Exogenous lactate supply affects lactate kinetics of rainbow trout, not swimming performance
Bibliographic record
Abstract
Intense swimming causes circulatory lactate accumulation in rainbow trout because lactate disposal (Rd) is not stimulated as strongly as lactate appearance (Ra). This mismatch suggests that maximal Rd is limited by tissue capacity to metabolize lactate. This study uses exogenous lactate to investigate what constrains maximal Rd and minimal Ra. Our goals were to determine how exogenous lactate affects: 1) Ra and Rd of lactate under baseline conditions or during graded swimming, and 2) exercise performance (critical swimming speed, Ucrit) and energetics (cost of transport, COT). Results show that exogenous lactate allows swimming trout to boost maximal Rd lactate by 40% and reach impressive rates of 56 μmol·kg(-1)·min(-1). This shows that the metabolic capacity of tissues for lactate disposal is not responsible for setting the highest Rd normally observed after intense swimming. Baseline endogenous Ra (resting in normoxic water) is not significantly reduced by exogenous lactate supply. Therefore, trout have an obligatory need to produce lactate, either as a fuel for oxidative tissues and/or from organs relying on glycolysis. Exogenous lactate does not affect Ucrit or COT, probably because it acts as a substitute for glucose and lipids rather than extra fuel. We conclude that the observed 40% increase in Rd lactate is made possible by accelerating lactate entry into oxidative tissues via monocarboxylate transporters (MCTs). This observation together with the weak expression of MCTs and the phenomenon of white muscle lactate retention show that lactate metabolism of rainbow trout is significantly constrained by transmembrane transport.
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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 | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| 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".