Objective To observe the efficacy and safety of neoadjuvant sintilimab combined with chemotherapy in the treatment of patients with locally advanced resectable esophageal squamous cell carcinoma (ESCC). Methods Clinical data were collected from patients with locally advanced resectable ESCC who received neoadjuvant immunotherapy combined with chemotherapy followed by surgical treatment at the Department of Thoracic Surgery of Jining No.1 People's Hospital from April 2020 to April 2022. The endpoints included major pathological response (MPR), pathological complete response (pCR), R0 resection rate, safety, and postoperative survival. Results A total of 43 patients with ESCC who received at least one cycle of neoadjuvant immunotherapy before surgery were included. Among them, there were 31 males and 12 females with a median age of 65 years (interquartile range: 58-69). All patients successfully completed the surgery without any surgical delays. The pCR rate was 14.0% (6/43), the MPR rate was 58.1% (25/43), and the R0 resection rate was 97.7% (42/43). Patients exhibited reliable safety during neoadjuvant therapy and postoperatively. The 2-year overall survival and disease-free survival rates were 90.7% and 81.4%, respectively. Kaplan-Meier survival analysis and log-rank test revealed lower recurrence rates and better survival in the MPR patients compared to the non-MPR patients. Conclusion The combination of neoadjuvant sintilimab and chemotherapy in the treatment of patients with locally advanced resectable ESCC has demonstrated significant clinical efficacy, while also being safe and reliable.

Colorectal cancer is the most common malignant tumor of digestive tract, and the incidence of colorectal cancer in China is especially characterized by middle and low rectal cancer. In recent years, with the progress of computer science, artificial intelligence technology has developed rapidly, and has achieved a lot of application results in the medical field. At present, artificial intelligence technology has covered various stages of colorectal cancer, including screening, individualized assessment, auxiliary diagnosis and treatment decision-making, refined surgery and prognosis judgment, providing help for the accurate and individualized treatment of rectal cancer. However, the lack of standardized, systematic, and scalable AI models remains a major pain point for the field. Therefore, it is necessary to carry out large-scale prospective clinical studies on artificial intelligence model to further confirm its application value in the clinical diagnosis and treatment of rectal cancer.

Surgery is the preferred treatment for resectable esophageal cancer, but in locally advanced esophageal cancer, the effect of surgery alone is not ideal, so surgery-based comprehensive treatment is the best option. Neoadjuvant therapy has become a standard treatment in the treatment of locally advanced resectable esophageal cancer. Neoadjuvant therapy includes neoadjuvant chemotherapy, radiochemotherapy, immunotherapy, targeted therapy, etc. With the significant efficacy and acceptable toxicity of immunotherapy in the first-line and second-line treatment of advanced esophageal cancer, neoadjuvant immunotherapy has become a research hotspot of locally advanced resectable esophageal cancer. This article reviews the latest research progress and some limitations of neoadjuvant immunotherapy in locally advanced resectable esophageal cancer.

Colorectal cancer is the most common malignant tumor of digestive tract, and the incidence of colorectal cancer in China is especially characterized by middle and low rectal cancer. In recent years, with the progress of computer science, artificial intelligence technology has developed rapidly, and has achieved a lot of application results in the medical field. At present, artificial intelligence technology has covered various stages of colorectal cancer, including screening, individualized assessment, auxiliary diagnosis and treatment decision-making, refined surgery and prognosis judgment, providing help for the accurate and individualized treatment of rectal cancer. However, the lack of standardized, systematic, and scalable AI models remains a major pain point for the field. Therefore, it is necessary to carry out large-scale prospective clinical studies on artificial intelligence model to further confirm its application value in the clinical diagnosis and treatment of rectal cancer.

With the rapid increase in the prevalence rate of nonalcoholic fatty liver disease (NAFLD), new treatment methods are needed to prevent disease progression to liver fibrosis, liver cirrhosis, and liver cancer. Although great efforts have been made to clarify the pathological mechanisms of NAFLD disease progression, there are still no effective treatment methods at present. Bile acids (BAs) regulate systemic metabolism by activating nuclear receptors and G protein-coupled receptors and have been identified as important signaling molecules involved in lipid, glucose, and energy metabolism. Dysregulation of BA homeostasis is associated with the severity of NAFLD. This article summarizes the important ligands in BA metabolism and their role in the progression of NAFLD, in order to provide a basis for the treatment of NAFLD by targeting BA messengers.

Objective:To investigate the clinical value of magnetic resonance imaging (MRI) based integrated deep learning model for predicting the times of linear staplers used in double stapling technique for middle-low rectal cancer resection.Methods:The retrospective cohort study was conducted. The clinicopathological data of 263 patients who underwent low anterior resection (LAR) for middle-low rectal cancer in Ruijin Hospital of Shanghai Jiaotong University School of Medicine from January 2018 to December 2022 were collected as training dataset. There were 183 males and 80 females, aged 63(55,68)years. The clinicopathological data of 128 patients with middle-low rectal cancer were collected as validation dataset, including 83 males and 45 females, with age as 65(57,70)years. The training dataset was used to construct the prediction model, and the validation dataset was used to validate the prediction model. Observation indicators: (1) clinicopathological features of patients in the training dataset; (2) influencing factors for ≥3 times using of linear staplers in the operation; (3) prediction model construction; (4) efficiency evaluation of prediction model; (5) validation of prediction model. Measurement data with skewed distribution were represented as M( Q1, Q3), and Mann-Whitney U test was used for comparison between groups. Count data were expressed as absolute numbers, and comparison between groups was conducted using the chi-square test. Wilcoxon rank sum test was used for non-parametric data analysis. Univariate analysis was conducted using the Logistic regression model, and multivariate analysis was conducted using the Logistic stepwise regression model. The receiver operating characteristic (ROC) curve was draw and the area under the curve (AUC) was calculated. The AUC of the ROC curve >0.75 indicated the prediction model as acceptable. Comparison of AUC was conducted using the Delong test. Results:(1) Clinicopathological features of patients in the training dataset. Of the 263 patients, there were 48 cases with linear staplers used in the operation ≥3 times and 215 cases with linear staplers used in the operation ≤2 times. Cases with preoperative serum carcinoembryonic antigen (CEA) >5 μg/L, cases with anastomotic leakage, cases with tumor diameter ≥5 cm were 20, 12, 13 in the 48 cases with linear staplers used ≥3 times in the operation, versus 56, 26, 21 in the 215 cases with linear staplers used ≤2 times in the operation, showing significant differences in the above indicators between them ( χ2=4.66, 5.29, 10.45, P<0.05). (2) Influencing factors for ≥3 times using of linear staplers in the operation. Results of multivariate analysis showed that preoperative serum CEA >5 μg/L and tumor diameter ≥5 cm were independent risk factors for ≥3 times using of linear staplers in the operation ( odds ratio=2.26, 3.39, 95% confidence interval as 1.15-4.43, 1.50-7.65, P<0.05). (3) Prediction model construction. According to the results of multivariate analysis, the clinical prediction model was established as Logit(P)=-2.018+0.814×preoperative serum CEA (>5 μg/L as 1, ≤5 μg/L as 0)+ 1.222×tumor diameter (≥5 cm as 1, <5 cm as 0). The image data segmented by the Mask region convolutional neural network (MASK R-CNN) was input into the three-dimensional convolutional neural network (C3D), and the image prediction model was constructed by training. The image data segmented by the MASK R-CNN and the clinical independent risk factors were input into the C3D, and the integrated prediction model was constructed by training. (4) Efficiency evaluation of prediction model. The sensitivity, specificity and accuracy of the clinical prediction model was 70.0%, 81.0% and 79.4%, respectively, with the Yoden index as 0.51. The sensitivity, specificity and accuracy of the image prediction model was 50.0%, 98.3% and 91.2%, respectively, with the Yoden index as 0.48. The sensitivity, specificity and accuracy of the integrated prediction model was 70.0%, 98.3% and 94.1%, respectively, with the Yoden index as 0.68. The AUC of clinical prediction model, image prediction model and integrated prediction model was 0.72(95% confidence interval as 0.61-0.83), 0.81(95% confidence interval as 0.71-0.91) and 0.88(95% confidence interval as 0.81-0.95), respectively. There were significant differences in the efficacy between the integrated prediction model and the image prediction model or the clinical prediction model ( Z=2.98, 2.48, P<0.05). (5) Validation of prediction model. The three prediction models were externally validated by validation dataset. The sensitivity, specificity and accuracy of the clinical prediction model was 62.5%, 66.1% and 65.6%, respectively, with the Yoden index as 0.29. The sensitivity, specificity and accuracy of the image prediction model was 58.8%, 95.5% and 92.1%, respectively, with the Yoden index as 0.64. The sensitivity, specificity and accuracy of the integrated prediction model was 68.8%, 97.3% and 93.8%, respectively, with the Yoden index as 0.66. The AUC of clinical prediction model, image prediction model and integrated prediction model was 0.65(95% confidence interval as 0.55-0.75), 0.75(95% confidence interval as 0.66-0.84) and 0.84(95% confidence interval as 0.74-0.93), respec-tively. There was significant differences in the efficacy between the clinical prediction model and the integrated prediction model ( Z=3.24, P<0.05). Conclusion:The MRI-based deep-learning model can help predicting the high-risk population with ≥3 times using of linear staplers in resection of middle-low rectal cancer with double stapling technique.

Objective:To investigate the clinical characteristics and risk factors for the progression of acute-on-chronic liver failure(ACLF)associated with hepatitis B in elderly patients.Methods:A total of 168 elderly patients with hepatitis B-related acute-on-chronic liver failure(HBV-ACLF)at Tianjin Third Central Hospital who met the diagnostic criteria of the Asian Pacific Association for the Study of the Liver(APASL)-ACLF were enrolled, 176 non-elderly HBV-ACLF patients served as the control group during the same period, and their baseline and progression data were recorded.At the same time, the elderly group was divided into the progressive subgroup and the non-progressive subgroup based on the diagnostic criteria of the European Society for the Study of the Liver(EASL)-ACLF, and their baseline and progression data were recorded.Independent risk factors for HBV-ACLF progression in the elderly were analyzed using multivariate Cox proportional risk model regression.Results:Compared with non-elderly patients with HBV-ACLF, elderly patients were more likely to progress to meet the EASL-ACLF diagnostic criteria and have higher mortality.Multivariate Cox proportional risk model regression analysis showed that baseline arterial lactic acid levels( HR=1.77, 95% CI: 1.36-2.30, P<0.01), secondary nosocomial infections( HR=13.90, 95% CI: 3.73-51.87, P<0.01), rates of change in maximum total bilirubin( HR=1.08, 95% CI: 1.01-1.15, P=0.04), rates of change in maximum MELD( HR=4.06, 95% CI: 1.53-10.77, P=0.01)and rates of change in maximum CLIF-SOFA( HR=12.74, 95% CI: 2.46-66.08, P<0.01)were independent risk factors for progression of HBV-ACLF in elderly patients. Conclusions:Compared with non-elderly patients, elderly patients with HBV-ACLF have more advanced disease and higher mortality.Therefore, risk factors should be identified as soon as possible and treatment plans should be formulated as soon as possible to further reduce the mortality.

Objective:To analyze the predictive ability of model for end-stage liver disease (MELD)-sarcopenia score in short-term prognosis of patients with acute-on-chronic liver failure (ACLF).Methods:Two hundred and seventy-one patients with ACLF hospitalized in Tianjin Third Central Hospital from January 2013 to December 2019 were selected, among whom 157 cases with sarcopenia and 114 cases without sarcopenia.According to ACLF classification, the patients were divided into group A (no cirrhosis basis) of 61 cases, group B (compensated cirrhosis basis) of 99 cases, and group C (previous history of uncompensated cirrhosis) of 111 cases.The basic data, laboratory examination results, computed tomography (CT) examination results and prognosis of the patients were retrospectively collected, and the MELD score, MELD-Na score and MELD-sarcopenia score were calculated. Multivariate logistic regression, multivariate Cox proportional hazards regression, Kaplan-Meier method, log-rank method and area under receiver operating characteristic curve were used for statistical analysis.Results:Low body mass index (odds ratio ( OR)=0.93, P<0.001), complicated cirrhosis ( OR=1.14, P=0.004), complicated hepatic encephalopathy ( OR=1.31, P<0.001), high white blood cell level ( OR=1.18, P=0.009) and high platelet level ( OR=1.08, P<0.001) were independent risk factors for sarcopenia in patients with ACLF. High MELD score (hazard ratio ( HR)=1.02, P=0.001), high MELD-Na score ( HR=1.07, P=0.038), high MELD-sarcopenia score ( HR=1.14, P<0.001), high total bilirubin ( HR=1.00, P<0.001) and high international normalized ratio (INR) ( HR=1.71, P<0.001) were independent risk factors for death in patients with ACLF. In subgroup analysis, the cumulative survival rate of sarcopenia patients in group A and B was lower than that of non-sarcopenia patients ( χ2=5.97 and 8.34, respectively, P=0.015 and 0.004, respectively), while there was no significant difference in the cumulative survival rate between sarcopenia patients and non-sarcopenia patients in group C ( χ2=4.90, P=0.053). In groups A and B, the area under the curve (AUC) of MELD-sarcopenia score in predicting short-term prognosis was 0.87, which was higher than MELD score (0.78) and MELD-Na score (0.78), and the differences were both statistically significant ( Z=2.86 and 2.56, respectively, P=0.004 and 0.011, respectively). The AUC of MELD-Na score in predicting short-term prognosis in group C (0.83) was higher than that of MELD score (0.71) and MELD-sarcopenia score (0.69), and the differences were both statistically significant ( Z=2.52 and 2.64, respectively, P=0.012 and 0.008, respectively). Conclusions:Patients with ACLF with no cirrhosis basis or compensated cirrhosis basis complicated with sarcopenia have shorter survival time and worse prognosis than those without sarcopenia. For patients with ACLF with no cirrhosis basis or compensated cirrhosis basis, MELD-sarcopenia score has better predictive value for the short-term prognosis.

Recently,the relationship between intestinal flora and its metabolites and tumorigenesis,inflammatory bowel diseasesas well as radiation-induced intestinal injury has captivated widespread attention from researchers.Accumulated evidence derived from nuclear accident investigation,animal model experiment and clinical research has proven the role of intestinal flora and its metabolites as the biomarkers to evaluate the radiation dose and severity of radiation-induced intestinal injury.This article reviews the relationship between intestinal flora and its metabolites and radiation-induced intestinal injury,aiming to provide theoretical reference for assessing the risk of radiation-induced intestinal injury.

Hepatopulmonary syndrome (HPS) is a common pulmonary complication in patients with liver disease and / or portal hypertension, and is characterized by abnormal arterial oxygenation caused by intrapulmonary vascular dilatation. The pathogenesis of HPS is complex, with a low clinical early diagnosis rate and poor prognosis. HPS currently lacks effective therapeutic drugs; therefore, liver transplantation is the only fundamental treatment. This article summarizes the pathogenesis, clinical manifestations, diagnosis and treatment of HPS in order to further improve the level of clinical screening and diagnosis and treatment of HPS.