Evidence map›Paper›PMID 32407343›Full record

ArticlePloS one2020

Maximizing viral detection with SIV droplet digital PCR (ddPCR) assays.

Samuel Long, Brian Berkemeier

Open access · goldAbstract readEvaluation StudyValidation Study
In one paragraph

Article in PloS one, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 10 papers.

0numbers the graph read from it
0cells of the map it votes in
10citing papers in PubMed
1.5field-weighted citation impact, top 18% 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

10 citing papers in PubMed, 15 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

2 authors at 1 institution in 1 country.

Samuel LongAIDS and Cancer Virus Program, Frederick National Laboratory for Cancer Research, Frederick, Maryland, United States of America.ORCID 0000-0001-7559-3232
Brian BerkemeierAIDS and Cancer Virus Program, Frederick National Laboratory for Cancer Research, Frederick, Maryland, United States of America.
Frederick National Laboratory for Cancer Research · US

Funding

CCR NIH HHS HHSN261200800001CNCI NIH HHS HHSN261200800001E
6 · The paper itself

Abstract

Highly sensitive detection of HIV-1 nucleic acids is of critical importance for evaluating treatment interventions superimposed on combination antiretroviral therapy (cART) in HIV-1 infected individuals. SIV infection of rhesus macaques models many key aspects of human HIV-1 infection and plays a key role in evaluation of approaches for prevention, treatment and attempted eradication of HIV infection. Here we describe two droplet digital PCR (ddPCR) assays, a ddPCR DNA assay and an RT-ddPCR RNA assay for detecting simian immunodeficiency virus (SIV) on the RainDance platform. We demonstrate that RainDance ddPCR can tolerate significantly higher cell DNA input without inhibition on a per reaction basis (compared to both qPCR and Bio-Rad ddPCR), thus allowing a large quantity of sample to be analyzed in each reaction. In addition, the combination of a high processivity RT enzyme and RainDance ddPCR could overcome inhibition in severely inhibited (99.99% inhibition in qPCR quantification) viral RNA samples. These assays offer valuable tools for assessing low level viral production/replication and strategies for targeting residual virus in the setting of cART suppression of viral replication. The methodologies presented here can be adapted for a broad range of applications where highly sensitive nucleic acid detection is required.

Indexed as

AnimalsDNA, ViralHIV-1HIV InfectionsHumansMacaca mulattaPolymerase Chain ReactionReal-Time Polymerase Chain ReactionRNA, ViralSimian Acquired Immunodeficiency SyndromeSimian Immunodeficiency VirusViral LoadDNA, ViralRNA, Viral

Identifiers

PMID32407343
PMCPMC7224504
OpenAlexW3025615060

What OpenQuestion holds

Textmetadata
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.