Evidence map›Paper›PMID 38637016›Full record

ArticleRNA (New York, N.Y.)2024

PACRAT: pathogen detection with aptamer-observed cascaded recombinase polymerase amplification-in vitro transcription.

Pavana Khan, Lauren M Aufdembrink, Katarzyna P Adamala, Aaron E Engelhart

Open access · bronzeAbstract read
In one paragraph

Article in RNA (New York, N.Y.), 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.

0numbers the graph read from it
0cells of the map it votes in
1citing papers in PubMed
–field-weighted citation impact, top 95% of its field
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

1 citing paper in PubMed, 0 citations in OpenAlex.

  1. Article
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 at 1 institution in 1 country.

Pavana KhanDepartment of Genetics, Cell Biology, and Development, University of Minnesota, Minneapolis, Minnesota 55455, USA.
Lauren M AufdembrinkDepartment of Genetics, Cell Biology, and Development, University of Minnesota, Minneapolis, Minnesota 55455, USA.
Katarzyna P AdamalaDepartment of Genetics, Cell Biology, and Development, University of Minnesota, Minneapolis, Minnesota 55455, USA.ORCID 0000-0003-1066-7207
Aaron E EngelhartDepartment of Genetics, Cell Biology, and Development, University of Minnesota, Minneapolis, Minnesota 55455, USA enge0213@umn.edu.ORCID 0000-0002-1849-7700
University of Minnesota · US

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

The SARS-CoV-2 pandemic underscored the need for early, rapid, and widespread pathogen detection tests that are readily accessible. Many existing rapid isothermal detection methods use the recombinase polymerase amplification (RPA), which exhibits polymerase chain reaction (PCR)-like sensitivity, specificity, and even higher speed. However, coupling RPA to other enzymatic reactions has proven difficult. For the first time, we demonstrate that with tuning of buffer conditions and optimization of reagent concentrations, RPA can be cascaded into an in vitro transcription reaction, enabling detection using fluorescent aptamers in a one-pot reaction. We show that this reaction, which we term PACRAT (pathogen detection with aptamer-observed cascaded recombinase polymerase amplification-in vitro transcription) can be used to detect SARS-CoV-2 RNA with single-copy detection limits,

Indexed as

Aptamers, NucleotideCOVID-19Escherichia coliNucleic Acid Amplification TechniquesRNA, ViralSARS-CoV-2COVID-19 Nucleic Acid TestingHumansLimit of DetectionRecombinasesSensitivity and SpecificityTranscription, GeneticAptamers, NucleotideRecombinasesRNA, Viralfluorescent aptamerisothermal amplificationpathogen detectionRPAT7 RNA polymerase

Identifiers

PMID38637016
PMCPMC11182012
OpenAlexW4394911917

What OpenQuestion holds

Textmetadata
LicenceCC BY-NC
Read underepoch 390

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.