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Design and development of an electroanalytical enzymatic sensor using anodized laser induced graphene for the management of Hirschsprung Disease
Roy S., Sain S., Mathur A., Pati A.B.,
Published in Elsevier Ltd
2022
Volume: 130
   
Abstract
Hirschsprung's disease (HD) is widely characterized by the absence of ganglion cells in the colon of infants and children resulting in a spectrum of diseases starting from constipation to intestinal obstruction. The disease can have a serious manifestation of Hirschsprung-associated enterocolitis which can lead to significant morbidity and even mortality. Presently the treatment includes surgical excision of the pathological segment of the colon. Identification of the segment requires histopathology and immunohistochemistry which is time-consuming and requires significant expertise of a pathologist. There is a gap in the management of the disease in the form of absence of suitable, sensitive, and efficient technologies to detect the pathological segment of the intestine, which could significantly reduce surgery duration in the operation theatre, as well as associated risks to patients. It, therefore, becomes a matter of extreme importance to develop a point-of-care platform for early and efficient identification of the occurrence of HD in neonates and older children during surgery. The present work reports on the development of an electrochemical enzymatic biosensor using anodized laser-induced graphene (aLIG) conjugated with acetylcholinesterase enzyme (AchE), for detection of HD as a function of acetylcholine – the key biomarker. The developed sensor was initially characterized using cyclic voltammetry (CV) and electrochemical impedance spectroscopy (EIS), while the sensor's analytical performance was assessed using CV. The sensor exhibited a limit of detection of 0.26 μM with a shelf life of 1 month, while remaining unperturbed in the presence of common interferants. The performance of the developed sensor was also examined in spiked serum samples and was observed to yield a high degree of linearity. © 2022 Elsevier B.V.
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Published in Elsevier Ltd
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