Metabolic differences in women with premature ovarian insufficiency: a systematic review and meta-analysis.

OBJECTIVE: This review aimed to investigate the metabolic profile of women with premature ovarian insufficiency (POI) compared relative to women with normal ovarian functioning. METHODS: A systematic search of PubMed, EMBASE, and the Web of Science for observational studies published up until the 6th of July 2021 that compared the metabolic profile of POI women with a healthy control group were assessed. Mean differences (MD) and 95% confidence interval (CI) were pooled using the fixed or random effect models. RESULTS: A total of 21 studies involving 1573 women with POI and 1762 control women were included. POI patients presented significantly higher waist circumference, total cholesterol, low-density lipoprotein, high-density lipoprotein, triglycerides, and fasting glucose. Additionally, POI patients had marginally higher insulin level. However, the differences in systolic, and diastolic blood pressure were non-significant relative to the control group. CONCLUSIONS: POI is associated with alterations in certain metabolic parameters compared to control women. This finding highlights the importance of early screening and the lifelong management of metabolic health for women with POI.

Establishment and Analysis of a Combined Diagnostic Model of Polycystic Ovary Syndrome with Random Forest and Artificial Neural Network.

Polycystic ovary syndrome (PCOS) is one of the most common metabolic and reproductive endocrinopathies. However, few studies have tried to develop a diagnostic model based on gene biomarkers. In this study, we applied a computational method by combining two machine learning algorithms, including random forest (RF) and artificial neural network (ANN), to identify gene biomarkers and construct diagnostic model. We collected gene expression data from Gene Expression Omnibus (GEO) database containing 76 PCOS samples and 57 normal samples; five datasets were utilized, including one dataset for screening differentially expressed genes (DEGs), two training datasets, and two validation datasets. Firstly, based on RF, 12 key genes in 264 DEGs were identified to be vital for classification of PCOS and normal samples. Moreover, the weights of these key genes were calculated using ANN with microarray and RNA-seq training dataset, respectively. Furthermore, the diagnostic models for two types of datasets were developed and named neuralPCOS. Finally, two validation datasets were used to test and compare the performance of neuralPCOS with other two set of marker genes by area under curve (AUC). Our model achieved an AUC of 0.7273 in microarray dataset, and 0.6488 in RNA-seq dataset. To conclude, we uncovered gene biomarkers and developed a novel diagnostic model of PCOS, which would be helpful for diagnosis.

Sweetness-induced activation of membrane dipole potential in STC-1 taste cells.

The biological functions of cell membranes strongly influence the binding and transport of molecular species. We developed STC-1 cell line stably expressing the sweet taste receptor (T1R2/T1R3), and explored the possible correlation between sweeteners and membrane dipole potential of STC-1 cells. In this study, sweetener-induced dipole potential activation was elucidated using a fluorescence-based measurement technique, by monitoring the voltage sensitive probe Di-8-ANEPPS using a dual wavelength ratiometric approach. It indicated that the presence of sweeteners resulted in cell membrane dipole potential change, and interaction of artificial sweeteners with taste cells resulted in a greater reduction in potential compared with natural sweeteners. Our work presents a newly developed approach using a fluorescence-based measurement technique to study sweetener-induced dipole potential activation of STC-1 cells. This new approach could be used as a complementary tool to study the function of sweet taste receptors or other GPCRs and helps to understand the basis sweetness mechanism.