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.

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.

Although geriatric diseases are complicated due to the coexistence of many diseases,they often have a common pathological basis and are closely related to the pathogenesis of deficiency and excess in traditional Chinese medicine.Exploring the characteristics of the pathogenesis of deficiency and excess diseases in the elderly is helpful to keep simplicity and restrain complexity,grasp the law of occurrence and development of diseases in the elderly as a whole,and give full play to the advantages of traditional Chinese medicine in the prevention and treatment of chronic diseases.Based on the fundamental characteristics of deficiency and excess in senile diseases,researcher Hu Jingqing further summarized that"essence deficiency,Yin deficiency,Yang deficiency,qi deficiency,blood deficiency"and"qi stagnation,phlegm dampness,blood stasis,fire and heat,and latent wind"are the most common pathogenesis in the occurrence and development of senile diseases.Among them,deficiency is the basic pathogenesis of senile diseases,especially deficiency of kidney essence.Excess disease is the key pathogenesis of the development and changes of senile diseases.Qi stagnation is often the initial step in the development of senile diseases.Phlegm dampness and blood stasis are the pathological products of"excess due to deficiency"and are also the main secondary pathogenesis of senile diseases.In clinical identification of senile diseases,attention should be paid to grasping the pathogenesis of deficiency and excess and its concurrent changes.

Objective To analyze the epidemiological characteristics of syphilis in Yangzhou City from 2013 to 2023, to understand the syphilis epidemic trends, and provide a scientific basis for formulating prevention and control strategies. Methods Syphilis case reports in Yangzhou City from 2013 to 2023 were collected through the infectious disease surveillance module of the Chinese Center for Disease Control and Prevention Information System. The annual average reported incidence rate and annual average growth rate were calculated. Descriptive epidemiological methods were employed to analyze the overview of syphilis and its temporal, geographical and demographic distribution characteristics. Linear trend chi-square tests were conducted to analyze the syphilis epidemic trends. Results A total of 19, 457 syphilis cases were reported in Yangzhou City from 2013 to 2023, with an annual average reported incidence rate of 39.17/100, 000. The overall incidence of syphilis showed an upward trend, with the reported incidence rate increasing from 37.26/100, 000 in 2013 to 43.27/100, 000 in 2023 (χ²= 50.528, P < 0.001), representing an annual average growth rate of 1.51%. The majority of reported cases were latent syphilis, accounting for 64.99% (12, 646/19, 457). The number of reported cases of primary, secondary and congenital syphilis revealed an overall decreased trend, while the number of reported cases of tertiary and latent syphilis increased trend. There was no significant seasonality or periodicity in the reported incidence rate of syphilis in Yangzhou City from 2013 to 2023. The top three districts with the highest annual average reported incidence rates were the Development District, Guangling District and Hanjiang District, with incidence rates of 50.93/100, 000, 50.89/100, 000 and 43.18/100, 000, respectively. There were 10, 290 male syphilis cases (52.89%) and 9, 167 female syphilis cases (47.11%). The top three occupations were farmers (8, 682 cases), homemakers and unemployed individuals (5, 379 cases) and retired personnel (2, 559 cases), accounting for 85.42% (16, 620/19, 457) of the reported cases. Conclusion The incidence of syphilis in Yangzhou City generally shows an upward trend, and the disease epidemic spectrum changes annually, with latent syphilis currently being the primary form. The prevention and control situation is severe. Therefore, syphilis prevention and control efforts should be further strengthened to block its transmission and prevalence.

Aim To clarify the anti-rheumatoid arthritis effect of Tibetan medicine Pulicaria insignis (P. insignis),sift out the active parts against rheumatoid arthritis,and investigate the mechanism. Methods Rat rheumatoid arthritis (CIA) model was established with bovine type II collagen and incomplete Freund's adjuvant. The effects of the total extract of P. insignis, macroporous resin eluted parts with different concentrations of ethanol and Tripterygium Glycosides (GTW) on the degree of foot swelling in CIA rats were observed,the levels of tumor necrosis factor (TNF-α), intd rheumaerleukin-1β (IL-1β) antoid factor (RF) in serum of rats were detected, the pathological changes of synovial tissues were investigated, and the effects on MAPK/p38/NF-κB, TLR4/NF-κB protein expressions were explored by Western blot. Results Compared with the model group, the total extract of P. insignis and the eluted part of macroporous resin 60% ethanol could significantly reduce the degree of joint swelling in CIA rats, effectively improve the pathological changes of rats synovium tissues, and significantly reduce the levels of rat tumor necrosis factor (TNF-α), interleukin-1β (IL-1β) and rheumatoid factor (RF) in serum inflammatory factors, and markedly decrease the expression of related inflammatory proteins (TLR4, NF-κB, Myd88, p-p38, p-IκBα, iNOS, etc) in synovial tissue. Conclusions (1) P. insignis can relieve the symptoms of joint inflammation in rats with rheumatoid arthritis, and the eluted part of macroporous resin 60% ethanol of P. insignis is the effective active part for anti-rheumatoid arthritis. (2) The total and partial extracts of P. insignis can relieve arthritis symptoms in CIA rats through inhibiting the MAPK/ p38/NF-κB and TLR4/NF-κB signaling pathways.