Can oestrogen influence skeletal muscle damage, inflammation, and repair?
Bibliographic record
Abstract
More research is needed before the use of oestrogen can be recommended for muscle damage Oestrogen may affect muscle damage and inflammation, but the physiological significance of this, particularly potential effects on muscle repair and recovery in humans, and the mechanisms of its actions are as of yet unknown. Over the last decade, evidence has accumulated that oestrogen may act to diminish skeletal muscle damage and inflammatory responses after unaccustomed exercise or other damaging insult. These prophylactic effects are similar to the effect of oestrogen in diminishing damage and inflammation in other tissues such as cardiac muscle, liver, and nervous tissue.1–3 What is not clear is the physiological significance of this effect of oestrogen and whether it is physiologically meaningful or results in significantly differential responses in skeletal muscle after over-exertion in humans.4 The mechanisms by which oestrogen exerts its effect on skeletal muscle responses to damaging exercise, and whether its influence extends to differential rates of muscle repair and recovery also require further investigation. The primary practical implication of a role for oestrogen in post-exercise muscle damage, recovery, and repair mechanisms is likely to be for postmenopausal women who have reduced oestrogen concentrations, and thus may be more susceptible to muscle damage and slower recovery than premenopausal women. Sex differences in susceptibility to muscle damage are also of interest. This overview will briefly examine these questions and summarise the current evidence for oestrogenic influence on skeletal muscle damage, inflammation, and repair. Considerable research is still needed before any conclusions can be reached about the physiological significance of these effects of oestrogen in humans. Oestrogen is a strong antioxidant and a membrane stabiliser.5,6 We have proposed that these properties, which are unique to oestrogen as a steroid hormone, may account for some of its mitigating …
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.006 | 0.002 |
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".