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Record W4211264070 · doi:10.1016/j.crphys.2022.02.004

Regulation of collagen deposition in the trout heart during thermal acclimation

2022· article· en· W4211264070 on OpenAlexafffund
Elizabeth F. Johnston, Todd E. Gillis

Bibliographic record

VenueCurrent Research in Physiology · 2022
Typearticle
Languageen
FieldEnvironmental Science
TopicPhysiological and biochemical adaptations
Canadian institutionsUniversity of Guelph
FundersNatural Sciences and Engineering Research Council of Canada
KeywordsExtracellular matrixVentricleHydroxyprolineCell biologyFibrosisInternal medicineMechanotransductionChemistryAcclimatizationAnatomyBiologyEndocrinologyMedicineEcology

Abstract

fetched live from OpenAlex

The passive mechanical properties of the vertebrate heart are controlled in part by the composition of the extracellular matrix (ECM). Changes in the ECM, caused by increased blood pressure, injury or disease can affect the capacity of the heart to fill with blood during diastole. In mammalian species, cardiac fibrosis caused by an increase in collagen in the ECM, leads to a loss of heart function and these changes in composition are considered to be permanent. Recent work has demonstrated that the cardiac ventricle of some fish species have the capacity to both increase and decrease collagen content in response to thermal acclimation. It is thought that these changes in collagen content help maintain ventricle function over seasonal changes in environmental temperatures. This current work reviews the cellular mechanisms responsible for regulating collagen deposition in the mammalian heart and proposes a cellular pathway by which a change in temperature can affect the collagen content of the fish ventricle through mechanotransduction. This work specifically focuses on the role of transforming growth factor β1, MAPK signaling pathways, and biomechanical stretch in regulating collagen content in the fish ventricle. It is hoped that this work increases the appreciation of the use of comparative models to gain insight into phenomenon with biomedical relevance.

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)0.001
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: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.969
Threshold uncertainty score0.501

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 head0.095
GPT teacher head0.364
Teacher spread0.268 · 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

Citations14
Published2022
Admission routes2
Has abstractyes

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