Research Projects

Thyroid-Stimulating Hormone (TSH) is an essential biomarker for diagnosing thyroid disorders such as hypothyroidism and hyperthyroidism. The normal range of Thyroid-Stimulating Hormone (TSH) levels in adults typically falls between 0.4 to 4.0 milli-international units per liter (mIU/L). For individuals undergoing treatment for thyroid disorders, optimal TSH levels are often targeted to a narrower range, such as 0.5 to 2.5 mIU/L, to ensure effective management of the condition. Current detection methods are often time consuming, costly, and require complex laboratory procedures, limiting their suitability for real-time, point-of-care (POC) diagnostics. The biosensor integrates graphene oxide (GO) and gold nanoparticles to achieve highly sensitive TSH detection. GO offers a large surface area, high conductivity, and chemical stability, facilitating efficient biomolecule immobilization and electron transfer critical for electrochemical sensing. Gold nanoparticles enhance performance through their superior optical, electrical, and biocompatible properties. Their high surface-to-volume ratio ensures stable TSH antibody conjugation, improving sensitivity and precision. Their inertness ensures long-term reliability, making them ideal for continuous monitoring. Together, GO and gold nanoparticles increase the active surface for antibody immobilization and amplify electrical signals during TSH binding. Advanced functionalization preserves antibody bioactivity, enabling specific detection by tracking electrical changes like impedance or current. This ensures rapid and accurate TSH measurement. Unlike traditional methods, this electrochemical approach eliminates the need for extensive sample preparation and delivers results in real-time. Its compatibility with POC applications ensures accessibility in resource-limited settings, offering a scalable and efficient solution for TSH detection and thyroid disorder management. The integration of Artificial Intelligence (AI) further enhances the system’s capabilities. AI algorithms will process sensor data to reduce noise, improve accuracy, and identify patterns in hormonal fluctuations. Predictive models will forecast future TSH trends, enabling early diagnosis and proactive thyroid health management. A graphical user interface (GUI) will provide real-time visualizations, historical trend analysis, alerts for abnormal TSH levels, and options for data export, making the system accessible and user-friendly for healthcare professionals and patients alike. This project aims to create a portable, cost-effective, and non-invasive diagnostic tool that revolutionizes TSH monitoring. By combining advanced nanotechnology, biosensing, and AI, this biosensor has the potential to transform thyroid disorder management, improving patient outcomes through timely and precise diagnosis and monitoring.