| http://purl.uniprot.org/citations/32538723 | http://www.w3.org/1999/02/22-rdf-syntax-ns#type | http://purl.uniprot.org/core/Journal_Citation |
| http://purl.uniprot.org/citations/32538723 | http://www.w3.org/2000/01/rdf-schema#comment | "Locating remedies for Alzheimer's disease (AD) has been majorly restricted by the inefficiency to establish a definitive detection model for early-stage diagnosis of pathological events. This current lapse in AD diagnosis also limits the therapeutic efficiency of the drugs, which might have been effective if given at the earlier stages of the disease. The indicated situation directs towards the burgeoned need for an effective biomarker technique that will help in early detection of AD and would be imminently useful to facilitate improved diagnosis and stimulate therapeutic trials. Till date, the major biomarkers, specifically associated with AD detection, may help in determining the early-stage AD diagnosis and identifying alterations in the cellular proteome, offering deeper insight into disease etiology. Currently existing multidisciplinary clinical diagnosis of AD is a very tedious, expensive procedure and requires highly trained and skilled professionals who are rarely available outside the specialty clinics. Mutations in amyloid precursor protein (APP) or Presenilin 1 and 2 (PSEN1 and PSEN2) are some biomarkers acting as critical checkpoints for AD diagnosis. However, the presence of some associated biomarkers in cerebrospinal fluid (CSF) such as total-Tau (tTau), phosphorylated-Tau (pTau) 181 and Amyloid-β (Aβ) 1-42 using structural or functional imaging techniques is considered for confirmatory diagnosis of AD. Furthermore, the molecular diagnosis of AD incorporates various sophisticated techniques including immuno-sensing, machine learning, nano conjugation-based detections, etc. In the current review description, we have summarized the various diagnostic approaches and their relevance in mitigating the long-standing urgency of targeted diagnostic tools for detection of AD."xsd:string |
| http://purl.uniprot.org/citations/32538723 | http://purl.org/dc/terms/identifier | "doi:10.2174/1389203721666200615173213"xsd:string |
| http://purl.uniprot.org/citations/32538723 | http://purl.uniprot.org/core/author | "Agarwal V."xsd:string |
| http://purl.uniprot.org/citations/32538723 | http://purl.uniprot.org/core/author | "Agarwal S."xsd:string |
| http://purl.uniprot.org/citations/32538723 | http://purl.uniprot.org/core/author | "Singh M."xsd:string |
| http://purl.uniprot.org/citations/32538723 | http://purl.uniprot.org/core/author | "Singh S.P."xsd:string |
| http://purl.uniprot.org/citations/32538723 | http://purl.uniprot.org/core/author | "Agarwal M."xsd:string |
| http://purl.uniprot.org/citations/32538723 | http://purl.uniprot.org/core/author | "Kaur H."xsd:string |
| http://purl.uniprot.org/citations/32538723 | http://purl.uniprot.org/core/author | "Mani S."xsd:string |
| http://purl.uniprot.org/citations/32538723 | http://purl.uniprot.org/core/author | "Dubey P.K."xsd:string |
| http://purl.uniprot.org/citations/32538723 | http://purl.uniprot.org/core/author | "Yadav D."xsd:string |
| http://purl.uniprot.org/citations/32538723 | http://purl.uniprot.org/core/author | "Rachana R"xsd:string |
| http://purl.uniprot.org/citations/32538723 | http://purl.uniprot.org/core/date | "2020"xsd:gYear |
| http://purl.uniprot.org/citations/32538723 | http://purl.uniprot.org/core/name | "Curr Protein Pept Sci"xsd:string |
| http://purl.uniprot.org/citations/32538723 | http://purl.uniprot.org/core/pages | "965-977"xsd:string |
| http://purl.uniprot.org/citations/32538723 | http://purl.uniprot.org/core/title | "Advent of Proteomic Tools for Diagnostic Biomarker Analysis in Alzheimer's Disease."xsd:string |
| http://purl.uniprot.org/citations/32538723 | http://purl.uniprot.org/core/volume | "21"xsd:string |
| http://purl.uniprot.org/citations/32538723 | http://www.w3.org/2004/02/skos/core#exactMatch | http://purl.uniprot.org/pubmed/32538723 |
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