Evidence map›Paper›PMID 34273478›Full record

ArticleMethods (San Diego, Calif.)2022

Absolute quantification of SARS-CoV-2 with Clarity Plus™ digital PCR.

Shawn Yi Han Tan, Sheng Yi Milton Kwek, Huiyu Low, Yan Ling Joy Pang

Abstract read
In one paragraph

Article in Methods (San Diego, Calif.), 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 9 papers, 1 of them a synthesis that pooled it.

0numbers the graph read from it
0cells of the map it votes in
9citing papers in PubMed, 1 pooled it
–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

9 citing papers in PubMed, 1 synthesis or guideline pooled it.

  1. Pooled it
  2. Review
  3. Article
  4. Article
  5. Article
  6. Article
  7. Review
  8. One-Step RT-qPCR for Viral RNA Detection Using Digital Analysis.Frontiers in bioengineering and biotechnology · 2022
    Article
  9. 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.

Shawn Yi Han TanSingapore Institute of Technology, 10 Dover Dr, Singapore 138683, Singapore.
Sheng Yi Milton KwekJN Medsys, 217 Henderson Road #02-08, Singapore 159555, Singapore.
Huiyu LowJN Medsys, 217 Henderson Road #02-08, Singapore 159555, Singapore.
Yan Ling Joy PangSingapore Institute of Technology, 10 Dover Dr, Singapore 138683, Singapore.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

In recent years, the usage of digital polymerase chain reaction (dPCR) for various clinical applications has increased exponentially. In this study, a dPCR assay optimized on the Clarity Plus™ dPCR system was evaluated for the absolute quantification of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), the causative agent of the global coronavirus disease 2019 (COVID-19) outbreak. The assay demonstrated good inter- and intra- assay precision, accuracy, as well as excellent linearity across a range of over 6 orders of magnitude for target gene quantification. In addition, a comparison of the assay on both dPCR and qPCR platforms revealed that dPCR exhibited a slightly higher sensitivity compared to its qPCR counterpart when quantifying SARS-CoV-2 at a lower concentration. Overall, the results showed that the dPCR assay is a reliable and effective approach for the absolute quantification of SARS-CoV-2 and can be a valuable molecular tool in clinical applications such as detecting low viral loads in patients as well as in wastewater surveillance of COVID-19.

Indexed as

COVID-19SARS-CoV-2HumansReal-Time Polymerase Chain ReactionRNA, ViralWastewaterWastewater-Based Epidemiological MonitoringRNA, ViralWastewaterAbsolute viral quantificationClarity Plus™ dPCRCOVID-19Sample partitioningSARS-CoV-2 RT-dPCR assay

Identifiers

PMID34273478
PMCPMC8339599

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.