Biosensors for Monitoring Metabolites in Clinical Medicine

Abstract

Biosensors for small‐molecular‐weight metabolites are finding increasing use in medicine for rapid, reagentless point‐of‐care measurement and continuous in vivo monitoring. Though glucose is the most well‐established clinical biosensor application, others include urea and creatinine for assessing renal function; lactic acid for monitoring tissue oxygenation; uric acid for assessing nucleic acid metabolism; cholesterol, triglyceride, and ketones for monitoring lipid metabolism; and bilirubin for liver and blood disorders. Many metabolite biosensors are enzyme electrodes, based on immobilized oxidases, hydrolases, or other enzymes, with amperometric or potentiometric electrochemical reaction monitoring, but other bioreceptors such as computer‐designed proteins and other reaction detection methods such as fluorescence are in use.

How Does It Work? Case Studies

“…Various clinical situations require a simultaneous detection of glucose and some other species, e.g., the lactate, cholesterol, triglicerides, insulin or ketone bodies (Pickup 2007). For this purpose, dual enzyme sensors were developed on the basis of appropriate enzymes co-immobilized on various transducers, preferably of the needle-type and screen-printed platform (Palmisano et al 2000;Newman and Turner 2005).…”

Detection of Pathogens in Water Using Micro and Nano-Technology

“…The selectivity of biosensors is ensured by a so called "key-lock" or "host-guest" principle which occurs between the molecules of target analyte and the selective molecular recognition elements ("bioreceptors") immobilised into/onto the biosensor. This is in contrast to the reactive-biosensor based devices, because latter ones are called and can be considered as "probe-type" devices (Pickup, 2007) while the former ones always need a controlled incubation process. A rough classification method of biosensors [which type of sensors count to be part of the chemosensors according to the definitions of the IUPAC (Thévenot et al, 2001)] is shown in Figure 5.1, taken from our previous work (Sántha, 2009).…”