Serial analysis of gene expression in the skeletal muscle of endurance athletes compared to sedentary men
Bibliographic record
Abstract
Physical exercise produces several adaptive changes in skeletal muscle. However, the molecular mechanisms of these effects are poorly understood. We performed serial analysis of gene expression (SAGE) to quantify the global gene expression profile in sedentary and endurance-trained muscle. A total of 10869 SAGE tags was sequenced and represented 4727 genes. The genes most expressed in muscle are mainly involved in contraction and energy metabolism. Thirty-three genes were differentially expressed between endurance athletes and sedentary individuals. Four genes such as myosin binding protein C fast-type, glycogen phosphorylase, and pyruvate kinase were expressed less in endurance athletes, whereas eight genes coding for expressed sequence tag similar to (EST) crystallin alpha B, EST myosin light chain 2, EST surfactant pulmonary-associated protein A1, EST thrombospondin, EST fructose-bisphosphate aldolase A, EST cytochrome oxidase 1, NADH dehydrogenase 3, and G8 protein were up-regulated. Most of the up-regulated tags corresponded to novel genes. On the other hand, different isoforms of fructose-bisphosphate aldolase A were also differentially expressed. The current study underlying the most highly expressed genes allows a better understanding of global muscle characteristics in normal and endurance-trained individuals. Moreover, the current data suggest novel candidate genes that may be responsible for enhanced endurance performance.
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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) | 0.001 | 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".