Evidence map›Paper›PMID 38463295›Full record

ReviewACS omega2024

Biosensing Platforms for Cardiac Biomarker Detection.

Zeynep Gerdan, Yeşeren Saylan, Adil Denizli

Abstract readReview
In one paragraph

Review in ACS omega, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 13 papers.

0numbers the graph read from it
0cells of the map it votes in
13citing 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

13 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.

Zeynep GerdanDepartment of Biomedical Engineering, Istanbul Beykent University, Istanbul 34398, Turkey.
Yeşeren SaylanDepartment of Chemistry, Hacettepe University, Ankara 06800, Turkey.ORCID https://orcid.org/0000-0001-5173-1522
Adil DenizliDepartment of Chemistry, Hacettepe University, Ankara 06800, Turkey.ORCID https://orcid.org/0000-0002-6916-3356

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Myocardial infarction (MI) is a cardiovascular disease that occurs when there is an elevated demand for myocardial oxygen as a result of the rupture or erosion of atherosclerotic plaques. Globally, the mortality rates associated with MI are steadily on the rise. Traditional diagnostic biomarkers employed in clinical settings for MI diagnosis have various drawbacks, prompting researchers to investigate fast, precise, and highly sensitive biosensor platforms and technologies. Biosensors are analytical devices that combine biological elements with physicochemical transducers to detect and quantify specific compounds or analytes. These devices play a crucial role in various fields including healthcare, environmental monitoring, food safety, and biotechnology. Biosensors developed for the detection of cardiac biomarkers are typically electrochemical, mass, and optical biosensors. Nanomaterials have emerged as revolutionary components in the field of biosensing, offering unique properties that significantly enhance the sensitivity and specificity of the detection systems. This review provides a comprehensive overview of the advancements and applications of nanomaterial-based biosensing systems. Beginning with an exploration of the fundamental principles governing nanomaterials, we delve into their diverse properties, including but not limited to electrical, optical, magnetic, and thermal characteristics. The integration of these nanomaterials as transducers in biosensors has paved the way for unprecedented developments in analytical techniques. Moreover, the principles and types of biosensors and their applications in cardiovascular disease diagnosis are explained in detail. The current biosensors for cardiac biomarker detection are also discussed, with an elaboration of the pros and cons of existing platforms and concluding with future perspectives.

Identifiers

PMID38463295
PMCPMC10918812

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Registered trials

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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.