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.

Customized Carbon Dots with Predictable Optical Properties Synthesized at Room Temperature Guided by Machine Learning

“…Machine learning algorithms , build a model based on sample data, known as “training data”, to make predictions or decisions without completely understanding the “black box”, which represents the mechanism and learning process during model training. These characteristics enable machine learning to be successfully applied in the fields of chemistry and biology, such as spectral prediction, − cancer biomarker detection, − biosensor advancement, − and chemical synthesis. − Despite this progress, the application of machine learning algorithms to guide CD synthesis is still limited. In our previous studies, we developed a deep convolution neural network (DCNN) model for predicting the optical properties of CDs .…”

Wearable Triboelectric Sensors with Self-Powered Energy: Multifunctional Laser-Engraved Electrets to Activate Satellite Communication for Life-Emergency Alert in Pandemics

“…For this purpose, the computational DFT ,, method using PBE/def2-SVP levels interfaced with MM approach was treated to evaluate divalent magnesium and calcium ions diffusing inside collagen and interacting with the single-electrode GO-Cu­(II). In fact, collagen is the predominant structural protein of the extracellular matrix found in various human connective tissues .…”

“…The simulation aimed to demonstrate the ion diffusion-directed bending of PTFE chains. The common approach for calculating the standard Gibbs free energy required for the energy changes due to the interacting forces between cations and PTFE was given as follows …”

“…In recent decades, multiple miniaturized electronics have been integrated for wearable sensors with the potential of revolutionizing modern technologies in the fifth-generation (5G) mobile network. − Wireless electronics hold a great capability for real-time data acquisition, processing, health monitoring, and signal transmission of emergency requests. , Advances in human–machine interfaces allow flexible hybrid materials to activate those communication electronics in several ways by scavenging energy from several sources such as electrochemical sensors using biofuel cells, solar energies, and triboelectric nanogenerator (TENG) via contact electrification. ,− Among these, TENG has been proven as a potential candidate for energy-harvesting technology to drive small electronic devices via electrostatic induction and contact electrification. − Relying on the simple structure of universal material, the obtained fabrics or yarns are easily weaved into TENGs with low-cost and high stability for fabricating textile-based wearable sensors . For instance, conductive carbon nanotube-based wearable sensor exhibited highly sensitive touch and gesture sensing performance for human–machine interaction. , Recently, laser-induced graphene composites showed an exceptional ability for converting wasted mechanical energy into electrical energy when contact with skin .…”

Wearable Eye Patch Biosensor for Noninvasive and Simultaneous Detection of Multiple Biomarkers in Human Tears

“…For instance, tear proteins have been widely reported as a new marker for early diagnosis of various diseases, including diabetic retinopathy, aniridia, and dry eyes. − Ocular tissues contain a high level of l -ascorbic acid (0.61 ± 0.59 mM in tears of a healthy person), which plays a role in antioxidant protection, wound healing, and keratitis. − Tear pH is a potential indicator for early diagnosis of rosacea and epidemic or herpetic keratoconjunctivitis if its value turns out to be above the normal range (6.0–7.6) of a healthy person. − Therefore, tears are very attractive to be used as medium for noninvasive monitoring physiological parameters. Wearable biosensing systems have been developed based on tear bio-fluid, most of which are for integrating electrochemical or colorimetric sensors into contact lenses for assaying of biomarkers. , Not until very recently has a noninvasive wearable tear biosensor been reported, via an online mounting fluidic device onto the eyeglasses nose bridge pad to allow direct collection of stimulated tears. , Nevertheless, a more comfortable and convenient wearable sensor is still in demand to accord with the application in everybody’s daily life.…”