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image/svg+xml Jakob Voss, based on art designer at PLoS, modified by Wikipedia users Nina and Beao Closed Access logo, derived from PLoS Open Access logo. This version with transparent background. http://commons.wikimedia.org/wiki/File:Closed_Access_logo_transparent.svg Jakob Voss, based on art designer at PLoS, modified by Wikipedia users Nina and Beao
ZENODO
Book . 2026
License: CC BY
Data sources: ZENODO
ZENODO
Book . 2026
License: CC BY
Data sources: Datacite
ZENODO
Book . 2026
License: CC BY
Data sources: Datacite
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Targeting Diabetes Through Precision Medicine and Drug Delivery Technologies

Authors: Dr T. Deborah Paripuranam; Dr. Apeksha Ghatge; Dr. Jagdish Bedekar; Dr. Dishaben P. Senjaliya; Dr. Farah Deeba;

Targeting Diabetes Through Precision Medicine and Drug Delivery Technologies

Abstract

Diabetes has emerged as one of the most complex and rapidly growing global health challenges, demanding more innovative and precise strategies for its diagnosis, monitoring, and treatment. Traditional approaches that rely solely on clinical symptoms and biochemical tests are no longer sufficient to address the deep biological diversity among individuals living with this disease. This book, Targeting Diabetes Through Precision Medicine and Drug Delivery Technologies, has been developed to explore this modern shift toward patient-specific, technologically driven, and molecularly informed diabetes care. Over the past decade, remarkable progress in genomics, biomarkers, multi-omics research, molecular diagnostics, and drug delivery technologies has transformed our understanding of diabetes as a heterogeneous disorder rather than a single disease. These advancements have revealed the crucial roles of genetics, epigenetics, lifestyle factors, and environmental triggers in shaping disease onset and progression. The chapters in this book highlight how these scientific insights are revolutionizing early detection, risk prediction, and personalized therapeutic interventions for both Type 1 and Type 2 Diabetes. Equally transformative has been the integration of digital health technologies such as wearable biosensors, continuous glucose monitoring, artificial intelligence, and point-of-care diagnostics. These innovations enable real-time data collection, predictive analytics, and individualized decision-making, empowering patients to actively participate in managing their condition. By combining molecular science with AI-powered tools, the future of diabetes management is shifting from reactive treatment to proactive, personalized, and preventive care. This book also emphasizes the importance of advanced drug delivery systems ranging from nanocarriers and microneedle technologies to smart insulin pumps which are redefining how therapies can be targeted, released, and optimized within the body. These emerging technologies offer promising avenues for improving drug absorption, reducing adverse effects, and enhancing patient adherence ultimately improving health outcomes. Overall, this work aims to provide students, researchers, clinicians, and healthcare innovators with a comprehensive understanding of how precision medicine is reshaping diabetes care. By integrating cutting-edge scientific evidence with practical insights, the book aspires to contribute to a future where diabetes management is more accurate, effective, and personalized than ever before. Thank You Dr. T. Deborah Paripuranam Dr. Apeksha Ghatge Dr. Jagdish Bedekar Dr. Dishaben P. Senjaliya Dr. Farah Deeba

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selected citations
These citations are derived from selected sources.
This is an alternative to the "Influence" indicator, which also reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
BIP!Citations provided by BIP!
popularity
This indicator reflects the "current" impact/attention (the "hype") of an article in the research community at large, based on the underlying citation network.
BIP!Popularity provided by BIP!
influence
This indicator reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
BIP!Influence provided by BIP!
impulse
This indicator reflects the initial momentum of an article directly after its publication, based on the underlying citation network.
BIP!Impulse provided by BIP!
0
Average
Average
Average
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