Nanomaterial-enhanced biosensors for polycystic ovarian syndrome diagnosis and pathophysiological insights

Abstract

Polycystic ovarian syndrome (PCOS) is an endocrine disease characterized by hormonal imbalances, metabolic inefficiency, and infertility problems. Furthermore, anti-Müllerian hormone (AMH), testosterone, and insulin are PCOS biomarkers that need to be detected accurately for early diagnosis and treatment. A narrative review discusses current improvements in nanomaterial-enhanced biosensors that detect biomarkers with high sensitivity and specificity. In addition, nanomaterials such as graphs and quantum dots have large surface areas and unique physicochemical properties that make them more effective biosphere. In addition, wearables and lab-on-chip platforms benefit from these features because they can detect in real time. In addition, artificial intelligence (AI) and machine learning (ML) are investigated to increase data interpretation and risk stratification using optical and electrochemical biosensors. Consequently, these biosensor technologies provide molecular insights into the underlying causes of PCOS, such as hyperandrogenism, insulin resistance, and chronic inflammation. By developing a short and portable biosensor, we can bridge the bridge between laboratory research and clinical practice and provide user-friendly diagnosis. Along with increasing clinical accuracy, the nanomaterial-based biosensor is considered a platform to learn more about PCOS pathology. Finally, their integration into clinical practice can contribute to the development of individual treatment methods in reproductive endocrinology and encourage research in the future.

Keywords: Artificial intelligence; Epidemiology; Optical biosensors; Pathophysiology; Polycystic ovarian syndrome.