Advancing Graphene Field-Effect Transistors for Multiplex Analysis: An Analytical Tool for the Diagnosis of Rare Autoimmune Diseases - GFETPlex4Rar

• Grant number: 23/09400-3
• Support Opportunities: Scholarships in Brazil - Post-Doctoral
• Start date: November 01, 2023
• End date: October 31, 2025
• Field of knowledge: Engineering - Electrical Engineering - Electrical, Magnetic and Electronic Circuits
• Principal Investigator: Cecilia de Carvalho Castro e Silva
• Grantee: Edwin Johan Ortiz Riaño
• Host Institution: Instituto Mackenzie de Pesquisas em Grafeno e Nanotecnologias. Universidade Presbiteriana Mackenzie (UPM). Instituto Presbiteriano Mackenzie. São Paulo , SP, Brazil

Abstract

With the COVID-19 pandemic, there has been an observed increasing incidence of rare autoimmune diseases. In this scenario, rare autoimmune diseases such as scleroderma, or systemic sclerosis (SSc), stand out. The diagnosis of this disease is based on clinical evaluation, accompanied by the detection of specific antibodies for it. However, conventional techniques such as immunohistochemistry and cell-based assays (CBA) are expensive, difficult to access, and time-consuming (taking several weeks). Based on the above, the central objective of this proposal is to explore the fabrication of graphene-based field-effect transistor (FET) microchips for the simultaneous (multiplex) detection of different biomarkers (antibodies) for rare autoimmune diseases like SSc (GFETPlex4Rar). The aim is to develop new portable analytical tools with ultra-high sensitivity capable of detecting these biomarkers at the early stage of the disease, providing early diagnosis and increasing the opportunities for successful clinical treatments. Furthermore, this proposal will prioritize the testing and validating GFETPlex4Rar using patient samples from the Evangelical Mackenzie University Hospital - Evangelical Mackenzie School of Paraná. Additionally, this proposal aims to develop portable devices for the electrical readout of GFETPlex4Rar and for the storage and transmission of patient data directly to the medical team. This will reduce the diagnosis time for these diseases and provide the medical team with rapid and accurate information about the patient's health and treatment response.