L‐Carnitine metabolism, protein turnover and energy expenditure in supplemented and exercised Labrador Retrievers
Bibliographic record
Abstract
Abstract L‐Carnitine is critical for protection against bioaccumulation, long‐chain fatty acid transportation and energy production. Energy production becomes important as the body maintains lean mass, repairs muscles and recovers from oxidative stress. The aim was to investigate the effects of supplemented L‐carnitine on protein turnover (PT), energy expenditure (EE) and carnitine metabolism in muscle/serum of Labrador Retrievers. In a series of experiments, all dogs were fed a low‐carnitine diet and sorted into one of two groups: L‐carnitine (LC) supplemented daily with 125 mg L‐carnitine and 3.75 g sucrose or placebo (P) supplemented with 4 g sucrose daily. The experiments consisted of analyses of muscle/serum for L‐carnitine content (EXP1), a protein turnover experiment (EXP2) and analysis of substrate utilization via indirect calorimetry (EXP3). EXP1: 20 Labradors (10 M/10 F) performed a 13 week running regimen. L‐Carnitine content was analysed in the serum and biceps femoris muscle before/after a 24.1 km run. LC serum had higher total (p < .001; p = .001), free (p < .001; p = .001) and esterified (p = .001; p = .003) L‐carnitine pre‐ and post‐run respectively. LC muscle had significantly higher free L‐carnitine post‐run (p = .034). EXP2: 26 Labs (13 M/13 F) performed a 60‐day running regimen. For the final run, half of the Labradors from each treatment rested and half ran 24.1 km. Twenty‐four Labradors received isotope infusion, and then, a biopsy of the biceps femoris of all 26 Labradors was taken to determine PT. Resting/exercised LC had a lower fractional breakdown rate (FBR) versus P group (p = .042). LC females had a lower FBR v. P females (p = .046). EXP3: Respiration of 16 Labradors (8 M/8 F) was measured via indirect calorimetry over 15 week. All dogs ran on a treadmill for 30 min at 30% VO2 max (6.5 kph), resulting in higher maximum and mean EE in LC females v. P females (p = .021; p = .035). Implications for theory, practice and future research are discussed.
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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 | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 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".