Adaptive responses to chronic creatine loading (CL) and voluntary running (Run) in rat skeletal muscle
Bibliographic record
Abstract
We investigated whether CL and Run alters myosin heavy chain (MHC) based fibre types, the proteins that regulate intracellular calcium and the metabolic profile in the rat plantaris . Forty Sprague‐Dawley male rats were assigned to one of four groups: CL+Sedentary ( Cre‐Sed ); CL+Run ( Cre‐Run ); control+Sed ( Con‐Sed ); Con+Run ( Con‐Run ). Cre‐Run resulted in a 10% increase in type IIB fibres and a corresponding 11% decrease in type IIA fibres compared to Con‐Run (P<0.03). Parvalbumin content was decreased by 75% in Cre‐Sed and Cre‐Run (P<0.04). No differences were observed in the fast Ca 2+ ATPase isoform, SERCA1, in any of the groups (P>0.49). SERCA2 content, a slow Ca 2+ ATPase isoform, was 21% and 19% lower in Cre‐Sed and Cre‐Run compared to Con‐Sed and Con‐Run , respectively (P≤0.05). Run increased citrate synthase and 3‐hydroxyacyl‐CoA dehydrogenase activities (P≤0.05), while phosphofructokinase and glyceraldehyde phosphate dehydrogenase activities did not change; Cr did not alter any of these enzyme contents (P>0.28). We conclude that CL during Run was able to maintain a faster fibre type while not changing the metabolic profile indicating a disconnect between coordinated fibre type conversions and metabolic phenotypic profiles. We suggest that CL alleviates the need for parvalbumin and SERCA2 expression probably due to enhanced high energy phosphate shuttling that better supports SERCA1. NSERC & AHFMR.
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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 | 0.000 | 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 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".