Objective:To investigate the value of habitat radiomic features based on gadolinium ethoxybenzyl diethylenetriamine pentaacetic acid (Gd-EOB-DTPA)-enhanced MRI in establishing a predictive model for cytokeratin 19 (CK19) expression in hepatocellular carcinoma (HCC) and to evaluate its role in prognostic risk stratification.Methods:This multicenter case-control study retrospectively enrolled 489 patients with pathologically confirmed HCC who underwent Gd-EOB-DTPA-enhanced MRI between June 2016 and June 2024. Among them, 346 patients from the First Affiliated Hospital of Soochow University were divided into a training cohort ( n=245) and an internal test cohort ( n=101) via stratified sampling at a 7∶3 ratio. And 143 patients from Nantong Third Hospital Affiliated to Nantong University served as an external validation cohort. The training cohort included 53 CK19-positive and 192 CK19-negative patients. The internal test cohort included 21 CK19-positive and 80 CK19-negative patients. The external validation cohort included 30 CK19-positive and 113 CK19-negative patients. Univariate logistic regression analysis was performed to identify potential factors associated with CK19 expression, and a clinical-radiologic model was constructed. The k-means clustering algorithm was applied to segment target HCC lesions into 3 subregions. Radiomic features were extracted and selected from these habitat subregions. Habitat radiomics models were constructed for the arterial phase (AP), portal venous phase, hepatobiliary phase (HBP), and combined phases (CP). Multivariate logistic regression analysis identified independent clinical and radiologic predictors of CK19 expression, and the optimal habitat model score was integrated to build a clinical-radiologic-habitat combined model. The area under the receiver operating characteristic curve (AUC) was used to evaluate model predictive performance. Recurrence-free survival (RFS) was analyzed using the Kaplan-Meier method and the differences in survival curves were compared with the log-rank test. Results:Univariate logistic regression analysis revealed that alpha-fetoprotein (AFP) ( OR=2.629, 95% CI 1.412-4.896, P=0.002), AP enhancement ( OR=3.636, 95% CI 1.642-8.052, P=0.001), AP peritumoral enhancement ( OR=2.219, 95% CI 1.084-4.542, P=0.029), and HBP peritumoral hypointensity ( OR=2.010, 95% CI 1.004-4.021, P=0.049) were potential factors associated with CK19 expression, which were incorporated into the clinical-radiologic model. In the internal and external validation cohorts, the AUC of the clinical-radiologic model was 0.690 (95% CI 0.590-0.778) and 0.650 (95% CI 0.565-0.727), respectively. The habitat radiomics model based on CP images demonstrated the highest performance. It achieved AUC of 0.729 (95% CI 0.622-0.836) and 0.725 (95% CI 0.607-0.842) in the internal and external validation cohorts, respectively. Multivariate analysis identified AFP ( OR=2.494, 95% CI 1.163-5.348, P=0.019), AP enhancement ( OR=5.230, 95% CI 1.868-14.643, P=0.002) and habitat radiomics model score ( OR=4.105, 95% CI 2.643-6.368, P<0.001) as independent predictors of CK19 positivity. Based on these factors, a combined clinical-radiologic-habitat combined model was established. The clinical-radiologic-habitat combined model achieved AUCs of 0.767 (95% CI 0.671-0.846) and 0.730 (95% CI 0.649-0.801) in the internal and external validation cohorts, respectively. Significant differences in RFS were observed between the CK19-positive group (25.1 month) and CK19-negative group (51.0 month) as predicted by the clinical-radiologic-habitat model ( χ2=4.17, P=0.041). Conclusion:The clinical-radiologic-habitat combined model based on Gd-EOB-DTPA-enhanced MRI habitat radiomics demonstrates good predictive performance for CK19 expression in HCC and offers valuable prognostic stratification for clinical practice.

Objective:To explore the value of the deep learning model based on gadolinium ethoxybenzyl diethylenetriamine pentaacetic acid (Gd-EOB-DTPA) enhanced MRI in preoperatively predicting vessels completely encapsulating tumor clusters (VETC) in hepatocellular carcinoma (HCC).Methods:This study adopted a case-control design to retrospectively analyze 420 patients with HCC confirmed by postoperative pathology who underwent Gd-EOB-DTPA enhanced MRI between June 2016 and March 2023. A total of 420 patients were divided into a training set ( n=305) from the First Affiliated Hospital of Soochow University and an external validation set ( n=115) from Affiliated Nantong Hospital 3 of Nantong University. Based on postoperative pathological findings, patients were stratified into VETC-positive and VETC-negative groups. The training set comprised 161 VETC-positive cases and 144 VETC-negative cases, while the external validation set included 55 VETC-positive cases and 60 VETC-negative cases. Tumor regions of interest in arterial, portal venous, and hepatobiliary phases were manually delineated using ITK-SNAP software. Pre-trained Vgg19, Densenet121, and Vision Transformer (ViT) models were employed for transfer learning, extracting deep learning features from each image. Feature data were processed using FAE software, and 12 logistic regression models (arterial phase, portal venous phase, hepatobiliary phase, and combined three-phase models) were constructed to select the optimal deep learning model. Independent predictors in clinical characteristics were identified through univariate and multivariate logistic analyses to establish a clinical model for predicting VETC pattern. Subsequently, a clinical-deep learning fusion model was developed by integrating these clinical predictors with the optimal deep learning features. Model performance in predicting VETC-positive HCC was evaluated using receiver operating characteristic curves, calibration curves, and decision curve analysis (DCA). Results:In the external validation set, the area under the curve (AUC) of the Vgg19 model in the arterial phase, portal venous phase, hepatobiliary phase, and combined three-phase, respectively were 0.799,0.756,0.789,0.821, which were higher than those of Densenet121 (AUC: 0.544,0.581,0.544,0.583) and ViT (AUC: 0.740,0.752,0.785,0.767) model. The three-phase combined Vgg19 model achieved the highest AUC of 0.821 (95% CI 0.746-0.897). Multivariate logistic regression identified alpha-fetoprotein level ( OR=1.826,95% CI 1.069-3.120, P=0.028) and tumor diameter ( OR=1.329,95% CI 1.206-1.466, P<0.001) as independent predictors of VETC-positive HCC, forming the clinical model with an AUC of 0.789 (95% CI 0.703-0.859). The clinical-deep learning fusion model further achieved the AUC of 0.825 (95% CI 0.749-0.900). Calibration curves confirmed high concordance between predicted and actual probabilities for the three-phase Vgg19 model, while DCA revealed greater net clinical benefit for the combined Vgg19 and fusion models compared with the clinical model alone. Conclusions:The deep learning model based on Gd-EOB-DTPA enhanced MRI can be used to predict VETC of HCC preoperatively, among which the three-phase combined Vgg19 model and the clinical-deep learning model provide high predictive value.

Objective:To conduct performance tests on medical ultrasound probes repaired by the third party,and explore whether the key parameters of the probes of third-party repair can meet the requirements of clinical use for quality.Methods:A total of 79 ultrasound probes that had been repaired by the third party were selected from different medical institutions.The performance tests were conducted on multiple parameters of ultrasound probes of different models and brands in accordance with national technical standards and relevant industry norms.Then,the test results were analyzed,studied and evaluated.Results:The tested results of the temperature rise and the leakage current of the ultrasound probes,which were repaired by the third party,met the national standards.However,in the test for sound power,26.58%of the probes failed to meet the national standards,which outputted sound intensity that was calculated was higher than the specified value.Conclusion:The general performance of the probes that have been repaired by the third party is well,but the quality of the repair is uneven levels,and some indicators do not meet national standards or industry norms,which might lead to occur risks in ultrasound diagnosis of medical institutions.It is recommended to implement regular test for quality and performance of medical ultrasound equipment,and establish a method and system for quality monitoring and re-evaluation after sale of repair for medical ultrasound,so as to ensure the use and safety of the equipment.

Objective To investigate the growth, development and health status of primary and secondary school students in Xinzhou District of Wuhan, and analyze the detection and change trend of common diseases in primary and secondary school students, and to provide a basis for relevant departments to formulate prevention and control measures of common diseases in students. Methods The monitoring data of common diseases and health influencing factors of primary and secondary school students in Xinzhou District from 2019 to 2022 were analyzed and compared according to different genders, different grades and ages. SPSS 20.0 software was used to analyze the data of detection rates of myopia, dental caries, obesity, malnutrition and abnormal spinal curvature. Results The overall detection rates of myopia, dental caries, malnutrition, obesity and abnormal spinal curvature were 57.00%, 58.45%, 4.60%, 14.91%, and 6.33%, respectively, in Xinzhou District from 2019 to 2022. The annual change rates were 7.22%, 15.10%, -2.72%, 13.29%, and 4.91%, respectively. The detection rates of myopia, dental caries, obesity and abnormal spinal curvature showed an increasing trend in each year (χ² ≥17.22, P<0.001). The detection rates of myopia and malnutrition increased with the increase of age and school level (both χ²≥42.37, P<0.001), while the opposite was true for the detection rates of dental caries and obesity (both χ²≥14.26, P<0.001). The detection rates of myopia and dental caries were higher in girls than in boys (both χ²≥33.66, P<0.001), while the detection rates of obesity and abnormal spinal curvature were higher in boys than in girls (both χ²≥8.22, P<0.005). The detection rates of myopia, dental caries, obesity and abnormal spinal curvature in 2019 were lower than those in 2020-2022 (χ²≥4.11, P<0.05), while the detection rates of malnutrition had decreased. Conclusion The growth, development and health status of primary and secondary school students in Xinzhou District are serious. The detection rate of common diseases such as myopia, dental caries, obesity and abnormal curvature of the spine is on the rise, which should be the focus of the surveillance work of common diseases in primary and secondary school students in the future, and comprehensive intervention measures are urgently needed to prevent and control these common diseases.

Objective To explore the mechanism underlying the role of disulfidptosis-related genes(DRGs)in the disease progression of metabolically associated fatty liver disease(MAFLD)based on bioinformatics.Methods In this study,the GEO database was utilized to screen for eligible MAFLD expression data,conduct differential gene analysis,and identify DRGs through consistent clustering to subtype MAFLD patients.The immune infiltration status among subtypes was further evaluated,and the infiltration of immune cells was analyzed using the CIBERSORT algorithm.The gene modules related to the disease were selected through weighted gene co-expression network analysis(WGCNA).Subsequently,a diagnostic model was constructed based on DRGs using machine learning models,and the performance of the model was verified.Finally,the stability of DRGs among different subtypes was evaluated using an external dataset,and the significance of the results was analyzed using statistical tests.Results Through the analysis of the dataset GSE31803,six disulfide death genes,namely,SLC3A2,NCKAP1,CYFIP1,FLNA,MYL6 and MYH10,which were closely related to the clinical characteristics of MAFLD,were screened out.MAFLD patients were classified into two subtypes,with subtype 1 having a higher level of immune cell infiltration.Key gene modules were identified through WGCNA.Through machine learning screening,the support vector machine(SVM)model was determined as the optimal classification model.External validation confirmed the stability and effectiveness of the key genes in different subtypes of MAFLD.Conclusion Based on DRGs,two highly heterogeneous subtypes of MAFLD were identified,which exhibited significant differences in clinical characteristics,biological processes and immune status,indicating that DRGs play a crucial role in the occurrence and development of MAFLD.

Polymyxin serves as the"last line of defense"for treating infection with multidrug-resistant Gram-ne-gative bacteria.However,the emergence and spread of polymyxin-resistant genes such as mcr-1 severely weakens its clinical efficacy.This paper systematically summarizes the antimicrobial and resistance mechanisms of polymy-xin,comprehensively summarizes the current research progresses in polymyxin sensitizers particular focusing on three aspects:natural compounds,synthetic small molecules,and drug repurposing.Furthermore,this paper explores the innovative strategies of gene intervention,new targets,and nanotechnology-based formulations in the develop-ment of sensitizer,aiming to provide systematic theoretical support and research ideas against polymyxin resistance.

Objective To explore the mechanism underlying the role of disulfidptosis-related genes(DRGs)in the disease progression of metabolically associated fatty liver disease(MAFLD)based on bioinformatics.Methods In this study,the GEO database was utilized to screen for eligible MAFLD expression data,conduct differential gene analysis,and identify DRGs through consistent clustering to subtype MAFLD patients.The immune infiltration status among subtypes was further evaluated,and the infiltration of immune cells was analyzed using the CIBERSORT algorithm.The gene modules related to the disease were selected through weighted gene co-expression network analysis(WGCNA).Subsequently,a diagnostic model was constructed based on DRGs using machine learning models,and the performance of the model was verified.Finally,the stability of DRGs among different subtypes was evaluated using an external dataset,and the significance of the results was analyzed using statistical tests.Results Through the analysis of the dataset GSE31803,six disulfide death genes,namely,SLC3A2,NCKAP1,CYFIP1,FLNA,MYL6 and MYH10,which were closely related to the clinical characteristics of MAFLD,were screened out.MAFLD patients were classified into two subtypes,with subtype 1 having a higher level of immune cell infiltration.Key gene modules were identified through WGCNA.Through machine learning screening,the support vector machine(SVM)model was determined as the optimal classification model.External validation confirmed the stability and effectiveness of the key genes in different subtypes of MAFLD.Conclusion Based on DRGs,two highly heterogeneous subtypes of MAFLD were identified,which exhibited significant differences in clinical characteristics,biological processes and immune status,indicating that DRGs play a crucial role in the occurrence and development of MAFLD.

Polymyxin serves as the"last line of defense"for treating infection with multidrug-resistant Gram-ne-gative bacteria.However,the emergence and spread of polymyxin-resistant genes such as mcr-1 severely weakens its clinical efficacy.This paper systematically summarizes the antimicrobial and resistance mechanisms of polymy-xin,comprehensively summarizes the current research progresses in polymyxin sensitizers particular focusing on three aspects:natural compounds,synthetic small molecules,and drug repurposing.Furthermore,this paper explores the innovative strategies of gene intervention,new targets,and nanotechnology-based formulations in the develop-ment of sensitizer,aiming to provide systematic theoretical support and research ideas against polymyxin resistance.

Objective:To investigate the value of habitat radiomic features based on gadolinium ethoxybenzyl diethylenetriamine pentaacetic acid (Gd-EOB-DTPA)-enhanced MRI in establishing a predictive model for cytokeratin 19 (CK19) expression in hepatocellular carcinoma (HCC) and to evaluate its role in prognostic risk stratification.Methods:This multicenter case-control study retrospectively enrolled 489 patients with pathologically confirmed HCC who underwent Gd-EOB-DTPA-enhanced MRI between June 2016 and June 2024. Among them, 346 patients from the First Affiliated Hospital of Soochow University were divided into a training cohort ( n=245) and an internal test cohort ( n=101) via stratified sampling at a 7∶3 ratio. And 143 patients from Nantong Third Hospital Affiliated to Nantong University served as an external validation cohort. The training cohort included 53 CK19-positive and 192 CK19-negative patients. The internal test cohort included 21 CK19-positive and 80 CK19-negative patients. The external validation cohort included 30 CK19-positive and 113 CK19-negative patients. Univariate logistic regression analysis was performed to identify potential factors associated with CK19 expression, and a clinical-radiologic model was constructed. The k-means clustering algorithm was applied to segment target HCC lesions into 3 subregions. Radiomic features were extracted and selected from these habitat subregions. Habitat radiomics models were constructed for the arterial phase (AP), portal venous phase, hepatobiliary phase (HBP), and combined phases (CP). Multivariate logistic regression analysis identified independent clinical and radiologic predictors of CK19 expression, and the optimal habitat model score was integrated to build a clinical-radiologic-habitat combined model. The area under the receiver operating characteristic curve (AUC) was used to evaluate model predictive performance. Recurrence-free survival (RFS) was analyzed using the Kaplan-Meier method and the differences in survival curves were compared with the log-rank test. Results:Univariate logistic regression analysis revealed that alpha-fetoprotein (AFP) ( OR=2.629, 95% CI 1.412-4.896, P=0.002), AP enhancement ( OR=3.636, 95% CI 1.642-8.052, P=0.001), AP peritumoral enhancement ( OR=2.219, 95% CI 1.084-4.542, P=0.029), and HBP peritumoral hypointensity ( OR=2.010, 95% CI 1.004-4.021, P=0.049) were potential factors associated with CK19 expression, which were incorporated into the clinical-radiologic model. In the internal and external validation cohorts, the AUC of the clinical-radiologic model was 0.690 (95% CI 0.590-0.778) and 0.650 (95% CI 0.565-0.727), respectively. The habitat radiomics model based on CP images demonstrated the highest performance. It achieved AUC of 0.729 (95% CI 0.622-0.836) and 0.725 (95% CI 0.607-0.842) in the internal and external validation cohorts, respectively. Multivariate analysis identified AFP ( OR=2.494, 95% CI 1.163-5.348, P=0.019), AP enhancement ( OR=5.230, 95% CI 1.868-14.643, P=0.002) and habitat radiomics model score ( OR=4.105, 95% CI 2.643-6.368, P<0.001) as independent predictors of CK19 positivity. Based on these factors, a combined clinical-radiologic-habitat combined model was established. The clinical-radiologic-habitat combined model achieved AUCs of 0.767 (95% CI 0.671-0.846) and 0.730 (95% CI 0.649-0.801) in the internal and external validation cohorts, respectively. Significant differences in RFS were observed between the CK19-positive group (25.1 month) and CK19-negative group (51.0 month) as predicted by the clinical-radiologic-habitat model ( χ2=4.17, P=0.041). Conclusion:The clinical-radiologic-habitat combined model based on Gd-EOB-DTPA-enhanced MRI habitat radiomics demonstrates good predictive performance for CK19 expression in HCC and offers valuable prognostic stratification for clinical practice.

Objective:To explore the value of the deep learning model based on gadolinium ethoxybenzyl diethylenetriamine pentaacetic acid (Gd-EOB-DTPA) enhanced MRI in preoperatively predicting vessels completely encapsulating tumor clusters (VETC) in hepatocellular carcinoma (HCC).Methods:This study adopted a case-control design to retrospectively analyze 420 patients with HCC confirmed by postoperative pathology who underwent Gd-EOB-DTPA enhanced MRI between June 2016 and March 2023. A total of 420 patients were divided into a training set ( n=305) from the First Affiliated Hospital of Soochow University and an external validation set ( n=115) from Affiliated Nantong Hospital 3 of Nantong University. Based on postoperative pathological findings, patients were stratified into VETC-positive and VETC-negative groups. The training set comprised 161 VETC-positive cases and 144 VETC-negative cases, while the external validation set included 55 VETC-positive cases and 60 VETC-negative cases. Tumor regions of interest in arterial, portal venous, and hepatobiliary phases were manually delineated using ITK-SNAP software. Pre-trained Vgg19, Densenet121, and Vision Transformer (ViT) models were employed for transfer learning, extracting deep learning features from each image. Feature data were processed using FAE software, and 12 logistic regression models (arterial phase, portal venous phase, hepatobiliary phase, and combined three-phase models) were constructed to select the optimal deep learning model. Independent predictors in clinical characteristics were identified through univariate and multivariate logistic analyses to establish a clinical model for predicting VETC pattern. Subsequently, a clinical-deep learning fusion model was developed by integrating these clinical predictors with the optimal deep learning features. Model performance in predicting VETC-positive HCC was evaluated using receiver operating characteristic curves, calibration curves, and decision curve analysis (DCA). Results:In the external validation set, the area under the curve (AUC) of the Vgg19 model in the arterial phase, portal venous phase, hepatobiliary phase, and combined three-phase, respectively were 0.799,0.756,0.789,0.821, which were higher than those of Densenet121 (AUC: 0.544,0.581,0.544,0.583) and ViT (AUC: 0.740,0.752,0.785,0.767) model. The three-phase combined Vgg19 model achieved the highest AUC of 0.821 (95% CI 0.746-0.897). Multivariate logistic regression identified alpha-fetoprotein level ( OR=1.826,95% CI 1.069-3.120, P=0.028) and tumor diameter ( OR=1.329,95% CI 1.206-1.466, P<0.001) as independent predictors of VETC-positive HCC, forming the clinical model with an AUC of 0.789 (95% CI 0.703-0.859). The clinical-deep learning fusion model further achieved the AUC of 0.825 (95% CI 0.749-0.900). Calibration curves confirmed high concordance between predicted and actual probabilities for the three-phase Vgg19 model, while DCA revealed greater net clinical benefit for the combined Vgg19 and fusion models compared with the clinical model alone. Conclusions:The deep learning model based on Gd-EOB-DTPA enhanced MRI can be used to predict VETC of HCC preoperatively, among which the three-phase combined Vgg19 model and the clinical-deep learning model provide high predictive value.