ArticleJournal of the American Chemical Society2019
Statistical Analysis of Nonuniform Volume Distributions for Droplet-Based Digital PCR Assays.
Article in Journal of the American Chemical Society, 2019. 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
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.
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.
Who cites it
10 citing papers in PubMed.
- Absolute quantification of synthetic DNA fragments spanning hundreds to thousands of base pairs using SEC coupled with ICP-MS.Analytical and bioanalytical chemistry · 2026Article
- Compartmentalized Suspension Array for the Isothermal, Digital, and Multiplex Detection of microRNAs.Journal of the American Chemical Society · 2025Article
- One-Tube Nested PCR Coupled with CRISPR-Cas12a for Ultrasensitive Nucleic Acid Testing.ACS omega · 2024Article
- Evaluation of multiple displacement amplification for metagenomic analysis of low biomass samples.ISME communications · 2024Article
- Highly sensitive digital detection of SARS-CoV-2 nucleocapsid protein through single-molecule counting.Analytical and bioanalytical chemistry · 2023Article
- Digital CRISPR Systems for the Next Generation of Nucleic Acid Quantification.Trends in analytical chemistry : TRAC · 2023Article
- Near-digital amplification in paper improves sensitivity and speed in biplexed reactions.Scientific reports · 2022Article
- Importance of sample input volume for accurate SARS-CoV-2 qPCR testing.Analytica chimica acta · 2022Article
- Deep-dLAMP: Deep Learning-Enabled Polydisperse Emulsion-Based Digital Loop-Mediated Isothermal Amplification.Advanced science (Weinheim, Baden-Wurttemberg, Germany) · 2022Article
- A critical review: Recent advances in "digital" biomolecule detection with single copy sensitivity.Biosensors & bioelectronics · 2021Review
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
5 authors.
Funding
Abstract
We present a method to determine the concentration of nucleic acids in a sample by partitioning it into droplets with a nonuniform volume distribution. This digital PCR method requires no special equipment for partitioning, unlike other methods that require nearly identical volumes. Droplets are generated by vortexing a sample in an immiscible oil to create an emulsion. PCR is performed, and droplets in the emulsion are imaged. Droplets with one or more copies of a nucleic acid are identified, and the nucleic acid concentration of the sample is determined. Numerical simulations of droplet distributions were used to estimate measurement error and dynamic range and to examine the effects of the total volume of droplets imaged and the shape of the droplet size distribution on measurement accuracy. The ability of the method to resolve 1.5- and 3-fold differences in concentration was assessed by using simulations of statistical power. The method was validated experimentally; droplet shrinkage and fusion during amplification were also assessed experimentally and showed negligible effects on measured concentration.
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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.