Manipulating training intensity and volume in already well-trained rats: effect on skeletal muscle oxidative and glycolytic enzymes and buffering capacity
Bibliographic record
Abstract
Well-trained endurance athletes undergo periods of high-intensity interval training (HIT) or high-volume training (HVT) to improve exercise performance, but little is known about the mechanistic changes that occur during this time. The purpose of this study was to examine the influence of HIT and HVT on the activities of citrate synthase (CS) and phosphofructokinase (PFK), and on intramuscular buffering capacity (betam) in already well-trained rats. At 4 weeks of age, Wistar rats were divided into sedentary (SED; n=18) and exercise training groups (n=38). Following a 10 week preliminary training program, trained rats were divided randomly into 3 further groups that completed 4 additional weeks of continued endurance (CON, n=14), high-intensity training (HIT, n=12), or high-volume training (HVT, n=12). Soleus (SOL), red and white gastrocnemius (RG and WG), and red and white vastus (RV and WV) muscles were removed 24-48 h after a final run-to-fatigue performance test (30 m.min(-1) 25% grade) to determine the activities of CS, PFK, and betam. No differences in run time to exhaustion were found between the groups. However the HIT group possessed CS and PFK activities and betam in WV muscle that were 60%, 24%, and 10% higher, respectively (all p<0.05), compared with the HVT group; differences were not found between the HIT and CON groups. Although no differences in run performance were found, HIT compared with HVT in already well-trained rats resulted in significantly higher oxidative and glycolytic capacities of fast-contracting fibres. No differences were shown in fast-contracting muscle between HIT and CON.
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) | 0.001 | 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 | 0.001 |
| 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".