ArticleSensors (Basel, Switzerland)2023
Uncertainty Estimation for Quantitative Agarose Gel Electrophoresis of Nucleic Acids.
Article in Sensors (Basel, Switzerland), 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 2 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
2 citing papers in PubMed.
- A simple and cost-effective denaturation-reannealing multiplex PCR (drmPCR) method for discriminating late-feathering genotypes in male Korean indigenous breed chickens.Poultry science · 2026Article
- Advances in Simple, Rapid, and Contamination-Free Instantaneous Nucleic Acid Devices for Pathogen Detection.Biosensors · 2023Review
Corrections and comments
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Authors and funding
7 authors.
Funding
Abstract
This paper considers the evaluation of uncertainty of quantitative gel electrophoresis. To date, such uncertainty estimation presented in the literature are based on the multiple measurements performed for assessing the intra- and interlaboratory reproducibility using standard samples. This paper shows how to estimate the uncertainty in cases where we cannot study scattering components of the results. The first point is dedicated to a case where we have standard samples (the direct expressions are shown). The second point considers the situation when standard samples are absent (the algorithm for estimating the lower bound for uncertainty is discussed). The role of the data processing algorithm is demonstrated.
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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.