Revolutionizing Diabetes Management: Nanostructured Biosensors for Real-time Monitoring

Year : 2024 | Volume :14 | Issue : 01 | Page : –
By

Seethaladevi

  1. Assistant Professor Department of ECE,Indira Institute of Technology & Sciences Andhra Pradesh India

Abstract

Chronic metabolic disease known as diabetes mellitus, which is characterised by dysregulated blood glucose levels, is becoming a major global health concern. The implementation of automated insulin administration systems and continuous glucose monitoring (CGM) has shown promise in the management of diabetes. Nanostructured biosensors, leveraging the unique properties of nanomaterials, offer enhanced sensitivity, selectivity, and biocompatibility, making them ideal candidates for real-time monitoring and personalized treatment.

This article delves into the design and application of nanostructured biosensors for diabetes management, covering both CGM and closed-loop insulin delivery systems. It explores various nanomaterials and nanostructured sensing platforms, including enzymatic and non-enzymatic glucose biosensors, as well as nanostructured platforms for insulin monitoring and controlled delivery. Strategies for improving biocompatibility, such as surface modifications and bioinspired approaches, are discussed, along with in vivo performance evaluation and regulatory considerations.

The article also addresses the integration of nanostructured biosensors into wearable devices, wireless communication technologies, and user-friendly interfaces, enabling continuous monitoring and real-time feedback. Additionally, it examines emerging trends and future directions, such as multifunctional biosensors, integration with artificial intelligence and machine learning, personalized diabetes management strategies, implantable and minimally invasive sensors, and the convergence with closed-loop insulin delivery systems.

This article emphasises the potential for revolutionising diabetes management, increasing glycemic control, lowering complications, and improving the quality of life for people with diabetes by utilising the capabilities of nanostructured biosensors and interdisciplinary collaboration.

Keywords: Nanostructured biosensors, continuous glucose monitoring, insulin delivery, nanomaterials, biocompatibility, wearable devices, artificial intelligence, machine learning,

[This article belongs to Journal of Nanoscience, NanoEngineering & Applications(jonsnea)]

How to cite this article: Seethaladevi. Revolutionizing Diabetes Management: Nanostructured Biosensors for Real-time Monitoring. Journal of Nanoscience, NanoEngineering & Applications. 2024; 14(01):-.
How to cite this URL: Seethaladevi. Revolutionizing Diabetes Management: Nanostructured Biosensors for Real-time Monitoring. Journal of Nanoscience, NanoEngineering & Applications. 2024; 14(01):-. Available from: https://journals.stmjournals.com/jonsnea/article=2024/view=152081

References

  1. Eswaran, U. (2024a). Revolutionizing medical imaging: Exploring cutting-edge technologies. In Enhancing Medical Imaging with Emerging Technologies (pp. 210–220).
  2. Garg, Satish K., et al. “Correlation of fingerstick blood glucose measurements with GlucoWatch biographer glucose results in young subjects with type 1 diabetes.” Diabetes care10 (1999): 1708-1714.
  3. Eswaran, U. (2024b). Fortifying cybersecurity in an interconnected telemedicine ecosystem. In Improving Security, Privacy, and Connectivity Among Telemedicine Platforms (pp. 30–60).
  4. Sun, Xiaoyan, et al. “Association between frequency of self-monitoring of blood glucose and glycemic control in patients with type 2 diabetes.” Diabetes Research and Clinical Practice209 (2024): 111027.
  5. Ghosh, Sanjay, and Vipasha Sharma. “Tracking of Disease—A Review of the State of the Art of Technology for Next Generation Healthcare.” Deep Learning in Internet of Things for Next Generation Healthcare(2024): 242-268.
  6. Eswaran, V., Eswaran, U., Eswaran, V., & Murali, K. (2024c). Revolutionizing healthcare: The application of image processing techniques. In Medical Robotics and AI-Assisted Diagnostics for a High-Tech Healthcare Industry (pp. 309–324).
  7. Eswaran, U. (2024d). Project-based learning: Fostering collaboration, creativity, and critical thinking. In Enhancing Education With Intelligent Systems and Data-Driven Instruction (pp. 23–43).
  8. Eichenlaub, Manuel, et al. “Comparator data characteristics and testing procedures for the clinical performance evaluation of continuous glucose monitoring systems.” Diabetes technology & therapeutics(2024).
  9. Eswaran, U., Eswaran, V., Murali, K., & Eswaran, V. (2024e). Healthcare smart sensors: Applications, trends, and future outlook. In Driving Smart Medical Diagnosis Through AI-Powered Technologies and Applications (pp. 24–48).
  10. Medanki, Sravani, et al. “Artificial intelligence powered glucose monitoring and controlling system: Pumping module.” World Journal of Experimental Medicine1 (2024).
  11. Eswaran, U., & Khang, A. (2024f). Augmented Reality (AR) and Virtual Reality (VR) Technologies in Surgical Operating Systems. In AI and IoT Technology and Applications for Smart Healthcare Systems (pp. 113–129).
  12. Uhl, S., Choure, A., Rouse, B., Loblack, A., & Reaven, P. (2024). Effectiveness of continuous glucose monitoring on metrics of glycemic control in type 2 diabetes mellitus: a systematic review and meta-analysis of randomized controlled trials. The Journal of Clinical Endocrinology & Metabolism, 109(4), 1119-1131.
  13. Eswaran, U., & Khang, A. (2024g). Artificial Intelligence (AI)-Aided Computer Vision (CV) in Healthcare System. In Computer Vision and AI-Integrated IoT Technologies in the Medical Ecosystem (pp. 125–137).
  14. Eswaran, U., Khang, A., & Eswaran, V. (2023a). Applying machine learning for medical image processing. In AI and IoT-Based Technologies for Precision Medicine (pp. 137–154).
  15. Golsanamlou, Z., Mahmoudpour, M., Soleymani, J., & Jouyban, A. (2023). Applications of advanced materials for non-enzymatic glucose monitoring: from invasive to the wearable device. Critical Reviews in Analytical Chemistry, 53(5), 1116-1131.
  16. Eswaran, U. (2023b). Integrating renewable energy sources into existing energy systems for achieving a low-carbon energy transition. In Sustainable Science and Intelligent Technologies for Societal Development (pp. 262–277).
  17. Ghorbanizamani, F., Moulahoum, H., Guler Celik, E., & Timur, S. (2023). Material Design in Implantable Biosensors toward Future Personalized Diagnostics and Treatments. Applied Sciences, 13(7), 4630.
  18. Eswaran, U., & Anand, S. (2023c). Horizontal Voting Ensemble Based Predictive Modeling System for Colon Cancer. Computer Systems Science and Engineering, 46(2), 1917–1928.
  19. Eswaran, U., Eswaran, V., & Sudharshan, V.B. (2013). Human like biosensor disease simulator, disease analyzer and drug delivery system. In 2013 IEEE Conference on Information and Communication Technologies, ICT 2013 (pp. 1033–1038).
  20. Shoaib, Ambreen, Ali Darraj, Mohammad Ehtisham Khan, Lubna Azmi, Abdulaziz Alalwan, Osamah Alamri, Mohammad Tabish, and Anwar Ulla Khan. “A nanotechnology-based approach to biosensor application in current diabetes management practices.” Nanomaterials13, no. 5 (2023): 867.
  21. Ushaa, S.M., Madhavilatha, M., & Rao, G.M. (2011). Design and analysis of nanowire sensor array for prostate cancer detection. International Journal of Nano and Biomaterials, 3(3), 239–255.
  22. Wang, Ying, et al. “One-step laser synthesis platinum nanostructured 3D porous graphene: A flexible dual-functional electrochemical biosensor for glucose and pH detection in human perspiration.” Talanta257 (2023): 124362.
  23. Eswaran, U., Madhavilatha, M., Murthy, J., & Ganji, M.R. (2008). Expert system design for disease detection using pattern recognition techniques. In Proceedings of the 2008 International Conference on Artificial Intelligence, ICAI 2008 and Proceedings of the 2008 International Conference on Machine Learning; Models, Technologies and Applications (pp. 902–906).
  24. Lazaro, Antonio, et al. “Recent advances in batteryless NFC sensors for chemical sensing and biosensing.” Biosensors8 (2023): 775.
  25. Eswaran, U., Madhavilatha, M., & Ganji, M.R. (2006). A proposal for design of hybrid multi-functional implantable biochip using bio-intelligent expert system. In Proceedings of the 2006 International Conference on Artificial Intelligence, ICAI’06 (Vol. 2, pp. 426–432).
  26. Eswaran, U., Ganji, M.R., & Thakur, M.S. (2004). Microprocessor based biosensors for determination of toxins and pathogens in restricted areas of human intervention. In Proceedings of the International Conference on Artificial Intelligence, IC-AI’04 (Vol. 1, pp. 525-531).
Regular Issue Subscription Review Article
Volume 14
Issue 01
Received May 24, 2024
Accepted June 24, 2024
Published June 25, 2024
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