An acute bout of high-intensity interval training increases the nuclear abundance of PGC-1α and activates mitochondrial biogenesis in human skeletal muscle
Bibliographic record
Abstract
Low-volume, high-intensity interval training (HIT) increases skeletal muscle mitochondrial capacity, yet little is known regarding potential mechanisms promoting this adaptive response. Our purpose was to examine molecular processes involved in mitochondrial biogenesis in human skeletal muscle in response to an acute bout of HIT. Eight healthy men performed 4 × 30-s bursts of all-out maximal intensity cycling interspersed with 4 min of rest. Muscle biopsy samples (vastus lateralis) were obtained immediately before and after exercise, and after 3 and 24 h of recovery. At rest, the majority of peroxisome proliferator-activated receptor γ coactivator (PGC)-1α, a master regulator of mitochondrial biogenesis, was detected in cytosolic fractions. Exercise activated p38 MAPK and AMPK in the cytosol. Nuclear PGC-1α protein increased 3 h into recovery from exercise, a time point that coincided with increased mRNA expression of mitochondrial genes. This was followed by an increase in mitochondrial protein content and enzyme activity after 24 h of recovery. These findings support the hypothesis that an acute bout of low-volume HIT activates mitochondrial biogenesis through a mechanism involving increased nuclear abundance of PGC-1α.
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".