Comparative expression profiling of 50–60 year old male competitive athletes and lean healthy individuals
Bibliographic record
Abstract
The current study examined genetic and metabolic adaptations to aerobic exercise in highly‐trained masters athletes (MA; n = 9; age, 53 ± 3 yrs; BMI, 24 ± 3 kg/m 2 ; VO 2 peak, 59.1 ± 5.2 ml·kg −1 ·min −1 ) and age and BMI matched controls (CON; n = 8; 54 ± 5 yrs; 25 ± 3 kg/m 2 ; 35.9 ± 9.7 ml·kg −1 ·min −1 ). All participants performed a 45 minute endurance ride at 60% of their VO 2 peak followed by cycling at 90% VO 2 peak to fatigue. Blood was sampled before, immediately after, and 24 hours after exercise. Fasted insulin (MA, 18.1 ± 4.3; CON, 32.6 ± 13.8 pmol/L), HDL (MA, 1.91 ± 0.49; CON 1.28 ± 0.26 mmol/L), and lipid ratio (3.00 ± 0.62; CON 4.16 ± 0.86) were different between groups. MA also demonstrated an augmented insulin response to exercise compared to CON. A genome‐wide DNA microarray analysis of RNA from blood samples revealed that a variety of genes involved in insulin activity, cardiovascular and metabolic functions were significantly different between the two groups. These differences will be confirmed for individual changes in gene expression by quantitative real‐time PCR. These results may lead to new insights into signaling pathways that control the beneficial effects of exercise in older men, and may help to identify surrogate markers for monitoring exercise and training load. This research was supported by NSERC.
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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".