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2000
Volume 33, Issue 1
  • ISSN: 0929-8673
  • E-ISSN: 1875-533X
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Abstract

Polycystic Ovary Syndrome (PCOS) is a complex endocrine disorder that affects millions of women worldwide and is characterized by ovarian dysfunction, hyperandrogenism, and metabolic abnormalities. The traditional diagnostic and therapeutic approaches often fail to address the multifaceted nature of PCOS. Recent advancements in next-generation sequencing (NGS), bioinformatics, and precision medicine have paved the way for innovative research and therapeutic strategies that promise to revolutionize PCOS management. This review focuses on exploring the genetic and molecular mechanisms of PCOS using innovative methodologies, such as genome-wide association studies (GWAS), transcriptomics, and computational approaches. Integrating big data analytics and machine learning algorithms enhances the predictive accuracy of PCOS diagnoses and treatment outcomes. In addition, the emergence of personalized medicine has enabled tailored therapeutic interventions based on individual genetic profiles and phenotypic expression. Furthermore, we explored the development of novel pharmacological agents and combinational therapies to enhance the understanding of PCOS pathophysiology. These approaches also focus on reducing inflammation, improving insulin sensitivity, and optimizing hormonal balance to achieve optimal health outcomes. The potential of digital health tools, including mobile applications and wearable technologies, to support self-monitoring and patient engagement in PCOS management is also highlighted. In conclusion, the integration of next-generation technologies and innovative research is necessary to transform the field of PCOS diagnosis and treatment, offering hope for more effective and individualized care. These underscore the importance of continued investment in advanced research methodologies and the adoption of personalized therapeutic strategies to address the complexities of PCOS.

© 2025 The Author(s). Published by Bentham Science Publishers.
This is an open access article published under CC BY 4.0 https://creativecommons.org/licenses/by/4.0/legalcode
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Next-generation Approaches in Targeting Polycystic Ovarian Syndrome: Innovative Strategies
Curr. Med. Chem. 33, 57 (2026); https://doi.org/10.2174/0109298673368951250404170052
/content/journals/cmc/10.2174/0109298673368951250404170052
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  • Article Type:     Review Article
Keyword(s): Bioinformatics; innovative research; machine learning; molecular docking; polycystic ovarian syndrome; therapeutic strategies

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