Evidence map›Paper›PMID 34462755›Full record

ArticlemedRxiv : the preprint server for health sciences2021

Multiplex Target-Redundant RT-LAMP for Robust Detection of SARS-CoV-2 Using Fluorescent Universal Displacement Probes.

Enos C Kline, Nuttada Panpradist, Ian T Hull, Qin Wang, Amy K Oreskovic, Peter D Han, Lea M Starita, Barry R Lutz

Open access · greenAbstract readPreprint
In one paragraph

Article in medRxiv : the preprint server for health sciences, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

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

0 citing papers in PubMed, 7 citations in OpenAlex.

No citing paper in PubMed yet.

4 · The record

Corrections and comments

5 · Who and what money

Authors and funding

8 authors at 1 institution in 1 country.

Enos C Kline
Nuttada PanpradistORCID 0000-0002-2733-4110
Qin Wang
Peter D Han
University of Washington · US

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

The increasing prevalence of variant lineages during the COVID-19 pandemic has the potential to disrupt molecular diagnostics due to mismatches between primers and variant templates. Point-of-care molecular diagnostics, which often lack the complete functionality of their high throughput laboratory counterparts, are particularly susceptible to this type of disruption, which can result in false negative results. To address this challenge, we have developed a robust Loop Mediated Isothermal Amplification assay with single tube multiplexed multi-target redundancy and an internal amplification control. A convenient and cost-effective target specific fluorescence detection system allows amplifications to be grouped by signal using adaptable probes for pooled reporting of SARS-COV-2 target amplifications or differentiation of the Internal Amplification Control. Over the course of the pandemic, primer coverage of viral lineages by the three redundant sub-assays has varied from assay to assay as they have diverged from the Wuhan-Hu-1 isolate sequence, but aggregate coverage has remained high for all variant sequences analyzed, with a minimum of 97.4% (Variant of Interest: Eta). In three instances (Delta, Gamma, Eta), a high frequency mismatch with one of the three sub-assays was observed, but overall coverage remained high due to multi-target redundancy. When challenged with extracted human samples the multiplexed assay showed 100% sensitivity for samples containing greater than 30 copies of viral RNA per reaction, and 100% specificity. These results are further evidence that conventional laboratory methodologies can be leveraged at the point-of-care for robust performance and diagnostic stability over time.

Identifiers

PMID34462755
PMCPMC8404892
OpenAlexW3193619084

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.