Evidence map›Paper›PMID 28176860›Full record

ArticleScientific reports2017

Massively parallel digital high resolution melt for rapid and absolutely quantitative sequence profiling.

Daniel Ortiz Velez, Hannah Mack, Julietta Jupe, Sinead Hawker, Ninad Kulkarni, Behnam Hedayatnia, Yang Zhang, Shelley Lawrence, Stephanie I Fraley

Open access · goldAbstract read
PubMed Full text at PMC Publisher
In one paragraph

Article in Scientific reports, 2017. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 26 papers.

0numbers the graph read from it
0cells of the map it votes in
26citing papers in PubMed
2.2field-weighted citation impact, top 13% 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

26 citing papers in PubMed, 52 citations in OpenAlex.

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  14. Extreme Compressed Sensing of Poisson Rates from Multiple Measurements.IEEE transactions on signal processing : a publication of the IEEE Signal Processing Society · 2022
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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

9 authors at 1 institution in 1 country.

Daniel Ortiz VelezBioengineering Department, University of California San Diego, 92093, USA.
Hannah MackBioengineering Department, University of California San Diego, 92093, USA.
Julietta JupeBioengineering Department, University of California San Diego, 92093, USA.
Sinead HawkerBioengineering Department, University of California San Diego, 92093, USA.
Ninad KulkarniElectrical and Computer Engineering, University of California San Diego, 92093, USA.
Behnam HedayatniaElectrical and Computer Engineering, University of California San Diego, 92093, USA.
Yang ZhangBioengineering Department, University of California San Diego, 92093, USA.
Shelley LawrenceDepartment of Pediatrics, Division of Neonatal-Perinatal Medicine, University of California San Diego and Rady Children's Hospital of San Diego, 92093, USA.
Stephanie I FraleyBioengineering Department, University of California San Diego, 92093, USA.
University of California, San Diego · US

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

In clinical diagnostics and pathogen detection, profiling of complex samples for low-level genotypes represents a significant challenge. Advances in speed, sensitivity, and extent of multiplexing of molecular pathogen detection assays are needed to improve patient care. We report the development of an integrated platform enabling the identification of bacterial pathogen DNA sequences in complex samples in less than four hours. The system incorporates a microfluidic chip and instrumentation to accomplish universal PCR amplification, High Resolution Melting (HRM), and machine learning within 20,000 picoliter scale reactions, simultaneously. Clinically relevant concentrations of bacterial DNA molecules are separated by digitization across 20,000 reactions and amplified with universal primers targeting the bacterial 16S gene. Amplification is followed by HRM sequence fingerprinting in all reactions, simultaneously. The resulting bacteria-specific melt curves are identified by Support Vector Machine learning, and individual pathogen loads are quantified. The platform reduces reaction volumes by 99.995% and achieves a greater than 200-fold increase in dynamic range of detection compared to traditional PCR HRM approaches. Type I and II error rates are reduced by 99% and 100% respectively, compared to intercalating dye-based digital PCR (dPCR) methods. This technology could impact a number of quantitative profiling applications, especially infectious disease diagnostics.

Indexed as

DNA, BacterialGenomicsHumansListeria monocytogenesNucleic Acid DenaturationOligonucleotide Array Sequence AnalysisPolymerase Chain ReactionSequence Analysis, DNAStreptococcus pneumoniaeSupport Vector MachineDNA, Bacterial

Identifiers

PMID28176860
PMCPMC5296755
OpenAlexW2586709961

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.