<i>In vivo</i>regulation of rainbow trout lipolysis by catecholamines
Bibliographic record
Abstract
Lipolysis provides fatty acids that support key life processes by functioning as membrane components, oxidative fuels and metabolic signals. It is commonly measured as the rate of appearance of glycerol (Ra glycerol). Its in vivo regulation by catecholamines has been thoroughly investigated in mammals, but little information is available for ectotherms. Therefore, the goals of this study were, first, to characterize the effects of the catecholamines norepinephrine (NE) and epinephrine (Epi) on the lipolytic rate of intact rainbow trout (Oncorhynchus mykiss) and, second, to determine whether the plasma glycerol concentration is a reliable index of Ra glycerol. Our results show that baseline Ra glycerol (4.6+/-0.4 micromol kg(-1) min(-1)) is inhibited by NE (-56%), instead of being stimulated, as in mammals, whereas Epi has the same activating effect in both groups of vertebrates (+167%). NE-induced inhibition of fish lipolysis might play a particularly important role during aquatic hypoxia, when survival often depends on regulated metabolic depression. The plasma glycerol concentration is a poor predictor of Ra glycerol, and it should not be used as an index of lipolysis. Trout maintain a particularly high baseline lipolytic rate because only 13% of the fatty acids provided are sufficient to support total energy expenditure, whereas the remaining fatty acids must undergo reesterification (87%).
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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 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".