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Record W4412538660 · doi:10.1016/j.genrep.2025.102302

Anoxia-responsive microRNA profile of freshwater turtle red skeletal muscle

2025· article· en· W4412538660 on OpenAlexafffund
Tighe Bloskie, Kenneth B. Storey

Bibliographic record

VenueGene Reports · 2025
Typearticle
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicMicroRNA in disease regulation
Canadian institutionsCarleton University
FundersNatural Sciences and Engineering Research Council of Canada
KeywordsTurtle (robot)microRNASkeletal muscleBiologyCell biologyFisheryAnatomyBiochemistryGene

Abstract

fetched live from OpenAlex

The red-eared slider ( Trachemys scripta elegans ) is a uniquely impressive vertebrate facultative anaerobe, capable of 18 weeks without oxygen at 3 °C. Metabolic rate depression (∼85 %) is the core feature of anaerobiosis and is characterized by the suppression of costly processes like protein synthesis/decay and the cell cycle. The elucidation of microRNA (miRNA) action in support of animal extreme stress adaptation is increasing, but is currently lacking in T.s. elegans anoxia tolerance. Here, we use small RNA sequencing and subsequent bioinformatic analyses to identify differentially expressed miRNA and predicted target pathways in 20 h anoxic red skeletal muscle of red-eared slider turtles. Of the 52 mapped miRNA species, we identify two that were upregulated (miR-2114-5p, let-7f-5p) and two that were downregulated (miR-1260b, miR-5100) under 20 h anoxic conditions (|FC| > 1.5; p < 0.05). KEGG and GO analysis predict miRNA contribute to the aerobic to anaerobic respiration shift and outline miRNA-mediated inhibition of numerous gene sets in (1) protein turnover, (2) RNA turnover and (3) the cell cycle. Conversely, alleviated miRNA interference in branched amino acid biosynthesis, arachidonic acid and linolenic acid metabolism suggest a role in atrophy resistance of skeletal muscles.

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 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)0.000
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 score0.092
Threshold uncertainty score0.487

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
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 head0.005
GPT teacher head0.236
Teacher spread0.231 · 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 teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designBench or experimental
Domainnot available
GenreEmpirical

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

Citations0
Published2025
Admission routes2
Has abstractyes

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