Glucose transporter 4 and localisation in skeletal muscle : the effect of glucose and insulin administration, acute exercise and exercise training
Bibliographic record
Abstract
Glucose transporter 4 (GLUT4) in skeletal muscle plays a vital role in the maintenance of glucose homeostasis. Chapter 2 of this thesis develops an immunofluorescence microscopy method to generate novel information in human skeletal muscle on the effect of physiological stimuli on GLUT4 localisation, translocation to the plasma membrane and total protein content. Chapter 3 shows that training-induced increases in total GLUT4 protein content are driven by increases in the number of large and size of smaller intracellular GLUT4 storage clusters in human skeletal muscle. In chapter 4 the method successfully demonstrates GLUT4 translocation 30 min following glucose ingestion and 30 min after the start of moderate intensity cycling exercise in humans. GLUT4 translocation after glucose ingestion is transient and modest in comparison to the exercise response. Chapters 5 and 6 report no changes in GLUT4 translocation following an 80 min hyperinsulinaemic-isoglycaemic clamp in rats and a 2 h hyperglycaemic clamp in humans despite elevated rates of whole body glucose disposal in both experiments. This immunofluorescence method will be a valuable analytical tool in future studies investigating the mechanisms behind changes in muscle glucose uptake in response to obesity, age-related chronic diseases and therapeutic interventions including diet and exercise.
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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) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
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".