Estrogen Receptor Blockage Attenuates Estrogen-Induced Increases in Post-Exercise Muscle Satellite Cells
Bibliographic record
Abstract
Our laboratory recently demonstrated that numbers of total, activated and proliferating satellite cells are increased during repair of skeletal muscle following downhill running in rats and their numbers appear to be augmented by estrogen. PURPOSE: To investigate whether estrogen receptors (ERs) play a role in mediating this potential estrogenic effect through administration of an ER antagonist, ICI 182,780. METHODS: Ovariectomized female rats (n=72) were divided into 3 groups: estrogen-supplemented (0.25 mg subcutaneous pellet), estrogen-supplemented plus inhibitor (ICI 182,780), and sham (no estrogen). Each group was divided into control (no exercise) and exercised groups. ICI 182,780 injections (1 mg/kg s.c.) were administered 1 day prior to and 6 days following implantation of the estrogen pellet. Following 8 days of estrogen exposure, animals ran downhill intermittently for a total of 90 min (17 m/min, -13.5 degree incline) on a motorized treadmill. Soleus and white vastus muscles were removed 24 and 72 h post-exercise and immunostained for total (Pax7), activated (MyoD) and proliferating (BrdU) satellite cells. RESULTS: The number of fibers positive for total (Pax7), activated (MyoD) and proliferating (BrdU) satellite cells increased significantly (P<0.05) 24 and 72 h post-exercise in both muscles. The exercise-induced increase in the number of fibers positive for all 3 markers was significantly (P<0.05) augmented with estrogen supplementation. In contrast, ICI 182,780 administration abolished both exercise- and estrogen-mediated increases in numbers of fibers positive for these 3 markers. CONCLUSION: Estrogen may potentially influence skeletal muscle repair through ER-mediated activation of satellite cells. Supported by NSERC Canada.
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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".