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Molecular mechanisms regulating cardiac contractility: subfunctionalization of fish‐specific paralogs of troponin C in Danio rerio

2012· article· en· W3173555163 on OpenAlexaff
Christine E. Genge, Glen F. Tibbits

Bibliographic record

VenueThe FASEB Journal · 2012
Typearticle
Languageen
FieldEnvironmental Science
TopicPhysiological and biochemical adaptations
Canadian institutionsSimon Fraser University
Fundersnot available
KeywordsSubfunctionalizationZebrafishDanioBiologyContractilityTroponin CMyofilamentCardiac muscleXenopusSkeletal muscleCell biologyGene knockdownGene isoformTroponin IMyocyteGeneGeneticsAnatomyInternal medicineGene expressionEndocrinologyGene familyMedicine

Abstract

fetched live from OpenAlex

Myofilament Ca 2+ sensitivity is a critical factor in regulating cardiac contractility. The key regulator, cardiac troponin (cTn), is made up of three proteins (cTnC, cTnI, cTnT). Though Ca 2+ ‐activated component of cTn,TnC is highly conserved across phylogenetically diverse groups, subtle variations are seen as interspecies orthologs and intraspecies paralogs between tissues. In mammals, two forms of TnC exist with distinct localization patterns (fast skeletal muscle and slow skeletal/cardiac muscle) and Ca 2+ affinities, but in fish a third paralog exists annotated ssTnC in zebrafish. 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 zebrafish ventricle, which may reflect the differing contractile properties between the cardiac chambers. The functionality of these differences is being compared biochemically through measurement of Ca2+ binding kinetics. This subfunctionalization of TnC isoforms lends insight the consequences of variation to the structure‐function of cTn and variation in cardiac muscle contraction.

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 score0.002
Threshold uncertainty score0.004

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)0.0010.000

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 head0.018
GPT teacher head0.218
Teacher spread0.200 · 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
Published2012
Admission routes1
Has abstractyes

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