Evidence map›Paper›PMID 32842632›Full record

ReviewSensors (Basel, Switzerland)2020

Electrochemical Biosensors Based on Nanomaterials for Early Detection of Alzheimer's Disease.

Celia Toyos-Rodríguez, Francisco Javier García-Alonso, Alfredo de la Escosura-Muñiz

Abstract readReview
In one paragraph

Review in Sensors (Basel, Switzerland), 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 18 papers.

0numbers the graph read from it
0cells of the map it votes in
18citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

18 citing papers in PubMed.

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4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

3 authors.

Celia Toyos-RodríguezNanoBioAnalysis Group-Department of Physical and Analytical Chemistry, University of Oviedo, Julián Clavería 8, 33006 Oviedo, Spain.ORCID 0000-0003-4941-2578
Francisco Javier García-AlonsoBiotechnology Institute of Asturias, University of Oviedo, Santiago Gascon Building, 33006 Oviedo, Spain.
Alfredo de la Escosura-MuñizNanoBioAnalysis Group-Department of Physical and Analytical Chemistry, University of Oviedo, Julián Clavería 8, 33006 Oviedo, Spain.ORCID 0000-0002-9600-0253

Funding

Gobierno del Principado de Asturias FC-GRUPIN-ID/2018/000166Ministerio de Ciencia e Innovación PRE2018-084853Ministerio de Ciencia e Innovación RyC-2016-20299Ministerio de Economía y Competitividad CTQ2017-86994-R
6 · The paper itself

Abstract

Alzheimer's disease (AD) is an untreatable neurodegenerative disease that initially manifests as difficulty to remember recent events and gradually progresses to cognitive impairment. The incidence of AD is growing yearly as life expectancy increases, thus early detection is essential to ensure a better quality of life for diagnosed patients. To reach that purpose, electrochemical biosensing has emerged as a cost-effective alternative to traditional diagnostic techniques, due to its high sensitivity and selectivity. Of special relevance is the incorporation of nanomaterials in biosensors, as they contribute to enhance electron transfer while promoting the immobilization of biological recognition elements. Moreover, nanomaterials have also been employed as labels, due to their unique electroactive and electrocatalytic properties. The aim of this review is to add value in the advances achieved in the detection of AD biomarkers, the strategies followed for the incorporation of nanomaterials and its effect in biosensors performance.

Indexed as

Alzheimer DiseaseBiosensing TechniquesElectrochemical TechniquesNanostructuresHumansQuality of LifeAlzheimer’s diseasebiosensorselectroactivityelectrocatalysiselectrochemistryimmunosensorslabelsnanomaterialsnanoparticlesneurodegenerative diseases

Identifiers

PMID32842632
PMCPMC7506792

What OpenQuestion holds

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Registered trials

None linked

Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.