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.

ObjectiveTo investigate the effects of jatrorrhizine on endogenous metabolites and metabolic pathways in the mouse model of ulcerative colitis. MethodsThirty male C57BL/6J mice were randomly divided into the normal group， the model group， the low-dose and high-dose jatrorrhizine groups （0.04， 0.16 g·kg^-1）， and the mesalazine group （0.52 g·kg^-1）The mouse model of ulcerative colitis was established with 3% dextran sulfate sodium （DSS） and treated with different doses of jatrorrhizine by gavage. The changes in body weight， colon length， disease activity index （DAI）， and colonic histopathology were analyzed to evaluate the therapeutic effects of jatrorrhizine. UPLC-Q-TOF/MS was employed to determine the serum and fecal levels of metabolites in mice. Metabolomics methods were used to screen the differential metabolites， on the basis of which the potential therapeutic mechanism of jatrorrhizine on DSS-induced ulcerative colitis in mice was investigated. ResultsAfter intervention with jatrorrhizine， the model mice showed significantly decreased DAI（P<0.05，P<0.01）， recovered colon length，（P<0.05，P<0.01） and alleviated histopathology of the colon. The metabolomics study screened out 13 differential metabolites in the serum and 8 differential metabolites in the feces. The pathway enrichment analysis predicted three potential metabolic pathways： Biosynthesis of unsaturated fatty acids， phenylalanine， tyrosine and tryptophan biosynthesis， and phenylalanine metabolism. ConclusionJatrorrhizine may treat ulcerative colitis by regulating the biosynthesis and metabolism of amino acids and the synthesis of unsaturated fatty acids.

Objective:To improve the risk management process for clinical trial projects using healthcare failure mode and effect analysis(HFMEA), for references for enhancing the risk identification and preventing capabilities of drug clinical trial institutions.Methods:From June to December 2022, this study focused on the project management process of a clinical trial centre in a tertiary public hospital. Following HFMEA procedures, a research team was established. Core processes prone to failure modes in the drug clinical trial project management and their potential failure modes were identified through group discussions, literature analysis, the Delphi method, and decision tree analysis. High-risk failure modes were screened via risk assessment, corresponding improvement measures were formulated and performed, and their effectiveness was validated.Results:The study identified 6 main processes, 17 sub-processes, and 102 potential failure modes. Delphi analysis confirmed 88 failure modes, with 19 having a failure risk priority number(RPN)≥8.00. Decision tree analysis identified 16 high-risk failure modes, involving 5 main processes and 10 sub-processes. Targeted improvements, such as adopting standardized hospital contract templates and setting deadlines for final payment settlement, etc., were implemented. One year post-implementation(January 2024), the RPN for all 16 high-risk failure modes were<8.00.Conclusions:HFMEA could help hospital clinical trial institutions comprehensively and systematically identify high-risk failure modes in the project management process, develop targeted improvement measures, and improve the level of drug clinical trial project management.

Objective: This study investigates the potential of radiomics to predict postoperative progression of ossification of the posterior longitudinal ligament (OPLL) after posterior cervical spine surgery. Methods: This retrospective study included 473 patients diagnosed with OPLL at Peking University Third Hospital between October 2006 and September 2022. Patients underwent posterior spinal surgery and had at least 2 computed tomography (CT) examinations spaced at least 1 year apart. OPLL progression was defined as an annual growth rate exceeding 7.5%. Radiomic features were extracted from preoperative CT images of the OPLL lesions, followed by feature selection using correlation coefficient analysis and least absolute shrinkage and selection operator, and dimensionality reduction using principal component analysis. Univariable analysis identified significant clinical variables for constructing the clinical model. Logistic regression models, including the Rad-score model, clinical model, and combined model, were developed to predict OPLL progression. Results: Of the 473 patients, 191 (40.4%) experienced OPLL progression. On the testing set, the combined model, which incorporated the Rad-score and clinical variables (area under the receiver operating characteristic curve [AUC] = 0.751), outperformed both the radiomics-only model (AUC = 0.693) and the clinical model (AUC = 0.620). Calibration curves demonstrated good agreement between predicted probabilities and observed outcomes, and decision curve analysis confirmed the clinical utility of the combined model. SHAP (SHapley Additive exPlanations) analysis indicated that the Rad-score and age were key contributors to the model’s predictions, enhancing clinical interpretability. Conclusion Radiomics, combined with clinical variables, provides a valuable predictive tool for assessing the risk of postoperative progression in cervical OPLL, supporting more personalized treatment strategies. Prospective, multicenter validation is needed to confirm the utility of the model in broader clinical settings.

Objective:To explore the impact of the two-way referral model on compliance and prognosis in patients with heart failure.Methods:This bidirectional cohort study enrolled chronic heart failure (CHF) patients treated at Qilu Hospital of Shandong University or designated primary hospitals between March 2018 and March 2022. Patients were categorized into two groups based on referral status: two-way referral group (participating in the referral model with≥1 follow-up visit at primary hospitals) and the core hospital group (receiving treatment and follow-up exclusively at Qilu Hospital). Baseline clinical characteristics were collected and compared between groups. Patients underwent followed-up, with primary endpoints including follow-up rate, drug (β-blockers, angiotension converting enzyme inhibitor (ACEI)/angiotensin Ⅱ receptor blockers (ARB)/angiotensin receptor-neprilysin inhibitor (ARNI), sodium-glucose cotransporter 2 inhibitors and mineralocorticoid receptor antagonists) utilization rate and target dose achievement rate. Secondary endpoints encompassed changes from baseline in left ventricular ejection fraction (LVEF), left ventricular end-diastolic diameter (LVEDd), and N-terminal pro-brain natriuretic peptide (NT-proBNP), plus cardiovascular mortality and heart failure rehospitalization. Generalized linear mixed models analyzed longitudinal trends in LVEF, LVEDd, and NT-proBNP levels. Kaplan-Meier curves and Cox regression evaluated LVEF recovery rates, supplemented by subgroup analyses. Multivariate logistic regression was used to identify factors influencing target dose achievement rate for β-blockers and ACEI/ARB/ARNI therapies in CHF patients.Results:A total of 357 patients were enrolled, aged 53 (41, 63) years, including 256 males (71.7%). 157 patients were in the two-way referral group and 200 patients in the core hospital-treated group. Compared with the core hospital-treated group, the two-way referral group had lower baseline LVEF (28 (22, 34)% vs. 31 (23, 36)%, P=0.021) and systolic blood pressure (116 (104, 125) mmHg vs. 121 (109, 134) mmHg (1 mmHg=0.133 kPa), P=0.010). The 12-month follow-up rate of the two-way referral group was higher than the core hospital-treated group (73.8% vs. 56.0%, P=0.004). No significant between-group differences were observed in drug utilization rate of β-blockers, ACEI/ARB/ARNI, or sodium-glucose cotransporter 2 inhibitors during follow-up (all P>0.05), while mineralocorticoid receptor antagonists use showed a declining trend in both groups. Although the core hospital-treated group had higher target dose achievement rates for β-blockers (65.4% vs. 49.3%, P=0.042) and ACEI/ARB/ARNI (79.8% vs. 65.8%, P=0.046) than the two-way referral group, multivariate logistic regression indicated that the two-way referral model was not a negative predictor for these outcomes (all P>0.05). Both groups showed improved NT-proBNP, LVEDd, and LVEF from baseline (all P<0.001) with no significant difference in trends between groups (all P>0.05). There was no significant difference in the composite incidence (7.6% vs. 6.5%, P=0.674) and cumulative incidence (log-rank P=0.684) of cardiovascular death and heart failure rehospitalization at 12 months between two groups. Conclusion:The two-way referral model demonstrates advantages in improving medication adherence, drug utilization rates, and targetdoseachievement rates among CHF patients. This model not only promotes cardiac functional recovery but also reduces risks of cardiovascular mortality and heart failure rehospitalization, achieving comparable therapeutic and management outcomes to those observed in core hospital-treated patients.

Objective:To analyze the abnormal results of health surveillance for workers exposed to dimethylformamide (DMF), and to provide reference for the formulation of relevant policies and standards.Methods:In April 2024, 11, 224 workers who participated in occupational health examinations in Jiangsu Province in 2023 were selected as the research subjects. Among them, 5, 615 people exposed to DMF were taken as the exposure group, and 5, 609 people not exposed to hepatotoxic chemicals were taken as the control group. By inquiring about related symptoms and combining with the occupational health examination of workers, the abnormal rates of blood pressure, blood routine, urine routine, electrocardiogram, liver function and B-ultrasonography of the survey subjects were statistically analyzed.Results:Compared with the control group, the abnormal detection rates of indicators such as blood pressure, blood routine, urine routine, and electrocardiogram were higher, and the difference was statistically significant P<0.05). The abnormal rates of various liver function indicators in the DMF exposure group were all higher than those in the control group. Among them, the abnormal rates of alanine aminotransferase, total protein, total bilirubin, and liver B-ultrasound in the exposure group were all higher than those in the control group, and the differences were statistically significant ( χ2=34.88, 42.49, 43.07, 55.28, P<0.001). Compared with the control group, the detected values of alanine aminotransferase, total protein and total bilirubin in the DMF exposure group were higher, and the difference was statistically significant ( F=5.367, 29.543, 37.766, P<0.05). In the DMF exposure group, the abnormal rates of liver function, electrocardiogram and liver B-ultrasound in men were much higher than those in women, and the differences were statistically significant ( χ2=185.05, 10.06, 141.94, P<0.001, P=0.002, P<0.001). Grouped by length of service, the abnormal detection rate of liver function was similar in each length of service segment, and the difference was not statistically significant ( χ2=0.34, P>0.05). The abnormal detection rate of electrocardiogram increased with the increase of length of service, among which it was the highest in the 10-20 years of service, and the difference was statistically significant ( χ2=7.26, P=0.026). The abnormal rate detected by liver B-ultrasound significantly increased with the increase of working years, and was the highest in the section of ≥20 years of working years. The difference was statistically significant ( χ2=44.15, P<0.001) . Conclusion:Occupational exposure to DMF can affect the health of workers, especially increasing the detection rate of abnormal liver function and liver B-ultrasound. It is very important to strengthen the occupational health monitoring of personnel exposed to DMF, improve the working environment, and pay attention to the chronic liver damage caused by DMF occupational exposure to workers.

Objective:To prepare 89Zr-labeled anti-human podoplanin (PDPN) monoclonal antibody SZ168 and evaluate its feasibility for melanoma immunoPET imaging. Methods:89Zr-desferrioxamine (DFO)-SZ168 was prepared by conjugating p-isothiocyanatobenzyl (SCN-Bn)-DFO with SZ168 and chelating with 89Zr. Quality control analyses were conducted, including labeling rate, radiochemical purity, and in vitro stability. Melanoma mouse models were created, with experimental group ( n=3) and control group ( n=3) receiving tail vein injections of 89Zr-DFO-SZ168 and 89Zr-DFO-immunoglobulin (Ig)G solutions (3.7MBq) respectively. The experimental group underwent microPET/CT imaging at 12, 24, 48 and 72h post-injection, while the control group underwent imaging at 48h post-injection. Tumor and organ radioactivity uptake was analyzed using the ROI method. Mice were sacrificed at 7d post-injection to assess the ex vivo biodistribution of 89Zr-DFO-SZ168 and 89Zr-DFO-IgG. Independent-sample t test was used to analyze the data. Results:The pH value of the 89Zr-DFO-SZ168 solution was approximately 7.0, with a labeling rate >60%, radiochemical purity >95% after PD10 column purification, and good stability after 72h in vitro. Series microPET/CT imagings showed significant tumor visualization in tumor-bearing mice. Radioactivity uptake in tumors peaked at 48h post-injection, while the tumor was not clearly detected by 89Zr-DFO-IgG microPET/CT imaging. Ex vivo biodistribution indicated that 89Zr-DFO-SZ168 mainly accumulated in tumors, liver, and bones, with tumor uptake significantly higher than that of 89Zr-DFO-IgG ((29.36±7.29) percentage activity of injection dose per gram of tissue (%ID/g) vs (8.78±1.63) %ID/g; t=4.77, P=0.009). Immunohistochemistry of tumor specimens showed high expression of PDPN in tumor tissues. Conclusions:The probe 89Zr-DFO-SZ168 is successfully prepared, showing potential for specific molecular imaging diagnosis of melanoma. This lays a basis for developing PDPN molecular target-based immuno-PET diagnosis and integrated diagnosis and treatment for melanoma.

Laparoscopic common bile duct exploration(LCBDE), as a safe and feasible surgical procedure for the treatment of choledocholithiasis, has been widely applied in clinical practice.However, due to the generally declined organ function, comorbidities, and polypharmacy associated with elderly patients, there is currently no unified consensus on the application of LCBDE in elderly patients with choledocholithiasis.This review aims to summarize the current status of LCBDE for the treatment of choledocholithiasis in the elderly patients, in order to provide reference for clinicians to select appropriate treatment strategies.

Objective:To improve the risk management process for clinical trial projects using healthcare failure mode and effect analysis(HFMEA), for references for enhancing the risk identification and preventing capabilities of drug clinical trial institutions.Methods:From June to December 2022, this study focused on the project management process of a clinical trial centre in a tertiary public hospital. Following HFMEA procedures, a research team was established. Core processes prone to failure modes in the drug clinical trial project management and their potential failure modes were identified through group discussions, literature analysis, the Delphi method, and decision tree analysis. High-risk failure modes were screened via risk assessment, corresponding improvement measures were formulated and performed, and their effectiveness was validated.Results:The study identified 6 main processes, 17 sub-processes, and 102 potential failure modes. Delphi analysis confirmed 88 failure modes, with 19 having a failure risk priority number(RPN)≥8.00. Decision tree analysis identified 16 high-risk failure modes, involving 5 main processes and 10 sub-processes. Targeted improvements, such as adopting standardized hospital contract templates and setting deadlines for final payment settlement, etc., were implemented. One year post-implementation(January 2024), the RPN for all 16 high-risk failure modes were<8.00.Conclusions:HFMEA could help hospital clinical trial institutions comprehensively and systematically identify high-risk failure modes in the project management process, develop targeted improvement measures, and improve the level of drug clinical trial project management.