Definition and research guide
This page combines a MeSH definition, publication statistics and selected research abstracts. It is a starting point for reading the literature, not a medical textbook or a systematic evidence review.
Content available: 4 complete abstracts (651 words), 10 references. Full article text is not stored or reproduced here; follow the PMID, DOI or PMC links to read the original sources and their access terms.
Definition
Concentration or quantity that is derived from the smallest measure that can be detected with reasonable certainty for a given analytical procedure.
Definition: NLM Medical Subject Headings (MeSH) scope note (Public domain (U.S. National Library of Medicine); attribution requested). View the MeSH record.
Also known as: Detection Limit; Detection Limits; Limits of Detection.
Where it fits in the MeSH classification
- Broader topics
- Sensitivity and Specificity
Research landscape
PubMed indexes 57,871 records tagged with the MeSH heading “Limit of Detection” (counts retrieved 2026-10-29). 28,854 of them (49.9%) were published from 2020 onward, and the busiest period so far is 2010-2019 with 29,268 records.
Reviews account for 476 records (0.8%) and meta-analyses for 7. Randomized controlled trials number 74 (0.1%) as a share of all records on the topic. Randomized trials make up only a small share of this literature, so much of what is known comes from laboratory, observational or descriptive work.
Show the data
| Period | Records |
|---|---|
| through 1999 | 0 |
| 2000-2009 | 769 |
| 2010-2019 | 29,268 |
| 2020 onward | 28,854 |
Publication-type chart data
| Type | Records | Share |
|---|---|---|
| Reviews | 476 | 0.8% |
| Meta-analyses | 7 | 0.0% |
| Randomized controlled trials | 74 | 0.1% |
Method: PubMed query "Limit of Detection"[MeSH Terms] with publication-type and date filters, retrieved 2026-10-29.
Reading the evidence in clinical research
Research methods determine how much a result can be trusted. Randomized trials guard against confounding, systematic reviews and meta-analyses combine many studies, and observational designs are valuable but can show only association. Reporting standards, trial registration and pre-specified outcomes reduce bias. When reading about a method, ask what problem it is designed to solve and which problems it does not solve.
Evidence from open-access studies
These open-access studies come from PubMed’s best-match results for this heading (papers whose main topic it is), ranked here by citation count, with one recent addition. Each abstract is reproduced in full under its recorded open licence so that you can read the authors’ own words. They illustrate the literature; they are not a systematic summary of it. Citation counts measure attention, not reliability or clinical benefit.
| Study / authors | Year | Source label / design | Europe PMC citations | Licence / source |
|---|---|---|---|---|
| On the Determination of Uncertainty and Limit of Detection in Label-Free Biosensors | 2018 | Review article Design not recorded in structured metadata | 73 | cc by PMID 29949904 · DOI · PMC6068557 |
| Classification and specific primer design for accurate detection of SARS-CoV-2 using deep learning | 2021 | Research article Design not recorded in structured metadata | 65 | cc by PMID 33441822 · DOI · PMC7806918 |
| Ultrasensitive optical biosensor for detection of miRNA-155 using positively charged Au nanoparticles | 2018 | Research article Design not recorded in structured metadata | 61 | cc by PMID 29440644 · DOI · PMC5811613 |
| Assessing Antiparasitic Compounds Persistence in Cattle Hair by DART-MS | 2025 | Research article Design not recorded in structured metadata | 4 | cc by PMID 39601233 · DOI · PMC11697339 |
On the Determination of Uncertainty and Limit of Detection in Label-Free Biosensors [1]
Abstract reading summary — source excerpts
- Opening excerpt
- A significant amount of noteworthy articles reviewing different label-free biosensors are being published in the last years.
- Closing excerpt
- Finally, the model is applied to curves that are typically obtained in immunoassays and a discussion is made on the application validity of the model and its limitations.
- Interpretation limits
- Verbatim excerpts from the authors’ abstract; the closing excerpt may describe a finding, limitation or recommendation. Design, population, effect size and uncertainty have not been independently extracted or appraised. Read the complete abstract and linked paper before interpreting the findings.
Read complete abstract (159 words)
A significant amount of noteworthy articles reviewing different label-free biosensors are being published in the last years. Most of the times, the comparison among the different biosensors is limited by the procedure used of calculating the limit of detection and the measurement uncertainty. This article clarifies and establishes a simple procedure to determine the calibration function and the uncertainty of the concentration measured at any point of the measuring interval of a generic label-free biosensor. The value of the limit of detection arises naturally from this model as the limit at which uncertainty tends when the concentration tends to zero. The need to provide additional information, such as the measurement interval and its linearity, among others, on the analytical systems and biosensor in addition to the detection limit is pointed out. Finally, the model is applied to curves that are typically obtained in immunoassays and a discussion is made on the application validity of the model and its limitations.
Abstract reproduced from the open-access article under the recorded cc by licence; © the authors. PMID 29949904 · DOI · PMC6068557
Classification and specific primer design for accurate detection of SARS-CoV-2 using deep learning [2]
Abstract reading summary — source excerpts
- Opening excerpt
- In this paper, deep learning is coupled with explainable artificial intelligence techniques for the discovery of representative genomic sequences in SARS-CoV-2.
- Closing excerpt
- Considering the possibility of future pandemics, these characteristics are invaluable to promptly create specific detection methods for diagnostics.
- Interpretation limits
- Verbatim excerpts from the authors’ abstract; the closing excerpt may describe a finding, limitation or recommendation. Design, population, effect size and uncertainty have not been independently extracted or appraised. Read the complete abstract and linked paper before interpreting the findings.
Read complete abstract (222 words)
In this paper, deep learning is coupled with explainable artificial intelligence techniques for the discovery of representative genomic sequences in SARS-CoV-2. A convolutional neural network classifier is first trained on 553 sequences from the National Genomics Data Center repository, separating the genome of different virus strains from the Coronavirus family with 98.73% accuracy. The network's behavior is then analyzed, to discover sequences used by the model to identify SARS-CoV-2, ultimately uncovering sequences exclusive to it. The discovered sequences are validated on samples from the National Center for Biotechnology Information and Global Initiative on Sharing All Influenza Data repositories, and are proven to be able to separate SARS-CoV-2 from different virus strains with near-perfect accuracy. Next, one of the sequences is selected to generate a primer set, and tested against other state-of-the-art primer sets, obtaining competitive results. Finally, the primer is synthesized and tested on patient samples (n = 6 previously tested positive), delivering a sensitivity similar to routine diagnostic methods, and 100% specificity. The proposed methodology has a substantial added value over existing methods, as it is able to both automatically identify promising primer sets for a virus from a limited amount of data, and deliver effective results in a minimal amount of time. Considering the possibility of future pandemics, these characteristics are invaluable to promptly create specific detection methods for diagnostics.
Abstract reproduced from the open-access article under the recorded cc by licence; © the authors. PMID 33441822 · DOI · PMC7806918
Ultrasensitive optical biosensor for detection of miRNA-155 using positively charged Au nanoparticles [3]
Abstract reading summary — source excerpts
- Opening excerpt
- An ultrasensitive optical biosensor for microRNA-155 (miR-155) was developed to diagnose breast cancer at early stages.
- Closing excerpt
- So, proposed sensor enables miR-155 detection at very low concentrations with the detection limit of 100 aM and a wide linear range from 100 aM to 100 fM.
- Interpretation limits
- Verbatim excerpts from the authors’ abstract; the closing excerpt may describe a finding, limitation or recommendation. Design, population, effect size and uncertainty have not been independently extracted or appraised. Read the complete abstract and linked paper before interpreting the findings.
Read complete abstract (145 words)
An ultrasensitive optical biosensor for microRNA-155 (miR-155) was developed to diagnose breast cancer at early stages. At first, the probe DNA covalently bind to the negatively charged gold nanoparticles (citrate-capped AuNPs). Then, the target miR-155 electrostatically adsorb onto the positively charged gold nanoparticles (polyethylenimine-capped AuNP) surface. Finally, by mixing citrate-capped AuNP/probe and polyethylenimine-capped AuNP/miR-155, hybridization occurs and the optical signal of the mixture give a measure to quantify the miR-155 content. The proposed biosensor is able to specify 3-base-pair mismatches and genomic DNA from target miR-155. The novelty of this biosensor is in its ability to trap the label-free target by its branched positively charged polyethylenimine. This method increases loading the target on the polyethylenimine-capped AuNPs' surface. So, proposed sensor enables miR-155 detection at very low concentrations with the detection limit of 100 aM and a wide linear range from 100 aM to 100 fM.
Abstract reproduced from the open-access article under the recorded cc by licence; © the authors. PMID 29440644 · DOI · PMC5811613
Assessing Antiparasitic Compounds Persistence in Cattle Hair by DART-MS [4]
Abstract reading summary — source excerpts
- Opening excerpt
- This study introduces an alternative strategy for evaluating antiparasitic persistence compounds in cattle hair by Direct Analysis in Real Time Mass Spectrometry (DART-MS).
- Closing excerpt
- Overall, the DART-MS method provided a fast and efficient analysis for determination of antiparasitic agents in cattle hair, which contributes to the evaluation of drug administration protocols and dosage intervals, and aids the safety and advancement of the livestock sector.
- Interpretation limits
- Verbatim excerpts from the authors’ abstract; the closing excerpt may describe a finding, limitation or recommendation. Design, population, effect size and uncertainty have not been independently extracted or appraised. Read the complete abstract and linked paper before interpreting the findings.
Read complete abstract (125 words)
This study introduces an alternative strategy for evaluating antiparasitic persistence compounds in cattle hair by Direct Analysis in Real Time Mass Spectrometry (DART-MS). The developed DART-MS method aimed to determine fenthion, chlorpyrifos, and cypermethrin in cattle hair samples. DART-MS analyses were performed in positive ion mode, and parameters related to the DART source were evaluated. The analytical performance demonstrated the efficiency of the optimized DART-MS method for fenthion, chlorpyrifos, and cypermethrin quantification in the evaluated samples, meeting criteria for precision, accuracy and limits of detection. Overall, the DART-MS method provided a fast and efficient analysis for determination of antiparasitic agents in cattle hair, which contributes to the evaluation of drug administration protocols and dosage intervals, and aids the safety and advancement of the livestock sector.
Abstract reproduced from the open-access article under the recorded cc by licence; © the authors. PMID 39601233 · DOI · PMC11697339
Key reviews and meta-analyses
The most cited review-type papers among PubMed’s best-match results for this heading, listed with bibliographic details only. Follow the links for the full text and access terms.
- Research article Super-resolution microscopy approaches for live cell imaging [5]
- Research article Sensitivity enhancement in lateral flow assays: a systems perspective [6]
- Research article High sensitivity cardiac troponin assays in the clinical laboratories [7]
- Research article Optical Nanoprobes for Ultrasensitive Immunoassay [8]
- Research article Recent progress, challenges and trends in trace determination of drug analysis using molecularly imprinted solid-phase microextraction technology [9]
- Research article Toward improved analysis of concentration data: Embracing nondetects [10]
References
- Lavín Á, Vicente J, Holgado M, Laguna MF, Casquel R, Santamaría B, et al.. On the Determination of Uncertainty and Limit of Detection in Label-Free Biosensors. . 2018. PMID 29949904 · DOI · PMC6068557
- Lopez-Rincon A, Tonda A, Mendoza-Maldonado L, Mulders DGJC, Molenkamp R, Perez-Romero CA, et al.. Classification and specific primer design for accurate detection of SARS-CoV-2 using deep learning. . 2021. PMID 33441822 · DOI · PMC7806918
- Hakimian F, Ghourchian H, Hashemi AS, Arastoo MR, Behnam Rad M. Ultrasensitive optical biosensor for detection of miRNA-155 using positively charged Au nanoparticles. . 2018. PMID 29440644 · DOI · PMC5811613
- Batista Junior AC, Maciel LÍL, Rocha YA, Souza GG, Vaz BG, Lopes WDZ, et al.. Assessing Antiparasitic Compounds Persistence in Cattle Hair by DART-MS. . 2025. PMID 39601233 · DOI · PMC11697339
- Godin AG, Lounis B, Cognet L. Super-resolution microscopy approaches for live cell imaging. Biophys J. 2014. PMID 25418158 · DOI · PMC4213717
- Bishop JD, Hsieh HV, Gasperino DJ, Weigl BH. Sensitivity enhancement in lateral flow assays: a systems perspective. Lab Chip. 2019. PMID 31251312 · DOI
- Jarolim P. High sensitivity cardiac troponin assays in the clinical laboratories. Clin Chem Lab Med. 2015. PMID 25252753 · DOI
- Fu X, Chen L, Choo J. Optical Nanoprobes for Ultrasensitive Immunoassay. Anal Chem. 2017. PMID 28105817 · DOI
- Ansari S, Karimi M. Recent progress, challenges and trends in trace determination of drug analysis using molecularly imprinted solid-phase microextraction technology. Talanta. 2017. PMID 28107981 · DOI
- Shoari N, Dubé JS. Toward improved analysis of concentration data: Embracing nondetects. Environ Toxicol Chem. 2018. PMID 29168890 · DOI
About this article
- Sources
- NLM Medical Subject Headings (definition, synonyms); NCBI PubMed (counts, identifiers); Europe PMC (open-access abstracts and citation counts).
- Verification
- Every reference was resolved against PubMed on 2026-10-31; title, year and DOI matched.
- Review state
- automated source verified; no human expert review has been recorded yet.
- Version
- 1, last updated 2026-10-29.
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Evidence strength
EvidenceHelix's computed view of the study designs behind this topic: how common meta-analyses, randomized trials and reviews are, how recent the literature is, and a plain-language reading. It describes the design mix of the literature. It is not a quality appraisal of any study, and it is not medical advice.