Evidence map›Paper›PMID 32295887›Full record

ArticleJournal of clinical microbiology2020

Improving Quantitative Power in Digital PCR through Digital High-Resolution Melting.

April Aralar, Yixu Yuan, Kevin Chen, Yunshu Geng, Daniel Ortiz Velez, Mridu Sinha, Shelley M Lawrence, Stephanie I Fraley

Open access · greenAbstract read
In one paragraph

Article in Journal of clinical microbiology, 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
0.8field-weighted citation impact, top 29% 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, 21 citations in OpenAlex.

  1. Quantification ofWorld journal of hepatology · 2025
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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

8 authors at 1 institution in 1 country.

April AralarDepartment of Bioengineering, University of California, San Diego, La Jolla, California, USA.
Yixu YuanDepartment of Bioengineering, University of California, San Diego, La Jolla, California, USA.
Kevin ChenDepartment of Bioengineering, University of California, San Diego, La Jolla, California, USA.
Yunshu GengDepartment of Bioengineering, University of California, San Diego, La Jolla, California, USA.
Daniel Ortiz VelezDepartment of Bioengineering, University of California, San Diego, La Jolla, California, USA.
Mridu SinhaDepartment of Bioengineering, University of California, San Diego, La Jolla, California, USA.
Shelley M LawrenceDepartment of Pediatrics, Division of Neonatal-Perinatal Medicine, University of California, San Diego, La Jolla, California, USA.
Stephanie I FraleyDepartment of Bioengineering, University of California, San Diego, La Jolla, California, USA sifraley@ucsd.edu.
University of California, San Diego · US

Funding

Digital High Resolution Melt and Machine Learning for Rapid and Specific Diagnosis in Neonatal SepsisR01AI134982 · NIAID · UNIVERSITY OF CALIFORNIA, SAN DIEGO · PI FRALEY, STEPHANIE IRENE · 2018 to 2022
$2.3M
NIAID NIH HHS R01 AI134982
6 · The paper itself

Abstract

Applying digital PCR (dPCR) technology to challenging clinical and industrial detection tasks has become more prevalent because of its capability for absolute quantification and rare target detection. However, practices learned from quantitative PCR (qPCR) that promote assay robustness and wide-ranging utility are not readily applied in dPCR. These include internal amplification controls to account for false-negative reactions and amplicon high-resolution melt (HRM) analysis to distinguish true positives from false positives. Incorporation of internal amplification controls in dPCR is challenging because of the limited fluorescence channels available on most machines, and the application of HRM analysis is hindered by the separation of heating and imaging functions on most dPCR systems. We use a custom digital HRM platform to assess the utility of HRM-based approaches for mitigation of false positives and false negatives in dPCR. We show that detection of an exogenous internal control using dHRM analysis reduces the inclusion of false-negative partitions, changing the calculated DNA concentration up to 52%. The integration of dHRM analysis enables classification of partitions that would otherwise be considered ambiguous "rain," which accounts for up to ∼3% and ∼10% of partitions in intercalating dye and hydrolysis probe dPCR, respectively. We focused on developing an internal control method that would be compatible with broad-based microbial detection in dPCR-dHRM. Our approach can be applied to a number of DNA detection methods including microbial profiling and may advance the utility of dPCR in clinical applications where accurate quantification is imperative.

Indexed as

Diagnostic Tests, RoutineDNAHumansReal-Time Polymerase Chain ReactionDNAdPCRhigh-resolution meltinternal control

Identifiers

PMID32295887
PMCPMC7269394
OpenAlexW3016579731

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.