Review on Uric Acid Recognition by MOFs with a Future in Machine Learning

Abstract

Uric acid (UA) is produced from purine metabolism and serves as a prevalent biomarker for multiple diseases including cancer. Hyperuricemia or hypouricemia can cause multiple dysfunctions throughout the biological processes. Consequently, there is a pressing need for monitoring UA concentration in body fluid. While clinical methods are known, the availability of a point-of-care testing (PoCT) kit remains conspicuously absent. In the case of electrochemical recognition of UA, the oxidation potential of ascorbic acid closely aligns with that of UA and thus it hinders the detection process, which eventually may result in false positive signals. Several chemosensors are known in the field of supramolecular chemistry, and metal–organic frameworks (MOFs) are one of the best-performing contenders due to their robustness, stability, and versatile structures. In this review, we tried to unbox the up-to-date development of UA sensing by MOFs. We delve into the state of UA recognition by MOFs, exploring both electrochemical and fluorometric pathways and drawing comparisons with structurally similar probes like covalent organic frameworks (COFs) to understand/establish the advantages of MOFs specifically in UA sensing. In the absence of a PoCT kit, we have provided the conceptual outlook for designing a PoCT device termed a “Urimeter” via electrochemical operation. For the first time, we have proposed different methods of how UA sensing can be tied up with artificial intelligence and machine learning (AI-ML).

Boronic Acid-Linked Cell-Penetrating Peptide for Protein Delivery

“…Several researchers currently use AI-ML for multiple applications in chemosensing and/or protein-based research. − Other than investigating individually with varying additives or mere alternation of additive substitution, which leads to exhaustive experimentation, the AI-ML method could be a predictor similarly to suggest future probes (Figure S11). , …”

“… 35 − 37 Other than investigating individually with varying additives or mere alternation of additive substitution, which leads to exhaustive experimentation, the AI-ML method could be a predictor similarly to suggest future probes ( Figure S11 ). 38 , 39…”