Comparative expression profiling of 50–60 year old male competitive athletes and lean healthy individuals
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| 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) | 0.000 | 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 it