Ginseng ameliorates exercise-induced fatigue potentially by regulating the gut microbiota
Bibliographic record
Abstract
The therapeutic effects of water extract of ginseng (WEG) on exercise-induced fatigue (EF) have been reported in several previous studies, but the molecular mechanisms involved remain unexplored. In this study, the anti-EF effects of WEG were studied, and the potential mechanisms were discussed. We characterized the chemical components of WEG by ultra-high performance liquid chromatography-tandem triple quadrupole mass spectrometry (UHPLC-QqQ-MS/MS) and high performance liquid chromatography coupled with evaporative light scattering detection (HPLC-ELSD), and then examined the anti-EF effects of WEG on a rat model of weight-loaded swimming with a focus on endogenous metabolism and gut microbiota. WEG contains abundant (90.15%, w/w) saccharides and ginsenosides with structurally diverse glycosyls. WEG taken orally showed strong anti-EF effects by ameliorating energy metabolism abnormality, oxidative stress, lipid peroxidation, inflammatory response, disorders in the metabolism of bile acid, amino acid, fatty acid and lipid, as well as the gut microbiota dysbiosis. Given that gut microbiota is significantly associated with energy expenditure, systemic inflammation and host metabolism, these findings suggest a potential central role of the gut microbiota in mediating the anti-EF effect of WEG. That is, the saccharides and ginsenosides in WEG serve as energy substrates for specific intestinal bacteria, thereby beneficially regulating the gut microbiota, and the reshaped gut microbial ecosystem then triggers several molecular and cellular signaling pathways (e.g. butyrate or TGR5 signals) to achieve the therapeutic effects on EF. The outcomes highlighted here enable deeper insight into how WEG overcomes EF.
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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 | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".