Relationship between skeletal muscle MCT1 and accumulated exercise during voluntary wheel running
Bibliographic record
Abstract
We examined whether the quantity of exercise performed influences the expression of monocarboxylate transporter (MCT) 1 and MCT4 in mouse skeletal muscles (plantaris, tibialis anterior, soleus) and heart. Wheel running exercise (1, 3, and 6 wk) was used, which results in marked variations in self-selected running activity. Differences in muscle MCT1 and MCT4 among animals, before the initiation of running, were not related to the quantity of exercise performed on the first day of wheel running. No changes in MCT4 were observed over the course of the study (P > 0.05). After 6 wk of running, were there significant increases in heart (50%; P < 0.05) and muscle MCT1 (31-60%; P < 0.05) but not after 1 and 3 wk (P > 0.05). Because skeletal muscle MCT1 and running distances varied considerably, we examined the relationship between these two parameters. Within the first week of training, MCT1 was negatively correlated with the accumulated running distance (r = -0.70, P < 0.05). On further analysis, it appears that, in the first week, excessive running (>20 km/wk) represses MCT1 (-16.1%; P < 0.05), whereas more modest amounts of running (<20 km/wk) increase MCT1 (+37%; P < 0.05). After 3 wk of running, a positive relationship was observed between MCT1 and running distance (r = +0.76), although there is a threshold that must be exceeded before an increase over the control animals occurs. Finally, in week 6, when MCT1 was increased in the tibialis anterior and plantaris muscles, there were no correlations with the accumulated running distances. These studies have shown that mild exercise training fails to increase MCT4 and that changes in MCT1 are complex, depending not only the accumulated exercise but also on the stage of training.
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".