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Background Little is known about rectal Chlamydia trachomatis (CT) infection in outpatients attending sexually transmitted disease (STD) clinics in China. In this study, we aimed to explore the clinical and epidemiologic features of rectal CT infection in this population. Methods A cross-sectional study was conducted among patients attending STD clinics in Tianjin and Guangxi provinces of China from June 2018 to August 2020. Bivariate and multivariate logistic regression analysis were developed to explore the association of different risk factors for urogenital and rectal CT infection. Results The prevalence of urogenital and rectal CT was 11.2% (154/1374) and 4.9% (68/1377), respectively. The rectal CT prevalence among female and male patients was 7.8% (60/767) and 1.3% (8/610), respectively. The most common genotype in urogenital CT–positive samples was genotype E (29.9%), while the most common genotype among rectal CT–positive samples was genotype J (23.4%). More than 85% (52/60) of women infected with rectal CT were co-infected with urogenital CT. About 90.0% (36/40) of women shared similar genotypes between rectal and urogenital samples. Females and patients infected with urogenital CT were deemed to be at an increased risk for rectal CT infection. A high proportion of rectal CT infection had concurrent urogenital CT infection, especially in women, and most of the co-infections were shared among the same genotypes. Conclusions It would be prudent to encourage awareness and introduce detection tests and treatment strategies for rectal CT infection particularly in female patients visiting STD clinics in China. [ABSTRACT FROM AUTHOR]

Background: Previous studies have investigated rectal Mycoplasma genitalium (MG) in men who have sex with men (MSM), while little is known about the prevalence of rectal MG infection in individuals attending sexually transmitted disease (STD) clinics in China. We aimed to estimate the prevalence of rectal MG infection in this population and identified the potential risk factors for rectal MG infection. Methods: A cross-sectional study was conducted among individuals attending STD clinics located in China from June 2018 to August 2020. Univariate and multivariate logistic regression analyses were conducted to explore the association of different risk factors for rectal MG infection. Results: A total of 1,382 patients were included in the final analyses. A total of 30 of 1377 rectal swabs (2.2%) and 77 of 1374 urogenital samples (5.6%) were positive for MG. In Guangxi, 18 of 47 patients (38.3%) infected with urogenital MG and 5 of 19 patients (26.3%) infected with rectal MG received the recommended treatment. Factors found to be significantly associated with rectal MG infection included: male (adjusted odds ratio (AOR) 0.232, [95% CI: 0.072– 0.745]) compared to female, homosexual or bisexual (AOR 40.427, [95% CI: 3.880– 421.196]) compared to heterosexual, and those infected with urogenital MG (AOR 7.215, [95% CI: 2.898– 17.965]) compared to those who did not get infected with urogenital MG. Conclusion: Rectal MG infection should be thought of not only in MSM population but also in STD clinic patients, especially females who have urogenital MG infection. Appropriate strategy for rectal MG screening and treatment needs to be developed for these patients in China. [ABSTRACT FROM AUTHOR]

The dysbiosis of human microbiome has been proven to be associated with the development of many human diseases. Metagenome sequencing emerges as a powerful tool to investigate the effects of microbiome on diseases. Identification of human gut microbiome markers associated with abnormal phenotypes may facilitate feature selection for multiclass classification. Compared with binary classifiers, multiclass classification models deploy more complex discriminative patterns. Here, we developed a pipeline to address the challenging characterization of multilabel samples. In this study, a total of 300 biomarkers were selected from the microbiome of 806 Chinese individuals (383 controls, 170 with type 2 diabetes, 130 with rheumatoid arthritis, and 123 with liver cirrhosis), and then logistic regression prediction algorithm was applied to those markers as the model intrinsic features. The estimated model produced an F1 score of 0.9142, which was better than other popular classification methods, and an average receiver operating characteristic (ROC) of 0.9475 showed a significant correlation between these selected biomarkers from microbiome and corresponding phenotypes. The results from this study indicate that machine learning is a vital tool in data mining from microbiome in order to identify disease-related biomarkers, which may contribute to the application of microbiome-based precision medicine in the future. [ABSTRACT FROM AUTHOR]