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Record W4404291774 · doi:10.1016/j.tem.2024.10.002

A three-layer perspective on miRNA regulation in β cell inflammation

2024· review· en· W4404291774 on OpenAlexfundno aff
Stefano Auddino, Elena Aiello, Giuseppina Emanuela Grieco, Francesco Dotta, Guido Sebastiani

Bibliographic record

VenueTrends in Endocrinology and Metabolism · 2024
Typereview
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicMicroRNA in disease regulation
Canadian institutionsnot available
FundersJDRFMinistero dell'Università e della RicercaInnovative Medicines InitiativeMinistero della SaluteEuropean Federation of Pharmaceutical Industries and AssociationsHorizon 2020 Framework ProgrammeUniversità degli Studi di SienaJuvenile Diabetes Research Foundation CanadaMinistero dell’Istruzione, dell’Università e della RicercaLeona M. and Harry B. Helmsley Charitable Trust
KeywordsInflammationmicroRNAPerspective (graphical)Layer (electronics)BiologyCell biologyMedicineImmunologyChemistryComputer scienceGeneticsGeneArtificial intelligence

Abstract

fetched live from OpenAlex

MicroRNAs (miRNAs) are noncoding RNA molecules that regulate gene expression post-transcriptionally and influence numerous biological processes. Aberrant miRNA expression is linked to diseases such as diabetes mellitus; indeed, miRNAs regulate pancreatic islet inflammation in both type 1 (T1D) and type 2 diabetes (T2D). Traditionally, miRNA research has focused on canonical sequences and offers a two-layer view - from expression to function. However, advances in RNA sequencing have revealed miRNA variants, called isomiRs, that arise from alternative processing or modifications of canonical sequences. This introduces a three-layer view - from expression, through sequence modifications, to function. We discuss the potential link between cellular stresses and isomiR biogenesis, and how this association could improve our knowledge of islet inflammation and dysfunction.

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 score0.003
Threshold uncertainty score0.010

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0020.001
Bibliometrics0.0010.002
Science and technology studies0.0000.001
Scholarly communication0.0020.003
Open science0.0010.001
Research integrity0.0020.003
Insufficient payload (model declined to judge)0.0030.002

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 head0.029
GPT teacher head0.331
Teacher spread0.303 · 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

Citations10
Published2024
Admission routes1
Has abstractno

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