2016 and September 2019,divided into DAART group(57 cases)and BMS group(59 cases).Results No statistical differences were observed between the DAART and BMS groups in terms of age,risk factors,distribution of Ruth-erford classification,lesion length,lesion type,lesion location,Global Limb Anatomic Staging Sys-tem grade,infra-popliteal runoff status,or preoperative ankle-brachial index(P＞0.05).However,the BMS group had significantly larger proportion of chronic limb-threatening ischemia than the other group(P＜0.05).Both groups achieved a 100％success rate of surgery.The DAART group obtained obviously higher primary patency rates at 1 and 2 years than the BMS group(90.4％vs 75.0％,76.3％vs 57.3％;P＜0.045).There were no significant differences in the rate of freedom from target lesion revascularization at 1 and 2 years between the two group(96.1％vs 88.8％,91.6％vs 77.7％;P＞0.05).In 2 years of follow-up,the incidence of major adverse events was 10.0％(5 cases)in the DAART group and 20.8％(12 cases)in the BMS group,but no obvious difference(P＞0.05).Conclusion DAART demonstrates superior mid-term efficacy than BMS in treating femoropopliteal artery disease in elderly patients.However,comprehensive preoperative assessment is essential to optimize individualized treatment strategies for this population.

Radiation-induced thrombocytopenia(RIT)faces a perplexing challenge in the clinical treatment of cancer patients,and current therapeutic approaches are inadequate in the clinical settings.In this research,oxy-matrine,a new molecule capable of healing RIT was screened out,and the underlying regulatory mecha-nism associated with magakaryocyte(MK)differentiation and thrombopoiesis was demonstrated.The capacity of oxymatrine to induce MK differentiation was verified in K-562 and Meg-01 cells in vitro.The ability to induce thrombopoiesis was subsequently demonstrated in Tg(cd41:enhanced green fluorescent protein(eGFP))zebrafish and RIT model mice.In addition,we carried out network pharmacological pre-diction,drug affinity responsive target stability assay(DARTS)and cellular thermal shift assay(CETSA)analyses to explore the potential targets of oxymatrine.Moreover,the pathway underlying the effects of oxymatrine was determined by Kyoto Encyclopedia of Genes and Genomes(KEGG)enrichment analyses,Western blot(WB),and immunofluorescence.Oxymatrine markedly promoted MK differentiation and maturation in vitro.Moreover,oxymatrine induced thrombopoiesis in Tg(cd41:eGFP)zebrafish and accelerated thrombopoiesis and platelet function recovery in RIT model mice.Mechanistically,oxymatrine directly binds to toll-like receptor 2(TLR2)and further regulates the downstream pathway stimulator of interferon genes(STING)/nuclear factor-kappaB(NF-κB),which can be blocked by C29 and C-176,which are specific inhibitors of TLR2 and STING,respectively.Taken together,we demonstrated that oxymatrine,a novel TLR2 agonist,plays a critical role in accelerating MK differentiation and thrombopoiesis via the STING/NF-κB axis,suggesting that oxymatrine is a promising candidate for RIT therapy.

Objective:To construct a predictive model for postoperative delirium (POD) in patients undergoing gastrointestinal surgery using machine learning.Methods:This retrospective study used clinical data from patients who underwent gastrointestinal surgery at Subei People′s Hospital between September 2022 and April 2024. The entire dataset was randomly divided into the training and validation sets in an 8∶2 ratio. Multivariate logistic regression analysis was conducted to identify the factors influencing POD. Eleven machine learning models were established and compared. The performance of the models was validated using metrics, including accuracy, precision, recall, Youden′s index, F1 score, Matthews′ correlation coefficient, Kappa coefficient, log loss, and Brier score. Receiver operating characteristic and calibration curves were plotted to assess the discrimination and consistency of the model. Shapley additive explanations were used in Python for interpretative analysis of the model with the best predictive performance, and the importance of the feature parameters was ranked.Results:A total of 1, 785 patients were ultimately included, of which 833 (46.67%) experienced POD. The results of multivariate logistic regression analysis revealed that advanced age, lower preoperative serum calcium ion concentration, postoperative pulmonary infection, and higher preoperative systolic blood pressure were independent risk factors for POD in patients undergoing gastrointestinal surgery, while laparoscopic surgery was a protective factor ( P<0.05). Among the 11 machine learning models, the categorical feature gradient boosting model exhibited the best performance, with an area under the receiver operating characteristic curve of 0.82 (95% confidence interval 0.77-0.87). The ranking of feature importance indicated that age had the greatest contribution in predicting POD. Conclusions:The predictive model for POD established based on the categorical boosting algorithm has higher predictive efficacy and clinical application value in patients undergoing gastrointestinal surgery.

Objective:To develop and validate a deep learning-based multimodal model integrating clinical and radiomic features for predicting acute kidney injury（AKI）risk after partial nephrectomy.Methods:A retrospective analysis was conducted on 416 patients who underwent partial nephrectomy at Zhongshan Hospital，Fudan University from January 2023 to January 2025. The cohort included 100 AKI patients［defined by a ≥ 25% reduction in postoperative evaluated glomerular filtration rate（eGFR）within 48 hours sustained for >24 hours］and 316 non-AKI patients（1∶3 ratio，randomly matched with 16 additional cases for redundancy）. Clinical and radiomic features were extracted from preoperative contrast-enhanced CT scans using PyRadiomics. Demographics included 259 males and 158 females，with a median age of 57（49，65）years，body mass index of（24.1 ± 3.3）kg/m2，preoperative eGFR of（88.5 ± 18.3）ml/（min·1.73 m2），postoperative eGFR（48-hour）of（76.0 ± 21.9）ml/（min·1.73 m2），Zhongshan Score（ZSscore）of 7.34 ± 2.01，and R.E.N.A.L. score of 7.50 ± 1.71. All tumors were T 1a stage. Patients were divided into training（n = 312）and test（n = 104）sets（3∶1 ratio）. A clinical model was constructed via multivariate logistic regression，while radiomic and combined（clinical + radiomic）models utilized an artificial neural network（ANN）with 1 input layer，5 hidden layers，1 output layer，and 10 5 training epochs. Model performance was evaluated by using receiver operating characteristic（ROC）curves and area under the curve（AUC），and was compared to the Martini model. Feature contributions were interpreted via SHapley Additive exPlanations（SHAP）. Results:In the test set，the results of multivariate logistic regression showed that patient’s weight，preoperative eGFR，R.E.N.A.L. score，surgical approach，and operation time were risk factors for AKI（ P < 0.05）. The AUC of the clinical feature prediction model constructed based on the above factors was 0.852（95% CI 0.775?0.929）. In the test set，the AUC of the Martini model was 0.725（95% CI 0.565?0.791）. The radiomic model，trained on 1 315 imaging features，achieved an AUC of 0.898（95% CI 0.804?0.993）with 94.2%（98/104）accuracy. The combined clinical and radiomic model，integrating 1 315 radiomic features and clinical features，demonstrated superior performance with an AUC of 0.946（95% CI 0.887?1.000）and 96.2%（100/104）accuracy，outperforming both the clinical model（ P = 0.03）and the Martini model（ P < 0.01）. SHAP analysis identified the top five predictors in the combined model：ZSscore（SHAP value：0.78），long-run low gray-level emphasis（SHAP value：0.61），run-length non-uniformity（SHAP value：0.58），size-zone non-uniformity（SHAP value：0.46），and gray-level co-occurrence matrix joint energy（SHAP value：0.36）. Conclusions:The deep learning-based multimodal model integrating clinical and radiomic features accurately predicts AKI risk after partial nephrectomy，offering a novel strategy for preoperative risk stratification and personalized intervention.