Lack of influence of estrogen on myosin phosphorylation and post-tetanic potentiation in muscles from young adult C57BL mice
Bibliographic record
Abstract
Estrogen influences myosin phosphorylation and post-tetanic potentiation in murine fast muscle. We tested the hypothesis that this influence is mediated by estrogen effects on skeletal myosin light chain kinase (skMLCK) activity. To this end, extensor digitorum longus muscles from female wildtype and skMLCK-absent (skMLCK−/−) mice were grouped as follows: ovariectomized with estrogen (E+), ovariectomized without estrogen (E–), sham surgery, and intact baseline. At 8 weeks of age, the ovariectomized groups were ovariectomized followed by implantation of either a 0.1 mg 17β-estradiol (E+) or placebo pellet (E–). Two weeks later, muscles were isolated and suspended in vitro (25° C) for determination of regulatory light chain phosphorylation and post-tetanic potentiation. Regulatory light chain phosphorylation was not different across conditions within either genotype although wildtype values were significantly greater than skMLCK−/− values. Consistent with this, the potentiation of concentric twitch force was similar between E+ and E– groups within each genotype but wildtype values were greater than skMLCK−/− values. However, unaltered estradiol levels following ovariectomy, likely due to previously underappreciated confounds of mouse age, development, and growth during estrogen supplementation, prevented direct testing of the hypothesis. Future studies should note the importance of estrous cycles and continuing physiological developments of young adult mice when working with ovarian hormones.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".