Evidence map›Paper›PMID 35328154›Full record

ArticleDiagnostics (Basel, Switzerland)2022

Comparison of Rapid Nucleic Acid Extraction Methods for SARS-CoV-2 Detection by RT-qPCR.

Lívia Mara Silva, Lorena Rodrigues Riani, Marcelo Silva Silvério, Olavo Dos Santos Pereira-Júnior, Frederico Pittella

Open access · goldAbstract read
In one paragraph

Article in Diagnostics (Basel, Switzerland), 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 8 papers.

0numbers the graph read from it
0cells of the map it votes in
8citing papers in PubMed
0.9field-weighted citation impact, top 26% 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

8 citing papers in PubMed, 10 citations in OpenAlex.

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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

5 authors at 1 institution in 1 country.

Lívia Mara SilvaFaculdade de Farmácia, Universidade Federal de Juiz de Fora, Juiz de Fora 36036-900, Brazil.
Lorena Rodrigues RianiFaculdade de Farmácia, Universidade Federal de Juiz de Fora, Juiz de Fora 36036-900, Brazil.
Marcelo Silva SilvérioFaculdade de Farmácia, Universidade Federal de Juiz de Fora, Juiz de Fora 36036-900, Brazil.
Olavo Dos Santos Pereira-JúniorFaculdade de Farmácia, Universidade Federal de Juiz de Fora, Juiz de Fora 36036-900, Brazil.
Frederico PittellaFaculdade de Farmácia, Universidade Federal de Juiz de Fora, Juiz de Fora 36036-900, Brazil.ORCID 0000-0002-7847-9512
Universidade Federal de Juiz de Fora · BR

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Since 2020, humanity has been facing the COVID-19 pandemic, a respiratory disease caused by the SARS-CoV-2. The world's response to pandemic went through the development of diagnostics, vaccines and medicines. Regarding diagnostics, an enormous challenge was faced due to shortage of materials to collect and process the samples, and to perform reliable mass diagnosis by RT-qPCR. In particular, time-consuming and high cost of nucleic acid extraction procedures have hampered the diagnosis; moreover, several steps in the routine for the preparation of the material makes the extracted sample susceptible to contamination. Here two rapid nucleic acid extraction reagents were compared as extraction procedures for SARS-CoV-2 detection in clinical samples by singleplex and multiplex RT-qPCR analysis, using different transport media, samples with high and low viral load, and different PCR machines. As observed, rapid nucleic acid extraction procedures can be applied for reliable diagnosis using a TaqMan-based assay, over multiple platforms. Ultimately, prompt RNA extraction may reduce costs with reagents and plastics, the chances of contamination, and the overall time to diagnosis by RT-qPCR.

Indexed as

COVID-19rapid nucleic acid extractionRT-qPCRSARS-CoV-2

Identifiers

PMID35328154
PMCPMC8946922
OpenAlexW4214588450

What OpenQuestion holds

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LicenceCC BY
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.