UG0712, A Ginsenoside Complex, Improved Endurance Performance and Changed Hepatic and Muscular Transcriptomic Signatures in C57BL/6N Male Mice
Bibliographic record
Abstract
Ginsenosides, active compounds derived from Panax ginseng, exhibit promising potential in enhancing physical performance. This study investigates the impact of UG0712 (UG), a novel ginsenoside compound, on endurance capacity, body weight, organ weights, blood parameters, and specific transcriptomic changes in liver and muscle tissues using a C57BL/6N mouse model. The mice received UGs orally at three doses: UG50 (50 mg/kg), UG100 (100 mg/kg), and UG200 (200 mg/kg) for a specified duration. Endurance capacity, physiological parameters, and transcriptome signatures in liver and muscle tissues were assessed. UG administration significantly improved time to exhaustion, with UG50 and UG200 showing substantial enhancements. Body and organ weights exhibited no notable differences, suggesting a lack of adverse effects. Biochemical markers, except for decreased creatine kinase levels in the UG100 group, showed no significant variations. Transcriptome analysis revealed limited group separation and dose-dependent patterns. The UG100 group displayed significant enrichment in lipid metabolism and muscle-related terms. Identified dose-dependent improvements in endurance capacity highlight UGs' potential as supplements. The absence of adverse effects on body and organ weights, along with positive effects on biochemical markers, supports their safety. Despite limited dose-dependent patterns in transcriptomic analyses, the UG100 group showcased significant enrichment in pathways related to muscle and lipid metabolism. These findings offer valuable insights for athletes and aging individuals seeking to enhance physical performance, warranting further exploration into UG effects' on molecular mechanisms.
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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 | 0.000 | 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".