Evidence map›Paper›PMID 39590010›Full record

ArticleBiosensors2024

Innovative Peptide-Based Plasmonic Optical Biosensor for the Determination of Cholesterol.

Ana Lia Bernardo, Anne Parra, Virginia Cebrián, Óscar Ahumada, Sergio Oddi, Enrico Dainese

Abstract read
In one paragraph

Article in Biosensors, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

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

0 citing papers in PubMed.

No citing paper in PubMed yet.

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

6 authors.

Ana Lia BernardoBiochemistry and Molecular Biology Unit, Department of Bioscience and Technology for Food, Agriculture and Environment, University of Teramo, Campus "Aurelio Saliceti" Via Renato Balzarini n. 1, 64100 Teramo, Italy.ORCID 0009-0000-9273-4981
Anne ParraMecwins S.A., Ronda de Poniente, 15, 2°D, Tres Cantos, 28760 Madrid, Spain.
Virginia CebriánMecwins S.A., Ronda de Poniente, 15, 2°D, Tres Cantos, 28760 Madrid, Spain.ORCID 0000-0002-3414-4796
Óscar AhumadaMecwins S.A., Ronda de Poniente, 15, 2°D, Tres Cantos, 28760 Madrid, Spain.
Sergio OddiDepartment of Veterinary Medicine, University of Teramo, Via Renato Balzarini n. 1, 64100 Teramo, Italy.ORCID 0000-0002-6217-698X
Enrico DaineseBiochemistry and Molecular Biology Unit, Department of Bioscience and Technology for Food, Agriculture and Environment, University of Teramo, Campus "Aurelio Saliceti" Via Renato Balzarini n. 1, 64100 Teramo, Italy.ORCID 0000-0001-7163-9344

Funding

European Union's Horizon 2020 research and innovation programme under the Marie Skłodowska-Curie 955643
6 · The paper itself

Abstract

Plasmonic-based biosensors have gained prominence as potent optical biosensing platforms in both scientific and medical research, attributable to their enhanced sensitivity and precision in detecting biomolecular and chemical interactions. However, the detection of low molecular weight analytes with high sensitivity and specificity remains a complex and unresolved issue, posing significant limitations for the advancement of clinical diagnostic tools and medical device technologies. Notably, abnormal cholesterol levels are a well-established indicator of various pathological conditions; yet, the quantitative detection of the free form of cholesterol is complicated by its small molecular size, pronounced hydrophobicity, and the necessity for mediator molecules to achieve efficient sensing. In the present study, a novel strategy for cholesterol quantification was developed, leveraging a plasmonic optical readout in conjunction with a highly specific cholesterol-binding peptide (C-pept) as a biorecognition element, anchored on a functionalized silica substrate. The resulting biosensor exhibited an exceptionally low detection limit of 21.95 µM and demonstrated a linear response in the 10-200 µM range. This peptide-integrated plasmonic sensor introduces a novel one-step competitive method for cholesterol quantification, positioning itself as a highly sensitive biosensing modality for implementation within the AVAC platform, which operates using reflective dark-field microscopy.

Indexed as

Biosensing TechniquesCholesterolPeptidesHumansLimit of DetectionSurface Plasmon ResonanceCholesterolPeptidesbiosensorcholesteroldark-field microscopylocalized surface plasmon resonancepeptide

Identifiers

PMID39590010
PMCPMC11592131

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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.