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Record W2153749888 · doi:10.1093/nar/gkr148

Analysis of microRNA turnover in mammalian cells following Dicer1 ablation

2011· article· en· W2153749888 on OpenAlex

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.

Bibliographic record

VenueNucleic Acids Research · 2011
Typearticle
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicMicroRNA in disease regulation
Canadian institutionsOccupational Cancer Research Centre
FundersNational Health and Medical Research CouncilMedical Research CouncilState Government of Victoria
KeywordsmicroRNABiologyCell biologyMessenger RNAGene silencingGeneRNA interferenceEmbryonic stem cellGene expressionRegulation of gene expressionRNAComputational biologyGenetics

Abstract

fetched live from OpenAlex

Although microRNAs (miRNAs) are key regulators of gene expression, little is known of their overall persistence in the cell following processing. Characterization of such persistence is key to the full appreciation of their regulatory roles. Accordingly, we measured miRNA decay rates in mouse embryonic fibroblasts following loss of Dicer1 enzymatic activity. The results confirm the inherent stability of miRNAs, the intracellular levels of which were mostly affected by cell division. Using the decay rates of a panel of six miRNAs representative of the global trend of miRNA decay, we establish a mathematical model of miRNA turnover and determine an average miRNA half-life of 119 h (i.e. ∼5 days). In addition, we demonstrate that select miRNAs turnover more rapidly than others. This study constitutes, to our knowledge, the first in-depth characterization of miRNA decay in mammalian cells. Our findings indicate that miRNAs are up to 10× more stable than messenger RNA and support the existence of novel mechanism(s) controlling selective miRNA cellular concentration and function.

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.

Full frame distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Bench or experimental · Consensus signal: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.360
Threshold uncertainty score0.403

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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 head0.035
GPT teacher head0.313
Teacher spread0.279 · 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