Evidence map›Paper›PMID 35884340›Full record

ArticleBiosensors2022

A Point-of-Care Device for Fully Automated, Fast and Sensitive Protein Quantification via qPCR.

Francesca Romana Cavallo, Khalid Baig Mirza, Sara de Mateo, Luca Miglietta, Jesus Rodriguez-Manzano, Konstantin Nikolic, Christofer Toumazou

Abstract read
In one paragraph

Article in Biosensors, 2022. 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

7 authors.

Francesca Romana CavalloCentre for Bio-Inspired Technology, Department of Electrical and Electronic Engineering, Imperial College London, London SW7 2AZ, UK.
Khalid Baig MirzaDepartment of Biotechnology and Medical Engineering, National Institute of Technology, Rourkela 769008, India.ORCID 0000-0001-8754-6468
Sara de MateoCentre for Bio-Inspired Technology, Department of Electrical and Electronic Engineering, Imperial College London, London SW7 2AZ, UK.
Luca MigliettaCentre for Bio-Inspired Technology, Department of Electrical and Electronic Engineering, Imperial College London, London SW7 2AZ, UK.
Jesus Rodriguez-ManzanoDepartment of Infectious Disease, Imperial College London, London SW7 2AZ, UK.ORCID 0000-0002-2583-8366
Konstantin NikolicCentre for Bio-Inspired Technology, Department of Electrical and Electronic Engineering, Imperial College London, London SW7 2AZ, UK.ORCID 0000-0002-6551-2977
Christofer ToumazouCentre for Bio-Inspired Technology, Department of Electrical and Electronic Engineering, Imperial College London, London SW7 2AZ, UK.

Funding

Engineering and Physical Sciences Research Council Doctoral Training PartnershipEngineering and Physical Sciences Research Council EP/N002474/1European Research Council 319818European Research Council 825796
6 · The paper itself

Abstract

This paper presents a fully automated point-of-care device for protein quantification using short-DNA aptamers, where no manual sample preparation is needed. The device is based on our novel aptamer-based methodology combined with real-time polymerase chain reaction (qPCR), which we employ for very sensitive protein quantification. DNA amplification through qPCR, sensing and real-time data processing are seamlessly integrated into a point-of-care device equipped with a disposable cartridge for automated sample preparation. The system's modular nature allows for easy assembly, adjustment and expansion towards a variety of biomarkers for applications in disease diagnostics and personalised medicine. Alongside the device description, we also present a new algorithm, which we named PeakFluo, to perform automated and real-time quantification of proteins. PeakFluo achieves better linearity than proprietary software from a commercially available qPCR machine, and it allows for early detection of the amplification signal. Additionally, we propose an alternative way to use the proposed device beyond the quantitative reading, which can provide clinically relevant advice. We demonstrate how a convolutional neural network algorithm trained on qPCR images can classify samples into high/low concentration classes. This method can help classify obese patients from their leptin values to optimise weight loss therapies in clinical settings.

Indexed as

Aptamers, NucleotidePoint-of-Care SystemsHumansNucleic Acid Amplification TechniquesReal-Time Polymerase Chain ReactionSoftwareAptamers, NucleotidealgorithmdiagnosticsDNA aptamersnoise minimisationpoint-of-careprotein quantificationqPCR

Identifiers

PMID35884340
PMCPMC9313270

What OpenQuestion holds

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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.