Lab-on-Eyeglasses to Monitor Kidneys and Strengthen Vulnerable Populations in Pandemics: Machine Learning in Predicting Serum Creatinine Using Tear Creatinine.

Abstract

The serum creatinine level is commonly recognized as a measure of glomerular filtration rate (GFR) and is defined as an indicator of overall renal health. A typical procedure in determining kidney performance is venipuncture to obtain serum creatinine in the blood, which requires a skilled technician to perform on a laboratory basis and multiple clinical steps to acquire a meaningful result. Recently, wearable sensors have undergone immense development, especially for noninvasive health monitoring without a need for a blood sample. This article addresses a fiber-based sensing device selective for tear creatinine, which was fabricated using a copper-containing benzenedicarboxylate (BDC) metal-organic framework (MOF) bound with graphene oxide-Cu(II) and hybridized with Cu2O nanoparticles (NPs). Density functional theory (DFT) was employed to study the binding energies of creatinine toward the ternary hybrid materials that irreversibly occurred at pendant copper ions attached with the BDC segments. Electrochemical impedance spectroscopy (EIS) was utilized to probe the unique charge-transfer resistances of the derived sensing materials. The single-use modified sensor achieved 95.1% selectivity efficiency toward the determination of tear creatinine contents from 1.6 to 2400 μM of 10 repeated measurements in the presence of interfering species of dopamine, urea, and uric acid. The machine learning with the supervised training estimated 83.3% algorithm accuracy to distinguish among low, moderate, and high normal serum creatinine by evaluating tear creatinine. With only one step of collecting tears, this lab-on-eyeglasses with disposable hybrid textile electrodes selective for tear creatinine may be greatly beneficial for point-of-care (POC) kidney monitoring for vulnerable populations remotely, especially during pandemics.