High resting triacylglycerol turnover of rainbow trout exceeds the energy requirements of endurance swimming
Bibliographic record
Abstract
Fish may use lipoproteins instead of albumin-bound fatty acids to fuel endurance exercise, but lipoprotein kinetics have never been measured in ectotherms. In vivo bolus injections of labeled very-low-density lipoproteins ((3)H-VLDL labeled in vivo from donor fish) and continuous infusions of Intralipid (3H-labeled artificial emulsion) were used to investigate the effects of prolonged exercise (6 h at 1.5 body length/s) and heparin (600 U/kg) on the turnover rate of circulating triacylglycerol (TAG) in rainbow trout. We hypothesized that swimming would stimulate TAG turnover rate to fuel working muscles and that heparin would reduce flux by releasing lipoprotein lipase (LPL) from endothelial cells. Results from both tracer methods show that the baseline TAG turnover rate of trout ranges from 24 to 49 mumol TAG.kg(-1) x min(-1) and exceeds all values measured to date in endotherms. More important, this high resting turnover rate is not stimulated during swimming, because it can already cover several times the energy requirements of locomotion. The fact that heparin causes a 50% decrease in baseline TAG turnover rate suggests that fish LPL must be bound to the endothelium for normal tissue uptake of fatty acids supplied by lipoproteins, as in mammals. We propose that the high resting TAG turnover rate of rainbow trout could be needed by ectotherms for rapid restructuring of membrane phospholipids. The continuous tracer infusion method implemented here could be a versatile tool to investigate the potential role of lipoproteins in providing fatty acids for rapid homeoviscous adaptation.
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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".