ObjectiveTo develop a predictive model and application for spontaneous passage of common bile duct stones using automated machine learning algorithms given the complexity of treatment decision-making for patients with common bile duct stones， and to reduce unnecessary endoscopic retrograde cholangiopancreatography （ERCP） procedures. MethodsA retrospective analysis was performed for the data of 835 patients who were scheduled for ERCP after a confirmed diagnosis of common bile duct stones based on imaging techniques in Changshu First People’s Hospital （dataset 1） and Changshu Traditional Chinese Medicine Hospital （dataset 2）. The dataset 1 was used for the training and internal validation of the machine learning model and the development of an application， and the dataset 2 was used for external testing. A total of 22 potential predictive variables were included for the establishment and internal validation of the LASSO regression model and various automated machine learning models. The area under the receiver operating characteristic curve （AUC）， sensitivity， specificity， and accuracy were used to assess the performance of models and identify the best model. Feature importance plots， force plots， and SHAP plots were used to interpret the model. The Python Dash library and the best model were used to develop a web application， and external testing was conducted using the dataset 2. The Kolmogorov-Smirnov test was used to examine whether the data were normally distributed， and the Mann-Whitney U test was used for comparison between two groups， while the chi-square test or the Fisher’s exact test was used for comparison of categorical data between groups. ResultsAmong the 835 patients included in the study， 152 （18.20%） experienced spontaneous stone passage. The LASSO model achieved an AUC of 0.875 in the training set （n=588） and 0.864 in the validation set （n=171）， and the top five predictive factors in terms of importance were solitary common bile duct stones， non-dilated common bile duct， diameter of common bile duct stones， a reduction in serum alkaline phosphatase （ALP）， and a reduction in gamma-glutamyl transpeptidase （GGT）. A total of 55 models were established using automated machine learning， among which the gradient boosting machine （GBM） model had the best performance， with an AUC of 0.891 （95% confidence interval： 0.859‍ ‍—‍ ‍0.927）， outperforming the extreme randomized tree mode， the deep learning model， the generalized linear model， and the distributed random forest model. The GBM model had an accuracy of 0.855， a sensitivity of 0.846， and a specificity of 0.857 in the test set （n=76）. The variable importance analysis showed that five factors had important influence on the prediction of spontaneous stone passage， i.e.， were solitary common bile duct stones， non-dilated common bile duct， a stone diameter of <8 mm， a reduction in serum ALP， and a reduction in GGT. The SHAP analysis of the GBM model showed a significant increase in the probability of spontaneous stone passage in patients with solitary common bile duct stones， non-dilated common bile duct， a stone diameter of <8 mm， and a reduction in serum ALP or GGT. ConclusionThe GBM model and application developed using automated machine learning algorithms exhibit excellent predictive performance and user-friendliness in predicting spontaneous stone passage in patients with common bile duct stones. This application can help avoid unnecessary ERCP procedures， thereby reducing surgical risks and healthcare costs.

Conjunctival lymphangiectasia is a low-incidence ocular surface disease that is currently rarely reported in the relevant literature. It may be related to cosmetic eyelid surgery, tumor, radiation or chemotherapy and other factors and often causes a foreign body sensation, lacrimation, eye pain, visual fatigue and other discomfort. These symptoms of constant eye irritation affect the patient's quality of life. At present, anterior segment optical coherence tomography can be used for clinical diagnosis, and the novel monoclonal antibody D2-40, as a marker of lymphatic endothelial cell dilatation, has high specificity in pathological diagnosis. Previous studies have not fully defined the pathogenesis of the disease, and treatment methods vary. Conventional treatment has resulted in varying degrees of damage to the conjunctiva in patients. In recent years, anti-vascular endothelial growth factor drugs have been reported to be effective in treating the disease with few complications. This article reviews the pathogenesis, diagnosis and treatment of this rare disease in order to gain a better understanding of conjunctival lymphangiectasia and provide more support for clinical diagnosis and treatment.

Objective:To construct and validate a predictive model and application program for stone recurrence after endoscopic retrograde cholangiopancreatography (ERCP) based on machine learning algorithms in patients with common bile duct stones (CBDS).Methods:A multicenter retrospective cohort study was conducted, 862 CBDS patients underwent ERCP from June 2020 to September 2023 in Changshu First People′s Hospital (data set 1, 759 cases, including a training set of 588 cases and a validation set of 171 cases) and Changshu Hospital of Traditional Chinese Medicine (data set 2, 103 cases, used as a test set). The demographics, medical history, ERCP procedural records and laboratory indices were collected. All patients were followed up for 1 year, and the stone recurrence was recorded. In training set, the feature selection was conducted by the least absolute shrinkage and selection operator (LASSO) algorithm, and a conventional Logistic regression model was constructed based on selected features. The 3 machine learning algorithms (gradient boosting machine model, extreme gradient boosting model and random forest model) and a conventional Logistic regression model (LASSO model) were trained to fit predictive models. The model performance was assessed by area under curve (AUC) of receiver operating characteristic curve. The model interpretability was analyzed by feature importance evaluation, Shapley additive explanations (SHAP) and force plots. The best-performing model was deployed as an online application by Streamlit framework (V1.36.0).Results:Among the 862 patients, 158 patients (18.33%) developed stone recurrence after ERCP. There were no statistical difference in demographics, medical history, ERCP procedural records and laboratory indices between training set and a validation set ( P>0.05). LASSO regression analysis result showed that 6 key variables (in descending order of significance: endoscopic sphincterotomy, common bile duct angulation, stone diameter, stone count, common bile duct diameter, and periampullary diverticulum) influencing stone recurrence. ROC curve analysis result showed that the random forest model exhibited the highest predictive performance (it had the largest AUC of 0.900). SHAP analysis result showed that common bile duct angulation, common bile duct diameter, stone diameter, endoscopic sphincterotomy and stone count were the top 5 contributing factors in the random forest model. Using Python, the random forest model was implemented into a Streamlit-based application with a user-friendly visual interface, providing predictive outcomes, confidence levels, SHAP force diagram and health recommendations. In the test set, the application program achieved an accuracy of 84.5% (87/103), sensitivity of 82.6% (19/23), and specificity of 85.0% (68/80). SHAP plots and force diagram intuitively illustrated the impact of key features on stone recurrence prediction, offering a clear visualization of each variable′s role within the model. Conclusions:The predictive model and application program based on the random forest machine learning algorithms demonstrate excellent predictive performance and practical usability in predicting stone recurrence after ERCP in patients with CBDS.

Objective To develop a combined model integrating radiomics and 3D deep learning features for improving the predictive efficacy of overall survival in non-small cell lung cancer(NSCLC)patients undergoing radiotherapy,thereby providing a foundation for optimizing individualized radiotherapy strategies.Methods A retrospective analysis was conducted on 522 NSCLC patients from 3 centers.Radiomics features were extracted from the tumor region of interest on radiotherapy planning CT scans,and a 3D-SE-ResNet was constructed to extract deep learning features.Following feature extraction,features were selected via univariate Cox analysis and Lasso-Cox regression,and a combined model was established by fusing the two feature types through principal component analysis.The discriminative ability of the model was evaluated using the concordance index(C-index)and the area under the receiver operating characteristic curve(AUC),while the risk stratification efficacy was verified by Kaplan-Meier survival analysis.Results The predictive performance of deep learning features was significantly superior to that of radiomics features(C-index:0.73 vs 0.65).The combined model achieved the highest predictive performance in the training set,internal test set,and external test set(C-index:0.74,0.69,0.72 respectively),with higher AUC values for predicting 1-year,2-year,and 3-year OS than either single model.Kaplan-Meier analysis showed significant differences in survival between the high-and low-risk groups(Log-rank test,P<0.001),and calibration curves indicated good consistency between predicted and actual survival outcomes.Conclusion The combined model integrating radiomics and 3D deep learning features can accurately predict survival outcomes in NSCLC patients undergoing radiotherapy.The multi-center validation results support its potential application in prognosis stratification for individualized radiotherapy.

Objective To develop a combined model integrating radiomics and 3D deep learning features for improving the predictive efficacy of overall survival in non-small cell lung cancer(NSCLC)patients undergoing radiotherapy,thereby providing a foundation for optimizing individualized radiotherapy strategies.Methods A retrospective analysis was conducted on 522 NSCLC patients from 3 centers.Radiomics features were extracted from the tumor region of interest on radiotherapy planning CT scans,and a 3D-SE-ResNet was constructed to extract deep learning features.Following feature extraction,features were selected via univariate Cox analysis and Lasso-Cox regression,and a combined model was established by fusing the two feature types through principal component analysis.The discriminative ability of the model was evaluated using the concordance index(C-index)and the area under the receiver operating characteristic curve(AUC),while the risk stratification efficacy was verified by Kaplan-Meier survival analysis.Results The predictive performance of deep learning features was significantly superior to that of radiomics features(C-index:0.73 vs 0.65).The combined model achieved the highest predictive performance in the training set,internal test set,and external test set(C-index:0.74,0.69,0.72 respectively),with higher AUC values for predicting 1-year,2-year,and 3-year OS than either single model.Kaplan-Meier analysis showed significant differences in survival between the high-and low-risk groups(Log-rank test,P<0.001),and calibration curves indicated good consistency between predicted and actual survival outcomes.Conclusion The combined model integrating radiomics and 3D deep learning features can accurately predict survival outcomes in NSCLC patients undergoing radiotherapy.The multi-center validation results support its potential application in prognosis stratification for individualized radiotherapy.

Objective:To construct and validate a predictive model and application program for stone recurrence after endoscopic retrograde cholangiopancreatography (ERCP) based on machine learning algorithms in patients with common bile duct stones (CBDS).Methods:A multicenter retrospective cohort study was conducted, 862 CBDS patients underwent ERCP from June 2020 to September 2023 in Changshu First People′s Hospital (data set 1, 759 cases, including a training set of 588 cases and a validation set of 171 cases) and Changshu Hospital of Traditional Chinese Medicine (data set 2, 103 cases, used as a test set). The demographics, medical history, ERCP procedural records and laboratory indices were collected. All patients were followed up for 1 year, and the stone recurrence was recorded. In training set, the feature selection was conducted by the least absolute shrinkage and selection operator (LASSO) algorithm, and a conventional Logistic regression model was constructed based on selected features. The 3 machine learning algorithms (gradient boosting machine model, extreme gradient boosting model and random forest model) and a conventional Logistic regression model (LASSO model) were trained to fit predictive models. The model performance was assessed by area under curve (AUC) of receiver operating characteristic curve. The model interpretability was analyzed by feature importance evaluation, Shapley additive explanations (SHAP) and force plots. The best-performing model was deployed as an online application by Streamlit framework (V1.36.0).Results:Among the 862 patients, 158 patients (18.33%) developed stone recurrence after ERCP. There were no statistical difference in demographics, medical history, ERCP procedural records and laboratory indices between training set and a validation set ( P>0.05). LASSO regression analysis result showed that 6 key variables (in descending order of significance: endoscopic sphincterotomy, common bile duct angulation, stone diameter, stone count, common bile duct diameter, and periampullary diverticulum) influencing stone recurrence. ROC curve analysis result showed that the random forest model exhibited the highest predictive performance (it had the largest AUC of 0.900). SHAP analysis result showed that common bile duct angulation, common bile duct diameter, stone diameter, endoscopic sphincterotomy and stone count were the top 5 contributing factors in the random forest model. Using Python, the random forest model was implemented into a Streamlit-based application with a user-friendly visual interface, providing predictive outcomes, confidence levels, SHAP force diagram and health recommendations. In the test set, the application program achieved an accuracy of 84.5% (87/103), sensitivity of 82.6% (19/23), and specificity of 85.0% (68/80). SHAP plots and force diagram intuitively illustrated the impact of key features on stone recurrence prediction, offering a clear visualization of each variable′s role within the model. Conclusions:The predictive model and application program based on the random forest machine learning algorithms demonstrate excellent predictive performance and practical usability in predicting stone recurrence after ERCP in patients with CBDS.

Hepatic encephalopathy （HE） is a common severe liver disease syndrome in clinical practice and is one of the critical and severe diseases in internal medicine， and more than half of liver failure patients diagnosed with overt HE have a survival time of less than 1 year. A comprehensive analysis of the complex pathogenesis of HE and the development of diagnosis and treatment regimens based on evidence-based medicine are of great importance for alleviating high medical resource consumption， high medical expenses， and high incidence and mortality rates in clinical practice. The latest studies have shown that the intestinal tract and the central nervous system can perform bidirectional continuous interaction and signal transmission and regulate the function of inflammation signals， molecules， cells， and organs， which is known as neuroimmune communication and is highly consistent with the main pathological features of HE. With a focus on the mechanism of neuroimmune communication in HE， this article reviews the association between inflammation signal transduction via the gut-brain axis and neurotransmitter regulation and its role in neuroimmune communication in HE， which provides new ideas for the clinical diagnosis and treatment of HE and the research and development of related drugs.

Objective To investigate the role of Taraxasterol(TAR)on ferroptosis in chondrocytes induced by Erastin.Methods The C28/I2 chondrocyte line was treated with Erastin to construct the ferroptosis model of chon-drocytes in vitro and the experiments were divided into Control,Erastin,TAR,and TAR+Erastin groups.Cell via-bility was detected by the CCK-8 assay.Cytotoxicity was detected by the lactate dehydrogenase(LDH)kit and the Calcein/PI cytokinesis kit.Flow cytometry was used to detect lipid reactive oxygen species(ROS).The intracellular glutathione(GSH)content was detected by GSH kit.Mitochondrial membrane potential was detected by JC-1 stai-ning and RH123 staining.ACSL4 and GPX4 protein expression and the key indicators of ferroptosis were detected by Western blot.Results TAR restored the decreased cell viability of C28/I2 chondrocytes induced by Erastin treatment as well as reduced Erastin-induced cytotoxicity(P＜0.01).Compared with the control group,the level of intracellular lipid ROS increased(P＜0.01)and the content of GSH decreased(P＜0.01)after treatment with Erastin,while TAR could reduce the production of lipid ROS(P＜0.01)and increase the content of GSH(P＜0.01).TAR restored mitochondrial membrane potential in C28/I2 chondrocytes ferroptosis,decreased ACSL4 pro-tein expression(P＜0.01)and increased GPX4 protein expression(P＜0.01).In addition,TAR restored the re-duced cell viability caused by IL-1 β treatment.Conclusion TAR can inhibit Erastin induced ferroptosis in C28/I2 chondrocytes,which may be related to the regulation of ACSL4 and GPX4 protein expression.

【Objective】 To explore the clinicopathological characteristics and comprehensive treatment strategies of prostate mucosa adenocarcinoma under multidisciplinary diagnosis and treatment (MDT) mode. 【Methods】 Data of two patients with typical prostate mucosa adenocarcinoma treated in our hospital during Sep.2020 and Apr.2023 were retrospectively analyzed. 【Results】 In case 1, the clinical manifestation was macroscopic hematuria; multiparametric magnetic resonance imaging (mpMRI) indicated solid prostatic nodules, clinical stage T4N1Mx; initial prostate specific antigen (PSA) was 1.2 ng/mL; ⁶⁸68Ga-prostate specific membrane antigen PET/CT (⁶⁸Ga-PSMA PET/CT) suggested abnormal uptake of nuclear lesions in the prostate (SUV4.2-5.3); biopsy results indicated invasive mucinous adenocarcinoma.After prostate and pelvic field radiotherapy + androgen deprivation therapy (ADT) + antihypertensive treatment, lesions were significantly reduced, and hematuria symptoms were relieved.In case 2, the clinical manifestation was dysuria; initial PSA was 91.78 ng/mL; mpMRI suggested invasion of prostate mass into the bladder and clinical stage of T4N1M1b; ⁶⁸Ga-PSMA PET/CT indicated prostate and pelvic lymph nodes, and multiple bone lesions showed increased nuclide uptake; biopsy results indicated prostate adenocarcinoma with mucinous adenocarcinoma.Initial endocrine treatment was performed.After 3 months, PSA was reduced to 0.083 ng/mL, and imaging showed the tumor was significantly reduced.Robotic-assisted laparoscopic tumor prostatectomy with extended pelvic lymph node dissection was performed, and endocrine adjuvant therapy was continued after surgery. 【Conclusion】 Prostate mucosa adenocarcinoma has different clinicopathological characteristics and prognosis from conventional acinar adenocarcinoma, and the whole-process management under MDT mode is of great value in the diagnosis and treatment of this disease.

【Objective】 To systematically evaluate the efficacy and safety of targeted drugs in the treatment of metastatic non-clear cell renal cell carcinoma (nccRCC) and to provide guidance for clinical treatment. 【Methods】 All observational studies and randomized controlled trials (RCTs) of nccRCC treated with targeted drugs were retrieved from the PubMed, Embase, the Cochrane Library and Web of Science. Three independent investigators screened the literature, extracted data and evaluated the quality of literature. The RCTs were evaluated using the Cochrane Handbook. One research with insufficient outcome data (follow-up bias) was assessed as high risk, and the other studies showed low or uncertain risk. The non-RCTs were evaluated with the JBI Quality Assessment Tool, and all studies displayed a low risk of bias. The data were analyzed with Stata 17.0 software. 【Results】 A total of 16 studies involving 989 patients were included. Meta-analysis showed that the objective response rate (ORR) was 12.6% (95%CI:8.1%-17.9%), the total disease control rate (DCR) was 65.3% (95%CI:58.3%-72.1%), the total median progression-free survival (PFS) was 5.80 (95%CI:4.69-6.91) months, and the median overall survival (OS) was 15.93 (95%CI:12.17-19.68) months. In subgroup analysis, the total ORR of patients with metastatic nccRCC treated with sunitinib and cabozantinib were 11.7% (95%CI:6.5%-18.0%) and 17.2% (95%CI:8.4%-28.2%), respectively. The total ORR of patients with papillary renal cell carcinoma was 9.1% (95%CI:2.4%-18.9%). 【Conclusion】 Targeted drugs have a significant effect on patients with metastatic nccRCC, but adverse reactions may occur. Targeted drugs have poor effects on metastatic papillary renal cell carcinoma, and cabozantinib may have greater survival benefits.