Purpose: To investigate the relationship between vaginal microbial community structure and premature ovarian insufficiency (POI). Methods: Twenty-eight women with POI and 12 healthy women were recruited at Shenzhen Maternity and Child Healthcare Hospital between August and September 2020. Blood samples were collected for glucose tests and detection of sex hormone levels and vaginal secretions were collected for microbial group determination. Vaginal microbial community profiles were analysed by 16S rRNA gene sequencing using the Illumina MiSeq system (Illumina Inc., San Diego, CA, USA). Results: Compared to the controls, the serum levels of follicle-stimulating hormone, luteinizing hormone, testosterone, and the follicle-stimulating hormone/luteinizing hormone ratio, significantly increased, and oestradiol and anti-Müllerian hormone levels significantly decreased in women with POI. Higher weighted UniFrac values were observed in women with POI than in healthy women. Bacteria in the genera Lactobacillus, Brevundimonas, and Odoribacter were more abundant in the microbiomes of healthy women, while the quantity of bacteria in the genus Streptococcus was significantly increased in the microbiomes of women with POI. Moreover, these differences in microbes in women with POI were closely related to follicle-stimulating hormone, luteinizing hormone, oestradiol, and anti-Müllerian hormone levels and to the follicle-stimulating hormone/luteinizing hormone ratio. Conclusions: Women with POI had altered vaginal microbial profiles compared to healthy controls. The alterations in their microbiomes were associated with serum hormone levels. These results will improve our understanding of the vaginal microbial community structure in women with POI. Trial registration: CHICTR, ChiCTR2000029576. Registered 3 August 2020 - Retrospectively registered, https://www.chictr.org.cn/showproj.aspx?proj=48844.

Introduction: This study aimed to explore the transcriptomic profile of premature ovarian insufficiency (POI) by investigating alterations in gene expression.Methods: A total of sixty-one women, comprising 31 individuals with POI in the POI group and 30 healthy women in the control group (HC group), aged between 24 and 40 years, were recruited for this study. The transcriptomic profiles of peripheral blood samples from all study subjects were analyzed using RNA-sequencing.Results: The results revealed 39 differentially expressed genes in individuals with POI compared to healthy controls, with 10 upregulated and 29 downregulated genes. Correlation analysis highlighted the relationship between the expression of SLC25A39, CNIH3, and PDZK1IP1 and hormone levels. Additionally, an effective classification model was developed using SLC25A39, CNIH3, PDZK1IP1, SHISA4, and LOC389834. Functional enrichment analysis demonstrated the involvement of these differentially expressed genes in the “haptoglobin-hemoglobin complex,” while KEGG pathway analysis indicated their participation in the “Proteoglycans in cancer” pathway.Conclusion: The identified genes could play a crucial role in characterizing the genetic foundation of POI, potentially serving as valuable biomarkers for enhancing disease classification accuracy.

Background: Coronary heart disease (CHD) is the leading cause of death worldwide, constituting a growing health and social burden. People with cardiometabolic disorders are more likely to develop CHD. Retinal image analysis is a novel and noninvasive method to assess microvascular function. We aim to investigate whether retinal images can be used for CHD risk estimation for people with cardiometabolic disorders. Methods: We have conducted a case–control study at Shenzhen Traditional Chinese Medicine Hospital, where 188 CHD patients and 128 controls with cardiometabolic disorders were recruited. Retinal images were captured within two weeks of admission. The retinal characteristics were estimated by the automatic retinal imaging analysis (ARIA) algorithm. Risk estimation models were established for CHD patients using machine learning approaches. We divided CHD patients into a diabetes group and a non-diabetes group for sensitivity analysis. A ten-fold cross-validation method was used to validate the results. Results: The sensitivity and specificity were 81.3% and 88.3%, respectively, with an accuracy of 85.4% for CHD risk estimation. The risk estimation model for CHD with diabetes performed better than the model for CHD without diabetes. Conclusions: The ARIA algorithm can be used as a risk assessment tool for CHD for people with cardiometabolic disorders.

Introduction Non-pharmacological treatments for postpartum depression have been investigated in various systematic reviews, and their efficacy has been evaluated. However, the quality of the evidence as a whole has not been quantified. The quality of this evidence may influence the choice of interventions and even cause misleading clinical decisions. This study aims to provide an objective presentation of the methodological bias and identify treatments supported by solid evidence. Methods and analysis For the purpose of conducting systematic reviews and meta-analyses, a comprehensive search of the relevant published literature will be conducted in English databases such as PubMed, Embase, Cochrane Library, PsycINFO and Scopus, as well as in four Chinese databases: the Chinese Biomedical Databases (CBM), Wan fang database, China National Knowledge Infrastructure and VIP Database (VIP). The time of publication will be limited from their inception to 31 May 2022. We will extract the following data from the included literature: title, first author, journal type of included literature, number and sample size, intervention/control measures, outcome indicators and main study outcomes. The Assessment of Multiple Systematic Reviews-2 will be used to measure the quality of the methods. In addition, we will use the Preferred Reporting Items for Systematic Reviews and Meta-Analyses statement to evaluate the quality of the reporting, as well as the Grading of Recommendations Assessment, Development and Evaluation to evaluate the quality of the evidence. PROSPERO registration number CRD42021285470.