Evidence map›Paper›PMID 31789505›Full record

ArticleACS sensors2019

Calibration-Free Measurement of Phenylalanine Levels in the Blood Using an Electrochemical Aptamer-Based Sensor Suitable for Point-of-Care Applications.

Andrea Idili, Claudio Parolo, Gabriel Ortega, Kevin W Plaxco

Abstract read
In one paragraph

Article in ACS sensors, 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 40 papers.

0numbers the graph read from it
0cells of the map it votes in
40citing 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

40 citing papers in PubMed.

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  12. Electrochemical DNA-based sensors for measuring cell-generated forces.bioRxiv : the preprint server for biology · 2023
    Article
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  20. Real-Time,ACS sensors · 2022
    Review
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

4 authors.

Gabriel Ortega

Funding

Bio-electrochemical detectors for in vivo continuous monitoringR01EB022015 · NIBIB · UNIVERSITY OF CALIFORNIA SANTA BARBARA · PI KIPPIN, TOD EDWARD, PLAXCO, KEVIN W · 2017 to 2025
$4.6M
NIBIB NIH HHS R01 EB022015
6 · The paper itself

Abstract

By analogy to the revolution the "home glucose monitor" created in the treatment of diabetes, the availability of a modular, "platform" technology able to measure nearly any metabolite, biomarker, or drug "at-home" in unprocessed, finger-prick volumes of whole blood could revolutionize the monitoring and treatment of disease. Thus motivated, we have adapted here the electrochemical aptamer-based sensing platform to the problem of rapidly and conveniently measuring the level of phenylalanine in the blood, an ability that would aid the monitoring and management of phenylketonuria (PKU). To achieve this, we exploited a previously reported DNA aptamer that recognizes phenylalanine in complex with a rhodium-based "receptor" that improves affinity. We re-engineered this to undergo a large-scale, binding-induced conformational change before modifying it with a methylene blue redox reporter and attaching it to a gold electrode that supports the appropriate electrochemical interrogation. The resultant sensor achieves a useful dynamic range of 90 nM to 7 μM. When challenged with finger-prick-scale sample volumes of the whole blood (diluted 1000-fold to match the sensor's dynamic range), the device achieves the accurate (±20%), calibration-free measurement of blood phenylalanine levels in minutes.

Indexed as

Aptamers, NucleotideBase SequenceCoordination ComplexesElectrochemical TechniquesElectrodesGoldHumansMethylene BluePhenylalaninePoint-of-Care TestingReceptors, ArtificialRhodiumAptamers, NucleotideCoordination ComplexesGoldMethylene BluePhenylalanineReceptors, ArtificialRhodiumamino acidaptasensorE-AB sensorsmetabolitepersonalized medicinetherapeutic drug monitoring

Identifiers

PMID31789505
PMCPMC8097980

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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.