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Sensors for diagnosis of prostate cancer: Looking beyond the prostate specific antigen

date_range 2021
person
Author Dejous C.
description
Abstract Prostate cancer represents one of the most common forms of cancer affecting men across the globe. Due to late diagnosis of this disease, the mortality of this condition is very high. Conventional diagnostic methods like the direct rectal examination are uncomfortable and, in most cases, delayed, and further confirmation is required with biopsies and Gleason score. The most common biomarker approved by the FDA (United States Food and Drug Administration) is the prostate specific antigen (PSA) that is detected by conventional biochemical assays which require expensive reagents, is time-consuming and more often is only indicative and cannot be considered confirmative as it is susceptible to erroneous conclusions. The prostate health index employs quantification of PSA in its free and bound forms to enumerate the risk of prostate cancer and has found acceptance with clinicians though the methods used to determine these quantities are slow and require additional sensitivity. Search for novel biomarkers other than PSA has resulted in the identification of several promising candidates. However, their detection is still heavily dependent upon conventional biochemical assays that retain the challenges of being time-consuming, poorly sensitive and expensive. Development of specific sensor technologies integrating nanomaterials offers a viable alternative for rapid and sensitive determination of these non-PSA markers. This review summarizes the major advances in the development of sensors for diagnosis of prostate cancer using non-PSA markers. It also highlights some of the emerging paradigms in cancer diagnosis that may transform the diagnostic field in the context of prostate cancer. © 2020
article
DOI 10.1016/j.bios.2020.112790
language
Journal Biosensors and Bioelectronics
description
Source Scopus

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Citations Articles

Source from Semantic Scholar
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References Articles

Source from Semantic Scholar