Improved assessment of the global transcriptional response to endurance exercise in human skeletal muscle
Bibliographic record
Abstract
Endurance exercise is a potent stimulus for mitochondrial biogenesis and cellular metabolism. While the molecular events surrounding an acute bout of endurance exercise have been previously evaluated using small‐scale approaches, recent advances in gene microarray technology have substantially improved the number of gene targets available for investigation. Therefore, the purpose of this study was to examine the global mRNA response to a single bout of endurance exercise. Eleven healthy, young men (age 22±1 yrs, VO 2peak 46±2 ml·kg −1 ·min −1 ) were recruited for this investigation. Muscle samples were acquired prior to and at 30 mins and 3 hours following an exhaustive cycling protocol. At 30 mins post‐exercise 404 genes were differentially expressed from rest (P<0.05), while at 3h 846 genes were significantly altered (P<0.05). When clustered by biological function, the greatest enrichment of genes at 30 mins occurred in those relating to developmental processes, cell differentiation, DNA binding, negative regulation of cellular processes, and primary metabolic processes. At 3h, functional annotation revealed clusters related to developmental processes, signal transduction, anatomical structure development, DNA binding and the regulation of apoptosis. To date, these data provide the most comprehensive transcriptional approach regarding the acute effects of endurance exercise. (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".