ArticleJournal of clinical microbiology2020
Improving Quantitative Power in Digital PCR through Digital High-Resolution Melting.
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.
What it found
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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.
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.
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Who cites it
10 citing papers in PubMed, 21 citations in OpenAlex.
- Quantification ofWorld journal of hepatology · 2025Article
- Digital Melting Curve Analysis for Multiplex Quantification of Nucleic Acids on Droplet Digital PCR.Biosensors · 2025Article
- Universal digital high-resolution melting for the detection of pulmonary mold infections.Journal of clinical microbiology · 2024Article
- Machine learning based DNA melt curve profiling enables automated novel genotype detection.BMC bioinformatics · 2024Article
- Universal Digital High-Resolution Melt Analysis for the Diagnosis of Bacteremia.The Journal of molecular diagnostics : JMD · 2024Article
- Decoding human cytomegalovirus for the development of innovative diagnostics to detect congenital infection.Pediatric research · 2024Review
- Universal Digital High Resolution Melt for the detection of pulmonary mold infections.bioRxiv : the preprint server for biology · 2023Article
- Melt-Encoded-Tags for Expanded Optical Readout in Digital PCR (METEOR-dPCR) Enables Highly Multiplexed Quantitative Gene Panel Profiling.Advanced science (Weinheim, Baden-Wurttemberg, Germany) · 2023Article
- Streamlined and quantitative detection of chimerism using digital PCR.Scientific reports · 2022Article
- An update on current and novel molecular diagnostics for the diagnosis of invasive fungal infections.Expert review of molecular diagnosticsReview
Corrections and comments
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Authors and funding
8 authors at 1 institution in 1 country.
Funding
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.
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Registered trials
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.