Eccentric Exercise Activates Novel Transcriptional Regulation of Hypertrophic Signaling Pathways Not Affected by Hormone Changes
Bibliographic record
Abstract
Unaccustomed eccentric exercise damages skeletal muscle tissue, activating mechanisms of recovery and remodeling that may be influenced by the female sex hormone 17beta-estradiol (E2). Using high density oligonucleotide based microarrays, we screened for differences in mRNA expression caused by E2 and eccentric exercise. After random assignment to 8 days of either placebo (CON) or E2 (EXP), eighteen men performed 150 single-leg eccentric contractions. Muscle biopsies were collected at baseline (BL), following supplementation (PS), +3 hours (3H) and +48 hours (48H) after exercise. Serum E2 concentrations increased significantly with supplementation (P<0.001) but did not affect microarray results. Exercise led to early transcriptional changes in striated muscle activator of Rho signaling (STARS), Rho family GTPase 3 (RND3), mitogen activated protein kinase (MAPK) regulation and the downstream transcription factor FOS. Targeted RT-PCR analysis identified concurrent induction of negative regulators of calcineurin signaling RCAN (P<0.001) and HMOX1 (P = 0.009). Protein contents were elevated for RND3 at 3H (P = 0.02) and FOS at 48H (P<0.05). These findings indicate that early RhoA and NFAT signaling and regulation are altered following exercise for muscle remodeling and repair, but are not affected by E2.
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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".