{"paper_id":"2e922e28-0868-43e3-9640-fed6064bdc26","body_text":"Review of the Abstract: miRNAs in Endometriosis: Towards Precise Diagnosis and New Therapeutic Approaches\nAuthors/Creators\nDescription\n“Endometriosis is a condition marked by the abnormal growth of tissue similar to the endometrium outside the uterus, impacting approximately 10% of women of reproductive age and leading to infertility as well as severe menstrual pain. Diagnosing this condition remains a complex endeavor, as symptoms are frequently dismissed as normal. Although laparoscopy has traditionally been regarded as the definitive diagnostic method, specialized ultrasound and magnetic resonance imaging (MRI) have emerged as effective alternatives. Nonetheless, the identification of specific molecular biomarkers is crucial for enhancing early detection and making diagnosis more accessible.\nIn our research, we examined the expression levels of miRNAs in plasma samples from 22 women, employing a non-probabilistic convenience sampling strategy. The relative expression of miRNAs was quantified using RT-qPCR, and the data were analyzed through individual and cumulative ROC curves, logistic regression, and decision trees. Participants were categorized into two groups: those diagnosed with endometriosis (n=15) and a control group (n=7), with diagnoses verified through imaging or laparoscopy.\nOur findings indicate that the miRNA panel we studied demonstrates significant discriminatory ability, making it a potential adjunct to existing diagnostic techniques. Furthermore, this research paves the way for the development of targeted molecular therapies, which could revolutionize the clinical management of endometriosis and provide more effective treatment options in the future. Confirming its clinical relevance through larger-scale studies will be essential for validating its use in both diagnosis and treatment.”\nFiles\nFiles\n(21.0 kB)\n| Name | Size | Download all |\n|---|---|---|\n|\nmd5:223226c5f5c30ac67f8f5473afc9734f\n|\n21.0 kB | Download |\nAdditional details\nIdentifiers\nSoftware\n- Repository URL\n- https://makedonskaia.org/v\n- Programming language\n- HTML , Python , CSS\n- Development Status\n- Active","source_license":"CC0","license_restricted":false}