Reproductive Sciences最新文献

The pathogenesis and progression of endometriosis may involve a complex combination of multiple factors, including chronic inflammation and oxidative stress. Hormonal therapy, the current standard for pharmacotherapy in endometriosis, causes various issues, such as restricting pregnancy during treatment and a high risk of recurrence after treatment discontinuation. This review investigates the use of lactoferrin (LF), a natural iron-binding glycoprotein, and outlines its mechanism of action, its potential as a non-hormonal therapeutic strategy, prospects for clinical application, and associated therapeutic issues. LF exerts anti-inflammatory, iron-chelating, antioxidant effects, and antiproliferative effects by suppressing signaling pathways and exhibits antifibrotic, antiangiogenic, and antibacterial properties. These are thought to be important physiological factors in endometriosis progression, for which LF exhibits a promising therapeutic candidate that could theoretically replace or complement hormone therapy. Observational studies have reported variations in serum and peritoneal fluid concentrations of LF and anti-LF antibodies in patients with endometriosis depending on the endometriosis stage, depicting LF as a potential therapeutic target. Furthermore, an in vitro study demonstrated that LF selectively induced cell cycle arrest in endometriotic stromal cells without affecting that of eutopic endometrial cells. Compared with other non-hormonal therapies, LF has an extremely lower risk of teratogenicity and fetal toxicity and could improve reproductive outcomes and perinatal prognosis, indicating its potential for continuous administration throughout various life stages. Further studies are needed to determine optimal administration routes and dosages for clinical applications.

Artificial Intelligence (AI) is revolutionizing reproductive medicine by enhancing fertility treatments, childbirth monitoring, and postnatal care. AI-driven technologies, including machine learning (ML), natural language processing (NLP), and robotic-assisted surgery, improve diagnostic accuracy, optimize treatment plans, and support clinical decision-making in assisted reproductive technology (ART). While AI has demonstrated significant potential, challenges such as ethical concerns, data privacy risks, algorithmic biases, and regulatory uncertainties remain critical barriers to widespread adoption in reproductive healthcare. This chapter explores the role, advancements, and challenges of AI in reproductive medicine, highlighting its impact on fertility, pregnancy, and postnatal care. A comprehensive analysis of AI applications in reproductive healthcare, including embryo selection, sperm and oocyte quality assessment, ovulation tracking, prenatal diagnostics, labor monitoring, and postpartum depression detection. AI aids in early detection of obstetric complications, optimizes neonatal care in intensive care units (NICUs), and enhances fertility treatments through predictive analytics. AI-driven virtual assistants support maternal health, while machine learning-based tools improve diagnostic precision and clinical decision-making. Addressing ethical and regulatory concerns is essential for equitable AI integration in reproductive medicine. Future advancements require interdisciplinary collaboration, standardized validation through randomized controlled trials, and responsible AI deployment to enhance patient outcomes while maintaining clinical expertise and ethical integrity.