Objective: o analyze the epidemic characteristics of five sexually transmitted diseases (STDs), including syphilis, gonorrhea, condyloma acuminatum, genital herpes and genital Chlamydia trachomatis infection in Yangzhou City, Jiangsu Province from 2019 to 2023, so as to provide the reference for the prevention and control strategies of STDs. Methods: Data of the onset time and diagnostic types of STDs cases in Yangzhou City from 2019 to 2023 were collected from the Infectious Disease Surveillance System of Chinese Disease Prevention and Control Information System. The temporal, regional and population characteristics of five types of STDs was analyzed using the descriptive epidemiological method. Results: A total of 10 895 cases of STDs were reported in Yangzhou City from 2019 to 2023, with an average annual reported incidence rate of 47.83/105. The average annual reported incidence rates of syphilis, gonorrhea, condyloma acuminatum, genital herpes and genital Chlamydia trachomatis infections were 41.11/105, 2.83/105, 2.59/105, 0.43/105 and 0.85/105, respectively. The reported incidence rate of STDs showed a decreasing trend from 2019 to 2023 (P<0.05), with an average annual growth rate of -3.44%. The reported incidence rates of syphilis and gonorrhea showed a decreasing trend (both P<0.05), with average annual growth rates of -4.26% and -6.47%, respectively. The reported incidence rate of genital Chlamydia trachomatis infection showed an increasing trend (P<0.05), with an average annual growth rate of 22.32%. Baoying County, Guangling District and Hanjiang District had the top three reported incidence rates of STDs, at 56.61/105, 55.61/105 and 46.50/105, respectively. The average annual reported incidence rate of STDs among males was higher than that among females (53.19/105 vs. 42.54/105, P<0.05). The STD cases were primarily people aged 50 years and above, with 6 641 cases accounting for 60.95%. The occupations of STD cases were mainly farmers, housekeepers and unemployed, with 4 670 and 3 273 cases accounting for 42.86% and 30.04%, respectively. Conclusions The overall reported incidence of STDs in Yangzhou City from 2019 to 2023 showed a downward trend, while the reported incidence of genital Chlamydia trachomatis infection showed an upward trend. The individuals aged 50 years and above, farmers, housekeepers and the unemployed were identified as high-risk groups for STDs.

Objective To explore the correlation of MET status in patients with advanced prostatic acinar adenocarcinoma with the clinical pathological parameters and prognosis. Methods The specimen from 135 patients with advanced prostatic acinar adenocarcinoma was included. The expression of c-MET protein was detected via immunohistochemistry, and MET gene amplification was assessed by fluorescence in situ hybridization. The relationships of c-MET expression and gene amplification with clinicopathological features and prognosis were analyzed. Results The positive expression rate of c-MET was 52.60% (71/135). Compared with the c-MET expression in adjacent tissues, that in tumor tissues showed lower heterogeneous expression. Among the cases, 1.71% (2/117) exhibited MET gene polyploidy, but no gene amplification was detected. Positive c-MET expression was significantly correlated with high Gleason scores and grade groups (P=0.0073). However, no significant relationship was found between c-MET expression and progression-free survival or post-treatment t-PSA levels. Conclusion c-MET protein is expressed at a relatively low level in advanced prostatic acinar adenocarcinoma, suggesting that c-MET may not be a viable target for ADC drug development in prostatic acinar adenocarcinoma.

Objective: To investigate the awareness rate of non-occupational post-exposure prophylaxis (nPEP) for AIDS and its influencing factors among men who have sex with men (MSM) in Yangzhou City, Jiangsu Province. Methods: From January to June 2024, MSM aged ≥16 years in Yangzhou City were recruited using the snowball sampling method. Basic information, sexual behavior characteristics, awareness and usage of nPEP, and awareness of pre-exposure prophylaxis (PrEP) for AIDS were collected through questionnaire surveys. Factors affecting awareness of nPEP among MSM were analyzed using multivariable logistic regression model. Results: A total of 740 participants were enrolled, with a median age of 29 (interquartile range, 14) years. There were 497 participants with a college degree and above educational level, accounting for 67.16%. A total of 541 participants resided in urban areas, accounting for 73.11%. The awareness rate of nPEP was 57.30%. The primary channels for awareness were the internet and healthcare institution promotions, with 159 and 119 participants, accounting for 37.50% and 28.07%, respectively. A total of 57 participants had utilized nPEP, with a usage rate of 7.70%. The main reason for using nPEP was having unprotected sex, with 21 participants, accounting for 36.84%. Multivariable logistic regression analysis revealed that unmarried MSM (OR=2.369, 95%CI: 1.236-4.540) and those who were aware of PrEP (OR=28.067, 95%CI: 17.664-44.597) had a significantly higher likelihood of being aware of nPEP. Conclusions The awareness rate and usage rate of nPEP among MSM in Yangzhou City are both relatively low. Awareness of nPEP is mainly influenced by marital status and whether participants are aware of PrEP.

Age-related macular degeneration (AMD) is a disease that affects the vision of elderly individuals worldwide. Although current therapeutics have shown effectiveness against AMD, some patients may remain unresponsive and continue to experience disease progression. Therefore, in-depth knowledge of the mechanism underlying AMD pathogenesis is urgently required to identify potential drug targets for AMD treatment. Recently, studies have suggested that dysfunction of mitochondria can lead to the aggregation of reactive oxygen species (ROS) and activation of the cyclic GMP-AMP synthase (cGAS)/stimulator of interferon genes (STING) innate immunity pathways, ultimately resulting in sterile inflammation and cell death in various cells, such as cardiomyocytes and macrophages. Therefore, combining strategies targeting mitochondrial dysfunction and inflammatory mediators may hold great potential in facilitating AMD management. Notably, emerging evidence indicates that natural products targeting mitochondrial quality control (MQC) and the cGAS/STING innate immunity pathways exhibit promise in treating AMD. Here, we summarize phytochemicals that could directly or indirectly influence the MQC and the cGAS/STING innate immunity pathways, as well as their interconnected mediators, which have the potential to mitigate oxidative stress and suppress excessive inflammatory responses, thereby hoping to offer new insights into therapeutic interventions for AMD treatment.

CD8⁺ T cell-based immune-therapeutics, including immune checkpoint inhibitors and adoptive cell therapies (tumor-infiltrating lymphocytes (TILs), T cell receptor-engineered T cells (TCR-T), chimeric antigen receptor T cells (CAR-T)), have achieved significant successes and prolonged patient survival to varying extents and even achieved cure in some cases. However, immunotherapy resistance and tumor insusceptibility frequently occur, leading to treatment failure. Recent evidences have highlighted the ponderance of tumor cells metabolic reprogramming in establishing an immunosuppressive milieu through the secretion of harmful metabolites, immune-inhibitory cytokines, and alteration of gene expression, which suppress the activity of immune cells, particularly CD8⁺ T cells to evade immune surveillance. Therefore, targeting tumor cell metabolic adaptations to reshape the immune microenvironment holds promise as an immunomodulatory strategy to facilitate immunotherapy. Here, we summarize recent advances in the crosstalk between immunotherapy and tumor reprogramming, focusing on the regulatory mechanisms underlying tumor cell glucose metabolism, amino acid metabolism, and lipid metabolism in influencing CD8⁺ T cells to provide promising metabolic targets or combinational strategies for immunotherapy.

AIM:To investigate the influencing factors of abnormal telangiectasia secondary to diabetic retinopathy(DR).METHODS: Prospective studies. A total of 153 cases(240 eyes)with DR treated in our hospital from January 2021 to January 2023 were selected to analyze the risk factors of abnormal telangiectasia secondary to DR and its predictive efficacy.RESULTS: The patients were divided into dilated group(77 eyes of 40 cases)and non-dilated group(163 eyes of 113 cases)according to whether they had secondary abnormal telangiectasia. There were significant differences in diabetic macular edema, hard exudates grade and fasting blood glucose level between the two groups(P<0.05). Logistic regression analysis showed that diabetic macular edema, high hard exudates grade and high blood glucose level were the risk factors for abnormal telangiectasia secondary to DR(P<0.05).CONCLUSION: The occurrence of telangiectasia secondary to DR may be related to diabetic macular edema, grade 3 hard exudates and high blood glucose level.

Objective To explore the performance of a new multimodal feature fusion classification model based on distance matching and discriminative representation learning for differentiating high-grade glioma(HGG)from solitary brain metastasis(SBM).Methods We collected multi-parametric magnetic resonance imaging(MRI)data from 61 patients with HGG and 60 with SBM,and delineated regions of interest(ROI)on T1WI,T2WI,T2-weighted fluid attenuated inversion recovery(T2_FLAIR)and post-contrast enhancement T1WI(CE_T1WI)images.The radiomics features were extracted from each sequence using Pyradiomics and fused using a multimodal feature fusion classification model based on distance matching and discriminative representation learning to obtain a classification model.The discriminative performance of the classification model for differentiating HGG from SBM was evaluated using five-fold cross-validation with metrics of specificity,sensitivity,accuracy,and the area under the ROC curve(AUC)and quantitatively compared with other feature fusion models.Visual experiments were conducted to examine the fused features obtained by the proposed model to validate its feasibility and effectiveness.Results The five-fold cross-validation results showed that the proposed multimodal feature fusion classification model had a specificity of 0.871,a sensitivity of 0.817,an accuracy of 0.843,and an AUC of 0.930 for distinguishing HGG from SBM.This feature fusion method exhibited excellent discriminative performance in the visual experiments.Conclusion The proposed multimodal feature fusion classification model has an excellent ability for differentiating HGG from SBM with significant advantages over other feature fusion classification models in discrimination and classification tasks between HGG and SBM.

Objective To evaluate the performance of magnetic resonance imaging(MRI)multi-sequence feature imputation and fusion mutual model based on sequence deletion in differentiating high-grade glioma(HGG)from low-grade glioma(LGG).Methods We retrospectively collected multi-sequence MR images from 305 glioma patients,including 189 HGG patients and 116 LGG patients.The region of interest(ROI)of T1-weighted images(T1WI),T2-weighted images(T2WI),T2 fluid attenuated inversion recovery(T2_FLAIR)and post-contrast enhancement T1WI(CE_T1WI)were delineated to extract the radiomics features.A mutual-aid model of MRI multi-sequence feature imputation and fusion based on sequence deletion was used for imputation and fusion of the feature matrix with missing data.The discriminative ability of the model was evaluated using 5-fold cross-validation method and by assessing the accuracy,balanced accuracy,area under the ROC curve(AUC),specificity,and sensitivity.The proposed model was quantitatively compared with other non-holonomic multimodal classification models for discriminating HGG and LGG.Class separability experiments were performed on the latent features learned by the proposed feature imputation and fusion methods to observe the classification effect of the samples in two-dimensional plane.Convergence experiments were used to verify the feasibility of the model.Results For differentiation of HGG from LGG with a missing rate of 10%,the proposed model achieved accuracy,balanced accuracy,AUC,specificity,and sensitivity of 0.777,0.768,0.826,0.754 and 0.780,respectively.The fused latent features showed excellent performance in the class separability experiment,and the algorithm could be iterated to convergence with superior classification performance over other methods at the missing rates of 30%and 50%.Conclusion The proposed model has excellent performance in classification task of HGG and LGG and outperforms other non-holonomic multimodal classification models,demonstrating its potential for efficient processing of non-holonomic multimodal data.

Objective To explore the performance of a new multimodal feature fusion classification model based on distance matching and discriminative representation learning for differentiating high-grade glioma(HGG)from solitary brain metastasis(SBM).Methods We collected multi-parametric magnetic resonance imaging(MRI)data from 61 patients with HGG and 60 with SBM,and delineated regions of interest(ROI)on T1WI,T2WI,T2-weighted fluid attenuated inversion recovery(T2_FLAIR)and post-contrast enhancement T1WI(CE_T1WI)images.The radiomics features were extracted from each sequence using Pyradiomics and fused using a multimodal feature fusion classification model based on distance matching and discriminative representation learning to obtain a classification model.The discriminative performance of the classification model for differentiating HGG from SBM was evaluated using five-fold cross-validation with metrics of specificity,sensitivity,accuracy,and the area under the ROC curve(AUC)and quantitatively compared with other feature fusion models.Visual experiments were conducted to examine the fused features obtained by the proposed model to validate its feasibility and effectiveness.Results The five-fold cross-validation results showed that the proposed multimodal feature fusion classification model had a specificity of 0.871,a sensitivity of 0.817,an accuracy of 0.843,and an AUC of 0.930 for distinguishing HGG from SBM.This feature fusion method exhibited excellent discriminative performance in the visual experiments.Conclusion The proposed multimodal feature fusion classification model has an excellent ability for differentiating HGG from SBM with significant advantages over other feature fusion classification models in discrimination and classification tasks between HGG and SBM.

Objective To evaluate the performance of magnetic resonance imaging(MRI)multi-sequence feature imputation and fusion mutual model based on sequence deletion in differentiating high-grade glioma(HGG)from low-grade glioma(LGG).Methods We retrospectively collected multi-sequence MR images from 305 glioma patients,including 189 HGG patients and 116 LGG patients.The region of interest(ROI)of T1-weighted images(T1WI),T2-weighted images(T2WI),T2 fluid attenuated inversion recovery(T2_FLAIR)and post-contrast enhancement T1WI(CE_T1WI)were delineated to extract the radiomics features.A mutual-aid model of MRI multi-sequence feature imputation and fusion based on sequence deletion was used for imputation and fusion of the feature matrix with missing data.The discriminative ability of the model was evaluated using 5-fold cross-validation method and by assessing the accuracy,balanced accuracy,area under the ROC curve(AUC),specificity,and sensitivity.The proposed model was quantitatively compared with other non-holonomic multimodal classification models for discriminating HGG and LGG.Class separability experiments were performed on the latent features learned by the proposed feature imputation and fusion methods to observe the classification effect of the samples in two-dimensional plane.Convergence experiments were used to verify the feasibility of the model.Results For differentiation of HGG from LGG with a missing rate of 10%,the proposed model achieved accuracy,balanced accuracy,AUC,specificity,and sensitivity of 0.777,0.768,0.826,0.754 and 0.780,respectively.The fused latent features showed excellent performance in the class separability experiment,and the algorithm could be iterated to convergence with superior classification performance over other methods at the missing rates of 30%and 50%.Conclusion The proposed model has excellent performance in classification task of HGG and LGG and outperforms other non-holonomic multimodal classification models,demonstrating its potential for efficient processing of non-holonomic multimodal data.