Oestrogen receptor‐alpha activation augments post‐exercise myoblast proliferation
Bibliographic record
Abstract
AIM: Our laboratory has shown that oestrogen acts to augment myoblast (satellite cell) activation, proliferation and total number and that this may occur through an oestrogen receptor (OR)-mediated mechanism. The purpose of this study was to further investigate the mechanism of oestrogen influence on augmentation of post-exercise myoblast numbers through use of a specific OR-alpha agonist, propyl pyrazole triol (PPT). METHODS: Ovariectomized rats were used (n = 64) and separated into four groups: sham, oestrogen supplemented, agonist supplemented, and a combined oestrogen and agonist supplemented group. These groups were further subdivided into control (unexercised) and exercise groups. Surgical removal of white vastus and soleus muscles was performed 72 h post-exercise. Muscle samples were immunostained for the myoblast markers Pax7 and MyoD. RESULTS: A significant increase in total (Pax7-positive) and activated (MyoD-positive) myoblasts was found in all groups post-exercise. A further significant augmentation of total and activated myoblasts occurred in oestrogen supplemented, agonist supplemented and the combined oestrogen and agonist supplemented groups post-exercise in white vastus and soleus muscles relative to unsupplemented animals. CONCLUSION: These results demonstrate that both oestrogen and the specific OR-alpha receptor agonist, PPT, can significantly and to similar degrees augment myoblast number and activation following exercise-induced muscle damage. This suggests that oestrogen acts through an OR-mediated mechanism to stimulate myoblast proliferation following exercise, with OR-alpha playing a primary role.
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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) | 0.002 | 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".