Cold acclimation increases cardiac myofilament function and ventricular pressure generation in trout
Bibliographic record
Abstract
Reducing temperature below the optimum of most vertebrate hearts impairs contractility and reduces organ function. However, a number of fish species, including the rainbow trout, can seasonally acclimate to low temperature. Such ability requires modification of physiological systems to compensate for the thermodynamic effects of temperature on biological processes. The current study tested the hypothesis that rainbow trout compensate for the direct effect of cold temperature by increasing cardiac contractility during cold acclimation. We examined cardiac contractility, following thermal acclimation (4, 11 and 17°C), by measuring the Ca(2+) sensitivity of force generation by chemically skinned cardiac trabeculae as well as ventricular pressure generation using a modified Langendorff preparation. We demonstrate, for the first time, that the Ca(2+) sensitivity of force generation was significantly higher in cardiac trabeculae from 4°C-acclimated trout compared with those acclimated to 11 or 17°C, and that this functional change occurred in parallel with a decrease in the level of cardiac troponin T phosphorylation. In addition, we show that the magnitude and rate of ventricular pressure generation was greater in hearts from trout acclimated to 4°C compared with those from animals acclimated to 11 or 17°C. Taken together, these results suggest that enhanced myofilament function, caused by modification of existing contractile proteins, is at least partially responsible for the observed increase in pressure generation after acclimation to 4°C. In addition, by examining the phenotypic plasticity of a comparative model we have identified a strategy, used in vivo, by which the force-generating capacity of cardiac muscle can be increased.
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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 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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".