Exercise- and Insulin-Stimulated Muscle Glucose Transport: Distinct Mechanisms of Regulation
Bibliographic record
Abstract
In mammals, skeletal muscle is the primary target for the stimulation of glucose transport by a variety of activators. These include the hormone insulin and stimuli which increase energy demand such as exercise, hypoxia, and challenges to the oxidative chain. While it is known that both stimuli rapidly elevate glucose uptake into muscle by signalling the translocation of glucose transporters from intracellular stores to the plasma membrane, there are numerous contrasts between energy stressors and insulin in their mechanisms of glucose transport activation. Exercise and insulin recruit distinct intracellular pools of glucose transporters in skeletal muscle and the maximal effects of contraction and insulin are additive. Activation of phosphatidylinositol 3-kinase (PI3-K) is utilized by insulin to induce glucose transporter translocation, but does not participate in the responses to exercise or hypoxia. These findings suggest that energy stressors utilize different mechanisms from insulin to increase glucose influx; however, how these factors elicit their response is not clear. This review will summarize our current knowledge of these alternative pathways of glucose transport regulation. Emphasis is placed on the use of the mitochondrial uncoupler dinitrophenol to investigate mediators of this alternative signalling pathway in L6 muscle cells, a line used to characterize physiological responses in muscle such as glucose transport.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.002 |
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".