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Record W3048331126 · doi:10.3389/fmolb.2020.00181

Fecal MicroRNAs as Potential Biomarkers for Screening and Diagnosis of Intestinal Diseases

2020· review· en· W3048331126 on OpenAlexfundno aff
Humaira Rashid, Biplob Hossain, Towfida Jahan Siddiqua, Mamun Kabir, Zannatun Noor, Mamun Ahmed, Rashidul Haque

Bibliographic record

VenueFrontiers in Molecular Biosciences · 2020
Typereview
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicMicroRNA in disease regulation
Canadian institutionsnot available
FundersGlobal Affairs CanadaDepartment for International DevelopmentStyrelsen för Internationellt Utvecklingssamarbete
KeywordsmicroRNABiologyTranslation (biology)Gene silencingMessenger RNAGene expressionRNAGeneSmall RNARegulation of gene expressionComputational biologyBioinformaticsDiagnostic biomarkerGeneticsBiomarker

Abstract

fetched live from OpenAlex

MicroRNAs (miRNAs) are a class of conserved endogenous, small non-coding RNA molecules with a length of 18-25 nucleotides that regulate gene expression by RNA interference processes, including mRNA chopping, mRNA deadenylation, and translation inhibition. miRNAs maintain the physiological functions of the intestine and are instrumental in gut pathogenesis. miRNAs play an important role in intercellular communication and are present in all body fluids, including stools with different composition and concentrations. However, under diseased conditions, miRNAs are aberrantly expressed and act as negative regulators of gene expression. The stable and differentially expressed miRNAs in stool enables miRNAs to be used as potential biomarkers for screening of various intestinal diseases. In this review, we summarize the expressed miRNA profile in stool and highlight miRNAs as biomarkers with potential clinical and diagnostic applications, and we aim to address the prospects for recent advanced techniques for screening miRNA in diagnosis and prognosis of intestinal disorders.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.001
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.002
Threshold uncertainty score0.007

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0020.001
Science and technology studies0.0000.000
Scholarly communication0.0010.001
Open science0.0010.001
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0020.002

Machine scores (provisional)

The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.

Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.

Opus teacher head0.012
GPT teacher head0.283
Teacher spread0.271 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designNot applicable
Domainnot available
GenreReview

How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".

Quick stats

Citations43
Published2020
Admission routes1
Has abstractyes

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Same venueFrontiers in Molecular BiosciencesSame topicMicroRNA in disease regulationFrench-language works237,207