Quercetin alters energy metabolism in swimming mice
Bibliographic record
Abstract
Quercetin has been demonstrated to be effective in increasing physical endurance in mice and humans. However, the mechanisms involved are not fully understood. In this study, male Kunming mice were fed a diet containing 0.1% quercetin for 14 days before swimming for 60 min. The overall serum metabolic profile was investigated by a ¹H nuclear magnetic resonance-based metabolomic approach. Serum glucose, lactate, nonesterified fatty acids (NEFA), and nonprotein nitrogen (NPN), as well as hepatic and muscular glycogen were measured biochemically. The results of metabolomic analysis showed that swimming induced a significant change in serum metabolic profile. Relative increases in the levels of lactate, alanine, low-density lipoprotein-very low-density lipoprotein, and unsaturated fatty acids, and decreases in choline, phosphocholine, and glucose were observed after swimming. With quercetin supplementation, these changes were attenuated. The results of biochemical assays were consistent with the data obtained from metabolomic analysis, in that serum NEFA was increased while lactate and NPN decreased after exposed to quercetin in swimming mice. Similar change in NEFA was also found in liver and gastrocnemius muscle tissues. Our current findings suggest that quercetin alters energy metabolism in swimming mice and increased lipolysis may contribute to the actions of quercetin on physical endurance.
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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 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 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".