Tehran University of Medical Sciences

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Smart Early Diagnosis of Acute Myocardial Infarction: A Zif-Based Nanofluorescence Lateral Flow Immunoassay for Point-Of-Care Detection of Ctni Publisher



Mirzaeizadeh Z1 ; Sadrabadi EA1 ; Naseri N1 ; Golmohammadi H2 ; Omidfar K1, 3
Authors
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Authors Affiliations
  1. 1. Biosensor Research Center, Endocrinology and Metabolism Molecular–Cellular Sciences Institute, Tehran University of Medical Sciences, P.O. Box 14395/1179, Tehran, Iran
  2. 2. Nanosensor Bioplatforms Laboratory, Chemistry and Chemical Engineering Research Center of Iran, Tehran, 14335-186, Iran
  3. 3. Endocrinology and Metabolism Research Center, Endocrinology and Metabolism Research Institute, Tehran University of Medical Sciences, Tehran, Iran

Source: Materials Advances Published:2024


Abstract

Despite significant progress in the diagnosis of acute myocardial infarction (AMI), its morbidity and mortality rates are still high, indicating the necessity of developing easy-to-use diagnostic tests with timely and reliable point-of-care (POC) detection capability. Aiming to address this need, we introduce here for the first time the development of a fluorescence lateral flow immunoassay (LFIA) based on antibody labeled ZIF-8@BSA Au/Ag nanoclusters for the quantitative detection of cardiac troponin I (cTnI) as the major biomarker of AMI. A portable IoT-enabled optoelectronic reader was also fabricated to quantify the fluorescence signals of the developed LFIA, enabling easy, precise, and smart on-site quantitative analysis of cTnI. The developed smart LFIA demonstrated desirable assay performance for cTn1 in a linear concentration range of 10 pg mL-1 to 150 pg mL-1 with an appropriate sensitivity (with a detection limit of 9 pg mL-1) compared to the enzyme-linked immunosorbent assay (ELISA) and other reported assays. The fascinating results of our developed smart LFIA for easy, rapid (B10 min), and highly sensitive and specific detection of cTnI in serum and whole blood samples make it a very promising biosensor capable of being exploited for smart, reliable, and early diagnosis of AMI at the POC. © 2025 The Author(s).