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Temperature regulation of fish‐specific paralogs of cardiac TnC

2013· article· en· W3168403622 on OpenAlexafffund
Christine E. Genge, Glen F. Tibbits

Bibliographic record

VenueThe FASEB Journal · 2013
Typearticle
Languageen
FieldEnvironmental Science
TopicPhysiological and biochemical adaptations
Canadian institutionsChild and Family Research InstituteSimon Fraser University
FundersNatural Sciences and Engineering Research Council of Canada
KeywordsGene isoformZebrafishBiologyContractilityCell biologyTroponin CGene knockdownTroponinGeneVertebrateVentricleTroponin TAnatomyGeneticsInternal medicineEndocrinology

Abstract

fetched live from OpenAlex

Temperature‐dependent regulation of factors involved with cardiac contractility is crucial for ectothermic fish facing changes in temperature. The teleost‐specific whole genome duplication created multiple copies of genes allowing for sub‐functionalization of isoforms. The Ca2+‐binding troponin C (TnC), responsible for initiating myocyte contraction is highly conserved across vertebrates due to its crucial role in regulating contractility. TnC expressed in the hearts of many teleosts is the product of two distinct genes: cardiac TnC (cTnC) and the fish‐specific slow skeletal TnC (ssTnC). Tissue‐specific mRNA profiles in adult zebrafish revealed a distinct localization of gene expression, with ssTnC preferentially expressed in the atrium and cTnC is almost exclusively expressed in the ventricle. Chamber‐specific localization varies between teleost species, with trout preferentially expressing cTnC in both chambers of the heart. Isoform composition of the chambers is also modulated by changes in temperature. Possible sub‐functionalization of TnC isoforms may provide insight into how teleosts achieve physiological versatility in chamber‐specific contractile properties. Funded by NSERC.

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)0.000
metaresearch head score (Gemma)0.000
Version: metacan-v3-hybrid-931329e0061cValidation 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 score0.004
Threshold uncertainty score0.009

Distilled classifier scores by category (both heads)

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)0.0020.001

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 head0.013
GPT teacher head0.199
Teacher spread0.185 · 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 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
Published2013
Admission routes2
Has abstractyes

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