A low-cost nanomaterial-based electrochemical immunosensor on paper for high-sensitivity early detection of pancreatic cancer

Prasad, K. Sudhakara; Cao, Xiyue; Gao, Ning; Jin, Qijie; Sanjay, Sharma T.; Henao-Pabon, Gilberto; Li, Xiujun

doi:10.1016/j.snb.2019.127516

Cited by 114 publications

(56 citation statements)

References 52 publications

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“…Electrochemical biosensors are one kind of the most popular detection methods for the detection of all kinds diseases, due to their advantages of short detection time, instrumental simplicity, low cost and portability, holding a great potential for POC diagnostic applications [85] , [86] , [87] , [88] , [89] . As a result, various electrochemical biosensors have been explored for the applications in the development of POC diagnostic platforms, including voltammetric/amperometric biosensors [90] , [91] , [92] , [93] , [94] , impedimetric biosensors [95] , [96] , potentiometric biosensors [97] , and field-effect transistor (FET) based biosensors [98] , [99] .…”

Section: Detection Methods For Poc Diagnostic Applicationsmentioning

confidence: 99%

Point-of-care diagnostics for infectious diseases: From methods to devices

et al. 2021

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Section: Detection Methods For Poc Diagnostic Applicationsmentioning

confidence: 99%

Point-of-care diagnostics for infectious diseases: From methods to devices

et al. 2021

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“…In the immunoassays, the biorecognition elements are frequently proteins (biomarkers) which will bind specifically to antibodies immobilized onto the sensing platform. 24 , 54 , 55 These devices can be applied to a large number of samples, as blood, serum, plasma, urine, and saliva. There is a large variety of materials onto which these biorecognition elements can be immobilized, including polystyrene in ELISA assays, magnetic beads in CLIA, and nanomaterials in electrochemical and electrical immunosensors.…”

Section: Sars-cov-2 Diagnosismentioning

confidence: 99%

On the Challenges for the Diagnosis of SARS-CoV-2 Based on a Review of Current Methodologies

et al. 2020

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Diagnosis of COVID-19 has been challenging owing to the need for mass testing and for combining distinct types of detection to cover the different stages of the infection. In this review, we have surveyed the most used methodologies for diagnosis of COVID-19, which can be basically categorized into genetic-material detection and immunoassays. Detection of genetic material with real-time polymerase chain reaction (RT-PCR) and similar techniques has been achieved with high accuracy, but these methods are expensive and require time-consuming protocols which are not widely available, especially in less developed countries. Immunoassays for detecting a few antibodies, on the other hand, have been used for rapid, less expensive tests, but their accuracy in diagnosing infected individuals has been limited. We have therefore discussed the strengths and limitations of all of these methodologies, particularly in light of the required combination of tests owing to the long incubation periods. We identified the bottlenecks that prevented mass testing in many countries, and proposed strategies for further action, which are mostly associated with materials science and chemistry. Of special relevance are the methodologies which can be integrated into point-of-care (POC) devices and the use of artificial intelligence that do not require products from a well-developed biotech industry.

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“…The recent microfluidic lab-on-a-chip technique provides a versatile platform for various bioanalyses particularly for POC analysis with the advantages of low reagent consumption, fast analysis, and high portability [25][26][27][28][29][30][31][32][33]. Paper-based microfluidic devices are low cost [2,34,35]; however, they have low performance in reagent control and handling, especially for ELISA that usually involves repeated washing steps. Because different chip substrates have different advantages and limitations, different paper/polymer hybrid microfluidic devices were developed to draw more benefits from two different substrates [36][37][38][39][40][41].…”

Section: Introductionmentioning

confidence: 99%