Objective:To investigate the factors affecting the efficacy of 131I treatment for Graves′ disease (GD) and to construct a predictive model for the treatment outcomes of 131I therapy. Methods:Retrospective analysis of the treatment efficacy was performed on 2 190 patients (547 males, 1 643 females, age (42.9±12.4) years) with GD, who received initial 131I treatment in Tianjin Medical University General Hospital between October 2013 and May 2018. Univariate analysis ( χ2 test, et al) and logistic regression were performed to analyze the possible factors affecting the efficacy of 131I treatment. An efficacy prediction model for 131I treatment of GD was constructed, and decision curve analysis (DCA) was used to evaluate the clinical utility of the prediction model. Results:The overall effectiveness rate of 131I treatment for GD patients was 99.95%(2 189/2 190), with a total cure rate of 83.74%(1 834/2 190), among which 94.11%(1 726/1 834) were cured after a single treatment. Pre-treatment thyroid mass was identified as an independent risk factor affecting the efficacy of initial 131I treatment (odds ratio ( OR)=0.983(95% CI: 0.977-0.989), P<0.001). The clinical cure rate was higher in patients who received an adequate dose of 131I compared with that in patients who didn′t receive an adequate dose (79.97%(1 537/1 922) vs 70.52%(189/268); χ2=12.57, P<0.001), but it did not increase the incidence of hypothyroidism within one year. A predictive model was constructed, and it was found that thyroid mass and disease duration had a relatively high impact on the clinical cure rate. The concordance index (C-index) of the predictive model was 0.623(95% CI: 0.593-0.654). DCA indicated that the predictive model offered substantial net benefits across a wide range of probability thresholds. Conclusions:131I treatment is effective in most patients with GD. The predictive model for efficacy of initial 131I treatment developed in this study can assist in evaluating treatment outcomes and help clinicians select the most suitable 131I treatment dose, enhancing clinical decision-making.

Chronic hepatitis B（CHB）is one of the major challenges in the global public health field. As of 2022，approximately 254 million people worldwide were infected with the hepatitis B virus（HBV）. CHB is one of the main causes of liver cirrhosis and hepatocellular carcinoma（HCC）. Nucleos（t）ide analogs（NAs）and interferon therapy can delay the progression of liver fibrosis by inhibiting viral replication，but they cannot completely avoid the problem of heterogeneous treatment responses. Some patients are in a state of low-level viremia（LLV）during treatment. The persistent LLV state can induce chronic inflammation and the progression of liver fibrosis，ultimately increase the risk of HCC. In patients with poor treatment responses，the continuous active viral replication can induce immune disorders，accelerate the evolution of fibrosis to the decompensated stage of liver cirrhosis，and increase the risk of patient death. This article aims to review the definition，mechanisms，and impact on treatment outcomes of LLV and suboptimal response based on the latest research，provide a basis for optimizing antiviral therapy for CHB.

BACKGROUND:In recent years,artificial intelligence has been increasingly integrated into the diagnosis and treatment of lumbar disc herniation,enhancing the accuracy and efficiency of diagnostic procedures and diversifying therapeutic approaches.This integration has positioned artificial intelligence as a burgeoning focal point within the field.OBJECTIVE:To provide a comprehensive overview of the current applications of artificial intelligence in the diagnosis and treatment of lumbar disc herniation,to analyze the limitations of the relevant technologies.METHODS:A systematic computer-assisted literature search was performed in PubMed,CNKI,WanFang Database,and VIP Database for relevant publications regarding the application of artificial intelligence in the diagnosis and treatment of lumbar disc herniation from database inception up to August 2024.The search keywords included"lumbar disc herniation,artificial intelligence,machine learning,deep learning,big data,robot,neural network,model,algorithm."A total of 101 articles were selected based on predefined inclusion criteria and were reviewed.RESULTS AND CONCLUSION:Different artificial intelligence technologies have propelled the intelligent treatment of lumbar disc herniation,showing great potential for future development.Deep learning technology,based on advanced algorithms,constructs corresponding learning models to optimize the processing of X-ray,CT,and MRI images,achieving precise localization,identification,and analysis of degenerated intervertebral discs,and improving the accuracy of automated diagnosis.Big data technology utilizes data platforms to analyze vast medical data,simulate disease development trends,and provide a new perspective for disease assessment and prediction.Surgical robots,combined with robotic arms,3D high-definition vision systems,and 5G communication technology,support remote precise surgical operations,demonstrating significant technological advantages.In the future,the diagnosis and treatment of lumbar disc herniation by artificial intelligence will evolve towards standardization,efficiency,and precision through continuous optimization of algorithms and the professional development of data platforms.

Objective:This study aims to analyze the economic impact of postoperative complications after colorectal cancer surgery.Methods:A retrospective cohort study was conducted. Patients with a preoperative pathological diagnosis of colorectal cancer who met surgical indications and underwent surgical treatment were included, while those with incomplete hospitalization cost data were excluded. From March 2017 to March 2022, three hundred and ninety-two colorectal cancer patients treated at Peking University Cancer Hospital and Institute Gastrointestinal Cancer Center I, were enrolled. Descriptive statistics were performed on the incidence of complications, hospitalization costs, and postoperative length of stay. A cohort was established based on the presence of postoperative complications (POC) and absence of postoperative complications (non-POC) to study economic differences. Propensity score matching analysis was employed to reduce potential confounding factors.Results:Among 392 colorectal cancer patients, 90 (23.0%) developed POC (POC group), while 302 were in the non-POC group. Significant statistical differences were found between the two groups in terms of operation duration, extent of resection, and stoma creation (all P < 0.05); other baseline indicators showed no significant differences (all P>0.05). The median hospitalization cost for patients with postoperative anastomotic leakage was 115 973 yuan, an increase of 38 941 yuan (50.5%) over the non-POC group's 77 059 yuan; the median hospitalization cost for patients with mechanical obstruction was 111 477 yuan, an increase of 34 418 yuan (44.7%) over the non-POC group; and the median hospitalization cost for patients with wound infection was 95 860 yuan, an increase of 18 801 yuan (24.4%) over the non-POC group. The median postoperative length of stay for patients with anastomotic leakage, mechanical obstruction, and wound infection was 22.0 days, 22.0 days, and 18.5 days, respectively, compared to 9.0 days in the non-POC group, extending by 13.0 days, 13.0 days, and 9.5 days. After propensity score matching, each group had 68 patients, and there were no statistically significant differences in preoperative and intraoperative observations between the two groups (all P>0.05); compared to the non-POC group, the hospitali- zation costs in the POC group significantly increased (89 165 yuan vs. 75 437 yuan, P<0.001), and the postoperative length of stay also significantly extended (14.0 days vs. 8.0 days, P<0.001). Conclusions:The occurrence of POC after colorectal cancer surgery significantly increases hospitalization costs and length of stay. This study provides specific and accurate reference data for subsequent health economic decision-making. This is the first detailed economic impact analysis of postoperative complications of colorectal cancer with a large sample size, which includes an economic impact analysis for each POC and subgroup.

ObjectiveTo observe the clinical efficacy and possible mechanisms of Xianglian Huazhuo Granules （香连化浊颗粒， XHG） in the treatment of chronic atrophic gastritis with intestinal metaplasia. MethodsA total of 140 patients with chronic atrophic gastritis and intestinal metaplasia were randomly divided into a treatment group and a control group， with 70 cases in each group. The treatment group received 12.5 g of XHG orally， twice daily. The control group received 12.5 g of placebo orally， twice daily. Both groups were treated for 6 months. The traditional Chinese medicine （TCM） symptom scores， pathological types， serum tumor markers of the digestive system， and serum bile acids （TBA）， interleukin-23 （IL-23）， and Dickkopf-related protein 1 （DKK-1） levels were observed before and after treatment. Safety indicators and adverse events were recorded. After treatment， TCM syndrome efficacy and pathological types were evaluated， and patients were followed up for 18 months with gastric endoscopy and pathological results， which were compared with the results after treatment finished. ResultsTwo patients dropped out in the control group， and a total of 168 cases were included in the final analysis， 70 in the treatment group and 68 in the control group. The treatment group showed a significant reduction in TCM symptom scores， serum TBA， IL-23， and DKK-1 levels， and a significant increase in alpha-fetoprotein （AFP）， carbohydrate antigen 125 （CA125）， carbohydrate antigen 199 （CA199） levels； in the control group， carcinoembryonic antigen （CEA）， CA125， CA199 levels significantly increased （P＜0.05 or P＜0.01）； and carbohydrate antigen 242 （CA242） level in both the treatment group and the control group decreased significantly （P＜0.01）. The treatment group had lower TCM symptom scores and lower levels of serum TBA， IL-23， and DKK-1 compared to the control group （P＜0.05）. The effective rate for TCM syndrome efficacy in the treatment group was 80.00% （56/70）， significantly higher than the 20.59% （14/68） in the control group （P < 0.05）. The effective rate for pathological classification in the treatment group was 72.73% （8/11） for mixed intestinal metaplasia， significantly better than 46.15% （6/13） in the control group （P＜0.05）. No adverse events were reported in either group. Among 40 patients who had a follow-up endoscopy after one year， 21 were from the treatment group， of whom 11 showed reduced intestinal metaplasia， 9 showed no significant changes， and 1 had worsened； while 19 patients in the control group had 4 with reduced intestinal metaplasia， 13 with no significant changes， and 2 with worsened conditions. No cancer was detected in either group. The treatment group showed significantly better improvement in intestinal metaplasia on follow-up gastric endoscopy pathology than the control group （P＜0.05）. ConclusionXHG can significantly improve the clinical symptoms in patients with chronic atrophic gastritis and intestinal metaplasia and reduce the degree of mixed intestinal metaplasia. The mechanism may involve lowering serum TBA， DKK-1， and IL-23 levles， thus delaying the progression from inflammation to cancer.

Objective:To evaluate the transjejunal choledochoscopy via a marked and fixed jejunal loop in the management of anastomotic complications after choledochojejunostomy.Methods:Clinical data of 17 patients undergoing choledochoscopy exploration and treatment via a subcutaneous jejunal loop at Beijing Luhe Hospital, Capital Medical University from January 2014 to August 2023 were retrospectively analyzed, including 5 males and 12 females, with a median age of 57 years (24-69 years). All patients had a jejunal loop marked and fixed subcutaneous during previouse Roux-en-Y choledochojejunostomy or biliary anastomotic reconstruction surgery. Choledochoscopy was performed via the pre-fixed loop to manage biliary strictures and stones. Baseline and perioperative date including gender, age, primary disease, jejunal loop puncture and catheterization, choledochoscopy exploration and operative time and frequency, and postoperative complications (bleeding, bile leakage, infection, etc.) were recorded.Results:The primary diseases of the 17 patients included nine cases of congenital choledochal cysts, three cases of common bile duct stones, three cases of common bile duct strictures, one case of intrahepatic and extrahepatic bile duct stones, and one case of gallbladder cancer. Eight patients had their jejunal loop drainage tubes removed or only had subcutaneous marking and fixation without T-tube placement. A total of 12 attempts were made to puncture and catheterize via the jejunal loop under X-ray guidance, with 11 successful punctures. Choledochoscopy exploration or operation was performed 67 times via the jejunal loop in 17 patients, all successfully reaching the biliary-enteric anastomosis. Intrahepatic cholangiolithiasis were found during 28 choledochoscopy explorations via the jejunal loop, with stones directly removed using a stone basket in 25 cases and electrohydraulic lithotripsy in three cases. Biliary-enteric anastomotic or intrahepatic bile duct strictures were found in 34 choledochoscopy explorations via the jejunal loop, with 22 cases undergoing balloon dilation. Among them, 14 cases had biliary strictures combined with stones, and the stones were removed after balloon dilation of the strictures. Other choledochoscopy explorations showed no abnormalities. The operative time of 67 choledochoscopy explorations via the jejunal loop was (67±42) min, with no intraoperative complications. Two cases had postoperative drainage tube displacement and dislocation, which were repositioned through the sinus tract immediately, and no complications such as bleeding, bile leakage, or infection occurred.Conclusion:Pre-fixation of the jejunal loop during choledochojejunostomy offers a minimally invasive access for postoperative choledochoscopy that could directly reach the biliary-enteric anastomosis, and effectively manage the long-term biliary-enteric anastomotic complications, including biliary strictures and cholangiolithiasis.

Objective:To investigate the factors affecting the efficacy of 131I treatment for Graves′ disease (GD) and to construct a predictive model for the treatment outcomes of 131I therapy. Methods:Retrospective analysis of the treatment efficacy was performed on 2 190 patients (547 males, 1 643 females, age (42.9±12.4) years) with GD, who received initial 131I treatment in Tianjin Medical University General Hospital between October 2013 and May 2018. Univariate analysis ( χ2 test, et al) and logistic regression were performed to analyze the possible factors affecting the efficacy of 131I treatment. An efficacy prediction model for 131I treatment of GD was constructed, and decision curve analysis (DCA) was used to evaluate the clinical utility of the prediction model. Results:The overall effectiveness rate of 131I treatment for GD patients was 99.95%(2 189/2 190), with a total cure rate of 83.74%(1 834/2 190), among which 94.11%(1 726/1 834) were cured after a single treatment. Pre-treatment thyroid mass was identified as an independent risk factor affecting the efficacy of initial 131I treatment (odds ratio ( OR)=0.983(95% CI: 0.977-0.989), P<0.001). The clinical cure rate was higher in patients who received an adequate dose of 131I compared with that in patients who didn′t receive an adequate dose (79.97%(1 537/1 922) vs 70.52%(189/268); χ2=12.57, P<0.001), but it did not increase the incidence of hypothyroidism within one year. A predictive model was constructed, and it was found that thyroid mass and disease duration had a relatively high impact on the clinical cure rate. The concordance index (C-index) of the predictive model was 0.623(95% CI: 0.593-0.654). DCA indicated that the predictive model offered substantial net benefits across a wide range of probability thresholds. Conclusions:131I treatment is effective in most patients with GD. The predictive model for efficacy of initial 131I treatment developed in this study can assist in evaluating treatment outcomes and help clinicians select the most suitable 131I treatment dose, enhancing clinical decision-making.

Chronic hepatitis B（CHB）is one of the major challenges in the global public health field. As of 2022，approximately 254 million people worldwide were infected with the hepatitis B virus（HBV）. CHB is one of the main causes of liver cirrhosis and hepatocellular carcinoma（HCC）. Nucleos（t）ide analogs（NAs）and interferon therapy can delay the progression of liver fibrosis by inhibiting viral replication，but they cannot completely avoid the problem of heterogeneous treatment responses. Some patients are in a state of low-level viremia（LLV）during treatment. The persistent LLV state can induce chronic inflammation and the progression of liver fibrosis，ultimately increase the risk of HCC. In patients with poor treatment responses，the continuous active viral replication can induce immune disorders，accelerate the evolution of fibrosis to the decompensated stage of liver cirrhosis，and increase the risk of patient death. This article aims to review the definition，mechanisms，and impact on treatment outcomes of LLV and suboptimal response based on the latest research，provide a basis for optimizing antiviral therapy for CHB.

Objective To develop a machine learning(ML)-based prediction model for assessing the therapeutic effects of resveratrol(RES)on the pathological damage of acute pancreatitis(AP),and to optimize RES administration strategies for AP through validation using an animal model.Methods AAn ML-based prediction model was constructed using published data.Interpretability analysis was applied to identify high-efficacy zones within the parameter space of administration dose and frequency,which was followed by rigorous screening to select the optimal dosing strategy that balanced therapeutic efficacy and experimental feasibility.A total of 32 C57BL/6 mice were randomly assigned to 4 groups(n=8 per group),including a control group(Ctrl),an AP model group induced by caerulein(CER)and referred to as CER-AP,a treatment group receiving RES via intraperitoneal injection(RES i.p.),and a treatment group receiving RES via intragastric gavage(RES i.g.).The Ctrl group received intraperitoneal injection of normal saline.The CER-AP and the treatment groups were induced with 10 intraperitoneal injections of CER at 50 μg/kg.RES was administered to the RES i.p.and RES i.g.groups according to the optimal dose and timing predicted by the ML model.Blood and tissue samples were collected 12 hours after the experiment started.Results The gradient boosting decision tree model,optimized via Hyperopt,yielded the best performance,predicting that the optimal dose and administration frequency were 19.992 mg/kg and 3.828 times,respectively.Accordingly,a regimen of 20 mg/kg RES,administered four times,was used in the animal experiments.Compared with the Ctrl group,the CER-AP group exhibited higher pancreatic pathology scores and elevated levels of serum amylase,lipase,pancreatic myeloperoxidase,and trypsin,with all differences reaching statistical significance(all P<0.05).The administration of 20 mg/kg RES via both intraperitoneal injection and intragastric gavage mitigated pancreatic inflammatory cell infiltration and necrosis,improved the overall pathology score,and reduced serum amylase,lipase,and pancreatic myeloperoxidase levels to varying degrees(all P<0.05).Conclusion A regimen of 20 mg/kg RES administered four times effectively alleviates the severity of CER-induced AP.The therapeutic benefits appear to arise from a multi-target regulatory network that simultaneously suppresses inflammatory cascades,mitigates oxidative stress,and reduces apoptosis,thereby reducing pancreatic tissue damage and systemic inflammatory responses.

The in-depth research of artificial intelligence in the medical field has greatly improved the workflow and diagnostic ability of diagnostic radiology.This article focuses on artificial intelligence technology in the field of interventional medicine,and enumerates its potential application scenarios,including improving image analysis capabilities to assist diagnosis and predict treatment response.It also describes the challenges that need to be overcome for practical application.Finally,with the continuous development of artificial intelligence in interventional medicine,artificial intelligence will further optimize the channels of interventional medicine and bring revolutionary changes to the clinical practice of interventional medicine.