Regulation of collagen deposition in the trout heart during thermal acclimation
Bibliographic record
Abstract
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.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot 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.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".