Coping with an exogenous glucose overload: glucose kinetics of rainbow trout during graded swimming
Bibliographic record
Abstract
This study examines how chronically hyperglycemic rainbow trout modulate glucose kinetics in response to graded exercise up to critical swimming speed (Ucrit), with or without exogenous glucose supply. Our goals were 1) to quantify the rates of hepatic glucose production (Ra glucose) and disposal (Rd glucose) during graded swimming, 2) to determine how exogenous glucose affects the changes in glucose fluxes caused by exercise, and 3) to establish whether exogenous glucose modifies Ucrit or the cost of transport. Results show that graded swimming causes no change in Ra and Rd glucose at speeds below 2.5 body lengths per second (BL/s), but that glucose fluxes may be stimulated at the highest speeds. Excellent glucoregulation is also achieved at all exercise intensities. When exogenous glucose is supplied during exercise, trout suppress hepatic production from 16.4 ± 1.6 to 4.1 ± 1.7 μmol·kg(-1)·min(-1) and boost glucose disposal to 40.1 ± 13 μmol·kg(-1)·min(-1). These responses limit the effects of exogenous glucose to a 2.5-fold increase in glycemia, whereas fish showing no modulation of fluxes would reach dangerous levels of 114 mM of blood glucose. Exogenous glucose reduces metabolic rate by 16% and, therefore, causes total cost of transport to decrease accordingly. High glucose availability does not improve Ucrit because the fish are unable to take advantage of this extra fuel during maximal exercise and rely on tissue glycogen instead. In conclusion, trout have a remarkable ability to adjust glucose fluxes that allows them to cope with the cumulative stresses of a glucose overload and graded exercise.
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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) | 0.001 | 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) | 0.001 | 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".