Exercise training increases lipid metabolism gene expression in human skeletal muscle
Bibliographic record
Abstract
The effects of a single bout of exercise and exercise training on the expression of genes necessary for the transport and beta-oxidation of fatty acids (FA), together with the gene expression of transcription factors implicated in the regulation of FA homeostasis were investigated. Seven human subjects (3 male, 4 female, 28.9 +/- 3.1 yr of age, range 20-42 yr, body mass index 22.6 kg/m(2), range 17-26 kg/m(2)) underwent a 9-day exercise training program of 60 min cycling per day at 63% peak oxygen uptake (VO(2 peak); 104 +/- 14 W). On days 1 and 9 of the program, muscle biopsies were sampled from the vastus lateralis muscle at rest, at the completion of exercise, and again 3 h postexercise. Gene expression of key components of FA transport [FA translocase (FAT/CD36), plasma membrane-associated FA-binding protein], beta-oxidation [carntine palmitoyltransferase(CPT) I, beta-hydroxyacyl-CoA dehydrogenase] and transcriptional control [peroxisome proliferator-activated receptor (PPAR)alpha, PPAR gamma, PPAR gamma coactivator 1, sterol regulatory element-binding protein-1c] were unaltered by exercise when measured at the completion and at 3 h postexercise. Training increased total lipid oxidation by 24% (P < 0.05) for the 1-h cycling bout. This increased capacity for lipid oxidation was accompanied by an increased expression of FAT/CD36 and CPT I mRNA. Similarly, FAT/CD36 protein abundance was also upregulated by exercise training. We conclude that enhanced fat oxidation after exercise training is most closely associated with the genes involved in regulating FA uptake across the plasma membrane (FAT/CD36) and across the mitochondrial membrane (CPT I).
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.
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".