ObjectiveTo improve the quality and service performance of community visual health services in Shanghai, and to establish a set of reasonable and effective evaluation index system for community visual health services. MethodsCentered on the national and Shanghai-based visual health policies and based on the current status and development trends of community visual health service program in Shanghai, the candidate indicators were formed through literature review and expert interviews, firstly. The framework of an evaluation index system was formulated through qualitative research successively, which was further revised and perfected using the Delphi method. Coefficient weights were calculated using the analytic hierarchy process (AHP), culminating in the establishment of the community visual health evaluation index system, lastly. ResultsA total of 22 visual health experts from district-level center for disease control, hospital ophthalmology and leaders in charging of visual health service in community health centers participated in the Delphi questionnaire survey, with a questionnaire recovery rate of 100% and an expert authority coefficient of 0.86, indicating high credibility. After a round of correspondence to experts’ importance ratings and discussions, a comprehensive evaluation index system comprising 3 primary indicators, 12 secondary indicators, and 47 tertiary indicators, along with 5 additional indicators, was finalized. ConclusionAn index system tailored to effective evaluation for community visual health initiatives was drawn up in this study, which can promote the capacity building in community eye health services, facilitating the high-quality development of visual health courses, and enhancing residents’ eye health.

Non-alcoholic fatty liver disease (NAFLD), including non-alcoholic fatty liver (NAFL), non-alcoholic steatohepatitis (NASH), and advanced fibrosis, is a leading cause of chronic liver disease worldwide, progressing to cirrhosis and ultimately hepatocellular carcinoma (HCC). Excessive accumulation of fatty acids in the liver triggers multiple forms of hepatocyte death and exacerbates NAFLD progression, with pyroptosis and apoptosis considered key events. Recent studies show that cysteine aspartic acid specific protease-3 (caspase-3) is a central regulator of both pyroptosis and apoptosis in NAFLD. Activated caspase-3 not only directly induces apoptosis but also cleaves the N-terminal domain of gasdermin E (GSDME), disrupts cell membranes, releases inflammatory factors, and thereby mediates pyroptosis. Inhibiting caspase-3 expression in NAFLD can alleviate hepatocyte injury (such as ballooning degeneration), dampen pro-inflammatory signaling, and reduce apoptosis. Caspase-3 acts as a key node coordinating pyroptosis and apoptosis and may serve as a novel therapeutic target for the prevention and treatment of NAFLD.
Non-alcoholic Fatty Liver Disease/metabolism* ; Humans ; Pyroptosis/physiology* ; Apoptosis/physiology* ; Caspase 3/physiology* ; Animals ; Gasdermins

Over the past four decades,respiratory imaging has achieved a leapfrogging development from conventional morphological assessment to precision medicine.Beginning with conventional X-ray that laid the foundation,followed by CT technological breakthroughs,MRI explorations,and advances in nuclear medicine and molecular imaging,this review outlined the key technologies and clinical translation achievements at each stage,analyzed current research hotspots such as artificial intelligence and radiomics,radiation protection in low-dose CT,and integration of functional and molecular imaging,prospected the future trends in early diagnosis,treatment monitoring,and synergy with targeted therapy,emphasizing the paradigm shift in this field from empirical diagnosis to data-driven precise evaluation,as well as its core supportive role in diagnosis and management of respiratory diseases.

ObjectiveTo investigate the prevalence of cardiovascular diseases and their influencing factors in community-based schizophrenia patients in Shanghai, and to provide a scientific basis for the early identification and prevention of cardiovascular disease in this population. MethodsBased on the Shanghai community cohort with severe mental disorders in 2022, a total of 3 954 community-based schizophrenia patients were identified and included in this study through a stratified cluster sampling method. Basic information and relevant clinical data (including metabolic index data) were collected through questionnaire survey, physical examination and laboratory testing. Univariate analyses were performed using the chi-square tests, and multivariate logistic regression analyses were employed to identify influencing factors of comorbid cardiovascular diseases. ResultsA total of 3 954 community-based schizophrenia patients were included, of which a total of 1 237 (31.28%) patients had comorbid cardiovascular diseases. Multivariate logistic regression analyses showed that age 60 years old or above (OR=5.524, 95%CI: 3.716‒8.214), smoking behavior (OR=1.328, 95%CI: 1.042‒1.692), overweight (OR=1.900, 95%CI: 1.046‒3.451) or obesity (OR=2.678, 95%CI: 1.439‒4.985), elevated blood pressure (OR=1.546, 95%CI: 1.294‒1.846), abnormal fasting blood glucose (OR=1.552, 95%CI: 1.322‒1.823) and high-density lipoprotein cholesterol abnormalities (OR=1.283, 95%CI: 1.025‒1.606) were positively associated with the risk of comorbid cardiovascular diseases in patients with schizophrenia, while educational attainment of college/bachelor’s degree or above (OR=0.640, 95%CI: 0.450‒0.910) and being unmarried (OR=0.552, 95%CI: 0.457‒0.667) were negatively associated with the risk of cardiovascular diseases comorbidity. ConclusionAdvanced age, unhealthy behaviors and lifestyles, as well as abnormalities in blood pressure, blood glucose, and blood lipids, could all increase the risk of comorbid cardiovascular diseases in community schizophrenia patients. It is suggested to strengthen the monitoring and management of these risk factors in this population in the future, so as to achieve early detection, early diagnosis and early intervention of cardiovascular diseases.

Objective:To explore the value of non-invasive habitat imaging (HI) multi-parametric MRI (mpMRI) in predicting the risk of prostate cancer (PCa).Methods:In this cross-sectional study, 220 patients with PCa confirmed by radical prostatectomy (RP) who underwent multi-parametric MRI (mpMRI) scanning at Xijing Hospital, Air Force Military Medical University from January 2018 to May 2024 were retrospectively collected. Patients were divided into a training set (154 cases) and a test set (66 cases) by simple random sampling in a 7∶3 ratio. Based on mpMRI imaging, the apparent diffusion coefficient (ADC), perfusion fraction (f), and mean kurtosis (MK) of each voxel were integrated. The K-means clustering algorithm was used to divide the PCa target lesions into habitat subregions, generate habitat maps, and calculate the proportion of each habitat subregion in the entire lesion. According to the 2019 International Society of Urological Pathology (ISUP) guidelines, patients were categorized into a low-risk group (ISUP≤2, 65 cases) and a high-risk group (ISUP≥3, 155 cases). The RP specimens were matched with the habitat map to identify corresponding habitat subregions, and the ISUP grade of each subregion was individually evaluated to calculate the detection rate of high-risk PCa patients. The logistic regression analysis was applied to identify the independent risk factors associated with PCa risk, and the HI-clinical imaging model and clinical imaging model were constructed. The efficacy of the models was assessed using receiver operating characteristic curve.Results:Based on the optimal cluster number, the habitat was divided into three subregions. Habitat 1 had lower ADC and f values and higher MK values, while habitat 2 had the opposite characteristics, and habitat 3 was intermediate. The proportion of habitat 1 in the high-risk group was 28.8%, in the low-risk group was 8.9%. In the training set, the comparison of habitat subregions with pathological results showed that the detection rate of high-risk lesions was 66.9% (103/154) in habitat 1, 25.3% (39/154) in habitat 2, and 47.4% (73/154) in habitat 3. The logistic regression analysis indicated that the proportion of habitat 1 ( OR=3.03, 95% CI 1.77-5.18, P<0.001), prostate-specific antigen ( OR=1.66, 95% CI 1.04-2.66, P=0.034), and the prostate imaging reporting and data system score ( OR=1.65, 95% CI 1.00-2.70, P=0.048) as independent risk factors for high-risk PCa. In the training set, the area under the curve (AUC) for predicting PCa risk was 0.854 (95% CI 0.789-0.920) for the HI-clinical imaging model and 0.779 (95% CI 0.701-0.856) for the clinical imaging model. In the test set, the AUC values were 0.809 (95% CI 0.693-0.895) and 0.738 (95% CI 0.619-0.856), respectively. Conclusion:HI based on mpMRI can effectively predict the risk of PCa.

Purpose To explore the value of contrast-enhanced CT habitat imaging(HI)in preoperative non-invasive visualization for predicting pathological grading of clear cell renal carcinoma(ccRCC).Materials and Methods A retrospective analysis was conducted on enhanced CT images and clinical data from 240 patients with pathologically confirmed ccRCC at Xijing Hospital,the Fourth Military Medical University from January 2020 to December 2023.All patients were randomly divided into training and test sets at a 7:3 ratio and classified into low-grade group(International Society of Urological Pathology Ⅰ-Ⅱ)and high-grade group(International Society of Urological Pathology Ⅲ-Ⅳ)based on postoperative pathology.Using wash-in and wash-out parametric maps,the tumors were segmented into three perfusion-based habitat subregions(low,medium and high)via K-means clustering,and the volume fraction of each subregion was calculated.Predictive factors were selected from habitat features and clinical variables(including sex,age,tumor size,etc.)using Logistic regression.Three models were constructed:a clinical model,a habitat imaging model and a combined clinical-habitat model.Model performance was evaluated using receiver operating characteristic curve,calibration curve and decision curve analysis.Results Habitat 3 exhibited higher wash-in and wash-out gradients compared to Habitats 1 and 2,indicating hyper perfusion.Its proportion was significantly higher in the low-grade group than in the high-grade group(Z=-7.71,-5.11,both P<0.01).Multivariate Logistic regression identified hypertension,maximum tumor diameter and platelet-to-lymphocyte ratio as independent risk factors for high-grade ccRCC,while the proportion of Habitat 3 was a protective factor(OR=0.297,95％CI 0.184-0.479).The combined clinical-habitat model demonstrated the highest predictive performance[area under the curve(AUC)=0.938],significantly outperforming the clinical model(AUC=0.801,Z=-3.832,P<0.01)and the habitat imaging model(AUC=0.895,Z=-2.157,P=0.031).Conclusion The clinical-habitat imaging model achieves the highest predictive performance for ccRCC pathological grading.Contrast-enhanced CT habitat imaging provides significant incremental value in predicting ccRCC pathological grading,showing potential to guide precision medicine in clinical practice.

Objective To explore the correlations between interleukin-6(IL-6),interleukin-8(IL-8),antimicrobial peptide LL-37 and the microbial abundance and the degree of mucosal injury in patients with trichomonal vaginitis(TV).Methods A total of 120 TV patients admitted from January 2022 to December 2024 were selected and divided into the occurrence group(n=63)and the non-occurrence group(n=57)according to presence and absence of mucosal injury.The general data,IL-6,IL-8,LL-37 and microbial abundance were compared between the two groups.According to the degree of mucosal injury,the occurrence group was divided into the mild subgroup(n=34),the moderate subgroup(n=20),and the severe subgroup(n=9).Pearson correlation analysis was applied to investigate the correlations between IL-6,IL-10,antimicrobial peptide LL-37,microbial abundance and the degree of mucosal injury.Multivariate logistic regression was used to analyze the influencing factors of mucosal injury in TV patients.The receiver operating characteristic curve(ROC)was used to evaluate the predictive value of relevant indicators for mucosal injury in TV patients.Result The levels of IL-6,IL-8,antimicrobial peptide LL-37,and the abundance of Actinobacteria and Gardnerella in the occurrence group were higher than those in the non-occurrence group,while the abundance of Firmicutes and Lactobacillus was lower than that in the non-occurrence group(P<0.05,P<0.01).Pearson correlation analysis showed that IL-6,IL-8,antimicrobial peptide LL-37,and abundance of Actinobacteria and Gardnerella were positively correlated with the degree of mucosal injury(r=0.543,0.713,0.352,0.409,0.659,P<0.01),while the abundance of Firmicutes and Lactobacillus was negatively correlated with the degree of mucosal injury(r=-0.540,-0.504,P<0.01).Multivariate logistic regression analysis showed that IL-6,IL-8,antimicrobial peptide LL-37,and abundance of Actinobacteria were risk factors for mucosal injury in TV patients,while the abundance of Firmicutes and the abundance of Lactobacillus were protective factors(P<0.05).The ROC curve showed that the area under the ROC curve(AUC)of the combined detection of IL-6,IL-8,antibacterial peptide 37,and the abundance of Actinobacteria,Firmicutes,and Lactobacillus in evaluating mucosal injury in TV patients was higher than that of the single detection(P<0.01).Conclusion IL-6,IL-8,antimicrobial peptide LL-37 and microbial abundance are closely related to the degree of mucosal injury in TV patients.The combined detection has a high value in evaluating the occurrence of mucosal injury in TV patients.

Objective To explore the correlations between interleukin-6(IL-6),interleukin-8(IL-8),antimicrobial peptide LL-37 and the microbial abundance and the degree of mucosal injury in patients with trichomonal vaginitis(TV).Methods A total of 120 TV patients admitted from January 2022 to December 2024 were selected and divided into the occurrence group(n=63)and the non-occurrence group(n=57)according to presence and absence of mucosal injury.The general data,IL-6,IL-8,LL-37 and microbial abundance were compared between the two groups.According to the degree of mucosal injury,the occurrence group was divided into the mild subgroup(n=34),the moderate subgroup(n=20),and the severe subgroup(n=9).Pearson correlation analysis was applied to investigate the correlations between IL-6,IL-10,antimicrobial peptide LL-37,microbial abundance and the degree of mucosal injury.Multivariate logistic regression was used to analyze the influencing factors of mucosal injury in TV patients.The receiver operating characteristic curve(ROC)was used to evaluate the predictive value of relevant indicators for mucosal injury in TV patients.Result The levels of IL-6,IL-8,antimicrobial peptide LL-37,and the abundance of Actinobacteria and Gardnerella in the occurrence group were higher than those in the non-occurrence group,while the abundance of Firmicutes and Lactobacillus was lower than that in the non-occurrence group(P<0.05,P<0.01).Pearson correlation analysis showed that IL-6,IL-8,antimicrobial peptide LL-37,and abundance of Actinobacteria and Gardnerella were positively correlated with the degree of mucosal injury(r=0.543,0.713,0.352,0.409,0.659,P<0.01),while the abundance of Firmicutes and Lactobacillus was negatively correlated with the degree of mucosal injury(r=-0.540,-0.504,P<0.01).Multivariate logistic regression analysis showed that IL-6,IL-8,antimicrobial peptide LL-37,and abundance of Actinobacteria were risk factors for mucosal injury in TV patients,while the abundance of Firmicutes and the abundance of Lactobacillus were protective factors(P<0.05).The ROC curve showed that the area under the ROC curve(AUC)of the combined detection of IL-6,IL-8,antibacterial peptide 37,and the abundance of Actinobacteria,Firmicutes,and Lactobacillus in evaluating mucosal injury in TV patients was higher than that of the single detection(P<0.01).Conclusion IL-6,IL-8,antimicrobial peptide LL-37 and microbial abundance are closely related to the degree of mucosal injury in TV patients.The combined detection has a high value in evaluating the occurrence of mucosal injury in TV patients.

Purpose To explore the value of contrast-enhanced CT habitat imaging(HI)in preoperative non-invasive visualization for predicting pathological grading of clear cell renal carcinoma(ccRCC).Materials and Methods A retrospective analysis was conducted on enhanced CT images and clinical data from 240 patients with pathologically confirmed ccRCC at Xijing Hospital,the Fourth Military Medical University from January 2020 to December 2023.All patients were randomly divided into training and test sets at a 7:3 ratio and classified into low-grade group(International Society of Urological Pathology Ⅰ-Ⅱ)and high-grade group(International Society of Urological Pathology Ⅲ-Ⅳ)based on postoperative pathology.Using wash-in and wash-out parametric maps,the tumors were segmented into three perfusion-based habitat subregions(low,medium and high)via K-means clustering,and the volume fraction of each subregion was calculated.Predictive factors were selected from habitat features and clinical variables(including sex,age,tumor size,etc.)using Logistic regression.Three models were constructed:a clinical model,a habitat imaging model and a combined clinical-habitat model.Model performance was evaluated using receiver operating characteristic curve,calibration curve and decision curve analysis.Results Habitat 3 exhibited higher wash-in and wash-out gradients compared to Habitats 1 and 2,indicating hyper perfusion.Its proportion was significantly higher in the low-grade group than in the high-grade group(Z=-7.71,-5.11,both P<0.01).Multivariate Logistic regression identified hypertension,maximum tumor diameter and platelet-to-lymphocyte ratio as independent risk factors for high-grade ccRCC,while the proportion of Habitat 3 was a protective factor(OR=0.297,95％CI 0.184-0.479).The combined clinical-habitat model demonstrated the highest predictive performance[area under the curve(AUC)=0.938],significantly outperforming the clinical model(AUC=0.801,Z=-3.832,P<0.01)and the habitat imaging model(AUC=0.895,Z=-2.157,P=0.031).Conclusion The clinical-habitat imaging model achieves the highest predictive performance for ccRCC pathological grading.Contrast-enhanced CT habitat imaging provides significant incremental value in predicting ccRCC pathological grading,showing potential to guide precision medicine in clinical practice.

Over the past four decades,respiratory imaging has achieved a leapfrogging development from conventional morphological assessment to precision medicine.Beginning with conventional X-ray that laid the foundation,followed by CT technological breakthroughs,MRI explorations,and advances in nuclear medicine and molecular imaging,this review outlined the key technologies and clinical translation achievements at each stage,analyzed current research hotspots such as artificial intelligence and radiomics,radiation protection in low-dose CT,and integration of functional and molecular imaging,prospected the future trends in early diagnosis,treatment monitoring,and synergy with targeted therapy,emphasizing the paradigm shift in this field from empirical diagnosis to data-driven precise evaluation,as well as its core supportive role in diagnosis and management of respiratory diseases.