Comparison of the effect of non-esterified and esterified astaxanthins on endurance performance in mice
Bibliographic record
Abstract
Astaxanthin, a natural antioxidant, exists in non-esterified and esterified forms. Although it is known that astaxanthin can improve exercise endurance and cause metabolic improvement in skeletal muscle, the effects of the two different forms are unclear. We investigated the effects of the different forms of astaxanthin on endurance in mice. Eight-week-old ICR mice were divided into four groups: control; astaxanthin extracted from Haematococcus pluvialis in an esterified form; astaxanthin extracted from Phaffia rhodozyma in a non-esterified form; and astaxanthin synthesized chemically in a non-esterified form. After 5 weeks of treatment, each group was divided into sedentary and exercise groups. In the group fed astaxanthin from Haematococcus, the running time to exhaustion was longest, and the plasma and tissue concentrations of astaxanthin were significantly higher than those in the other groups. Astaxanthin from Haematococcus increased 5'-adenosine monophosphate-activated protein kinase levels in the skeletal muscle. Although the mice in the Haematococcus group ran for longer, hexanoyl lysine adduct levels in the skeletal muscle mitochondria were similar in the control and Haematococcus groups. Our results suggested that esterified astaxanthin promoted energy production and protected tissues from oxidative damage during exercise owing to its favorable absorption properties, leading to a longer running time.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| 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".