Abstract
Modern Internet-of-Things diagnostic approaches address a progressively expanding of the biomedical information that can be extracted from analytes, such as saliva, urine, sweat, or tears. In this work, a fiber-optic sensing system for detecting biomarkers in tears is proposed and experimentally validated. The system is based on a fiber-optic ball resonator, rapidly fabricated through a CO2 laser splicer, and biofunctionalized for the specific detection of the Lipocalin-1 (LCN1) protein. The sensor has a low detection limit (240 ag/mL) and a log-linear response, and it can detect LCN1 protein in a wide range of concentrations up to 10 ng/mL. A wearable eye-goggle device with a sensor built in that it can detect the dynamic protein change in artificial tears has been designed and proposed as an in situ detection system. The proposed fiber optic sensor is a highly effective sensing device for in-tear, IoT-oriented sensing platforms, with dynamic sensing features and a low cost per sensing unit, as LCN1 protein has been demonstrated to be a reliable biomarker for diabetic retinopathy (DR).