Muscle contraction increases lactate transport while reducing sarcolemmal MCT4, but not MCT1
Bibliographic record
Abstract
Rates of lactate uptake into giant sarcolemmal vesicles were determined in vesicles collected from rat muscles at rest and immediately after 10 min of intense muscle contraction. This contraction period reduced muscle glycogen rapidly by 37-82% in all muscles examined (P < 0.05) except the soleus muscle (no change P > 0.05). At an external lactate concentration of 1 mM lactate, uptake into giant sarcolemmal vesicles was not altered (P > 0.05), whereas at an external lactate concentration of 20 mM, the rate of lactate uptake was increased by 64% (P < 0.05). Concomitantly, the plasma membrane content of monocarboxylate transporter (MCT)1 was reduced slightly (-10%, P < 0.05), and the plasma membrane content of MCT4 was reduced further (-25%, P < 0.05). In additional studies, the 10-min contraction period increased the plasma membrane GLUT4 (P < 0.05) while again reducing MCT4 (-20%, P < 0.05) but not MCT1 (P > 0.05). These studies have shown that intense muscle contraction can increase the initial rates of lactate uptake, but only when the external lactate concentrations are high (20 mM). We speculate that muscle contraction increases the intrinsic activity of the plasma membrane MCTs, because the increase in lactate uptake occurred while plasma membrane MCT4 was decreased and plasma membrane MCT1 was reduced only minimally, or not at all.
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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) | 0.002 | 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".