Objective To investigate the expression level and diagnostic value of serum cysteine protease inhibitor S(CST4)in patients with gastric cancer.Methods Clinical data of 115 patients with suspected gastric cancer who complained of gastric discomfort were retrospectively analyzed,and they were divided into benign disease group(n=50),precancerous disease group(n=26)and gas-tric cancer group(n=39).The levels of serum CST4,carcinoembryonic antigen(CEA),carbohy-drate antigen 19-9(CA19-9)and carbohydrate antigen 72-4(CA72-4)were analyzed in the three groups.The positive rates of CST4 among the three groups were compared.Binary Logistic regression analysis was used to screen for independent risk factors for gastric cancer occurrence.The receiver operating characteristic(ROC)curve was used to evaluate the diagnostic value of CST4 in gastric cancer.Results The positive rate of CST4 was 6.00％(3/50)in the benign gastric disease group,30.77％(8/26)in the gastric precancerous lesion group,and 66.67％(26/39)in the gastric canc-er group.The positive rate of CST4 in the gastric cancer group was higher than that in the gastric pre-cancerous lesion group and the benign gastric disease group(P＜0.05).The results of binary Logistic regression analysis showed that advanced age,high levels of serum CST4 and high levels of CEA were independent risk factors for gastric cancer occurrence(P＜0.05).The area under the curve(AUC)for CST4 alone in diagnosing gastric cancer was 0.847(95％CI,0.760 to 0.934),with an optimal cut-off value of 94.6 U/mL,the Youden index of 0.638,sensitivity of 71.8％,and specificity of 92.0％.The AUC for the combined diagnosis of gastric cancer using CST4,age and CEA was 0.959(95％CI,0.919 to 0.992),with sensitivity of 94.9％and specificity of 86.0％.Conclusion As a novel se-rum marker,CST4 has high predictive value in the auxiliary diagnosis of gastric cancer.

Objective To investigate the value of early cystatin C(Cys-C)combined with prothrombin time(PT)for assessing the severity and outcome of patients with severe fever with thrombocytopenia syndrome(SFTS).Methods The data of 101 patients with SFTS diagnosed and treated in Chaohu Hospital of Anhui Medical University from April 2021 to August 2023 were reviewed retrospectively.The patients were assigned to non-severe group or severe group according to the severity of the disease,and assigned to survivors group or deaths group according to the treatment outcome.The clinical manifestations and early laboratory test results were compared between groups.Logistic regression model was used to analyze the factors for predicting the outcome.The receiver operating characteristic(ROC)curve was used to evaluate the ability of Cys-C and PT levels alone and in combination to distinguish survivors from deaths.Results In the early stage,the patients in severe group and deaths group showed significantly higher levels of serum Cys-C,blood urea nitrogen(BUN),creatinine(Cr),uric acid(UA),PT,activated partial thromboplastin time(APTT),creatine kinase(CK)and creatine kinase MB(CKMB)but significantly lower lymphocyte count compared to the patients in the non-severe group and survivors group(P＜0.05).Multivariate binary Logistic regression analysis showed that age(OR=1.146,95％CI:1.036-1.267),PT(OR=2.643,95％CI:1.323-5.281),and Cys-C(OR=5.039,95％CI:1.548-16.395)were independent risk factors for the outcome of SFTS patients.Serum Cys-C was valuable in distinguishing survivors from deaths,the area under the ROC curve of which was 0.831.When Cys-C,PT and age were combined,the AUC was up to 0.930.Conclusions SFTS mainly occurs in farmers and elderly people.Serum PT and Cys-C levels at early stage can effectively predict the outcome of patients with SFTS.PT and Cys-C levels are expected to be biomarkers for distinguishing survivors from deaths.

Background/Aims: The accuracy of endosonographers in diagnosing gastric subepithelial lesions (SELs) using endoscopic ultrasonography (EUS) is influenced by experience and subjectivity. Artificial intelligence (AI) has achieved remarkable development in this field. This study aimed to develop an AI-based EUS diagnostic model for the diagnosis of SELs, and evaluated its efficacy with external validation. Methods: We developed the EUS-AI model with ResNeSt50 using EUS images from two hospitals to predict the histopathology of the gastric SELs originating from muscularis propria. The diagnostic performance of the model was also validated using EUS images obtained from four other hospitals. Results: A total of 2,057 images from 367 patients (375 SELs) were chosen to build the models, and 914 images from 106 patients (108 SELs) were chosen for external validation. The sensitivity, specificity, positive predictive value, negative predictive value, and accuracy of the model for differentiating gastrointestinal stromal tumors (GISTs) and non-GISTs in the external validation sets by images were 82.01%, 68.22%, 86.77%, 59.86%, and 78.12%, respectively. The sensitivity, specificity, positive predictive value, negative predictive value, and accuracy in the external validation set by tumors were 83.75%, 71.43%, 89.33%, 60.61%, and 80.56%, respectively. The EUS-AI model showed better performance (especially specificity) than some endosonographers.The model helped improve the sensitivity, specificity, and accuracy of certain endosonographers. Conclusions We developed an EUS-AI model to classify gastric SELs originating from muscularis propria into GISTs and non-GISTs with good accuracy. The model may help improve the diagnostic performance of endosonographers. Further work is required to develop a multi-modal EUS-AI system.