Evidence map›Paper›PMID 35985558›Full record

ArticleJournal of immunological methods2022

Digital RT-PCR Chip method for detection of SARS-CoV-2 virus.

Laura Dioni, Annarosa Orlandi, Sara Uceda Renteria, Chiara Favero, Giulia Solazzo, Massimo Oggioni, Valentina Bollati

Abstract read
In one paragraph

Article in Journal of immunological methods, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 3 papers.

0numbers the graph read from it
0cells of the map it votes in
3citing 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

3 citing papers in PubMed.

  1. Review
  2. Article
  3. 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

7 authors.

Laura DioniEPIGET Lab, Department of Clinical Sciences and Community Health, Università degli Studi di Milano, Milan, Italy. Electronic address: laura.dioni@unimi.it.
Annarosa OrlandiVirology Unit, Fondazione IRCCS Ca' Granda Ospedale Maggiore Policlinico, Milan, Italy.
Sara Uceda RenteriaVirology Unit, Fondazione IRCCS Ca' Granda Ospedale Maggiore Policlinico, Milan, Italy.
Chiara FaveroEPIGET Lab, Department of Clinical Sciences and Community Health, Università degli Studi di Milano, Milan, Italy.
Giulia SolazzoEPIGET Lab, Department of Clinical Sciences and Community Health, Università degli Studi di Milano, Milan, Italy.
Massimo OggioniVirology Unit, Fondazione IRCCS Ca' Granda Ospedale Maggiore Policlinico, Milan, Italy.
Valentina BollatiEPIGET Lab, Department of Clinical Sciences and Community Health, Università degli Studi di Milano, Milan, Italy; Occupational Health Unit, Fondazione IRCCS Ca' Granda Ospedale Maggiore Policlinico, Milan, Italy.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

The "gold standard" method for detection of SARS-CoV-2 is the real time reverse transcription-polymerase chain reaction, but due to pre-analytical and technical limitations, biological samples with low viral load are not sometimes detected. For this purpose a digital RT-PCR method on-chip was developed for detection of the SARS-CoV-2 virus, using two TaqMan™ Assays for quantification of the N Protein (Nucleocapsid) and the S Protein (Spike), and the QuantStudio™ 3D Digital PCR instrument. The method was applied to assess the nasopharyngeal swabs of asymptomatic subjects recruited in the UNICORN Study. The digital RT-PCR method is characterized by a higher sensitivity than the RT-qPCR method, even if performed with the same TaqMan™, and could be a promising tool for SARS-CoV-2 viral load quantification.

Indexed as

COVID-19SARS-CoV-2COVID-19 TestingHumansReal-Time Polymerase Chain ReactionReverse Transcriptase Polymerase Chain ReactionRNA, ViralSensitivity and SpecificityRNA, ViralChipDigital RT-PCRSARS-CoV-2UNICORNViral load

Identifiers

PMID35985558
PMCPMC9383957

What OpenQuestion holds

Textmetadata
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.