Hyperinsulinemia does not cause de novo capillary recruitment in rat skeletal muscle
Bibliographic record
Abstract
OBJECTIVE: The effect of insulin on blood flow distribution within muscle microvasculature has been suggested to be important for glucose metabolism. However, the "capillary recruitment" hypothesis is still controversial and relies on studies using indirect contrast-enhanced ultrasound (CEU) methods. METHODS: We studied how hyperinsulinemia effects capillary blood flow in rat extensor digitorum longus (EDL) muscle during euglycemic hyperinsulinemic clamp using intravital video microscopy (IVVM). Additionally, we modeled blood flow and microbubble distribution within the vascular tree under conditions observed during euglycemic hyperinsulinemic clamp experiments. RESULTS: Euglycemic hyperinsulinemia caused an increase in erythrocyte (80 ± 25%, P < .01) and plasma (53 ± 12%, P < .01) flow in rat EDL microvasculature. We found no evidence of de novo capillary recruitment within, or among, capillary networks supplied by different terminal arterioles; however, erythrocyte flow became slightly more homogenous. Our computational model predicts that a decrease in asymmetry at arteriolar bifurcations causes redistribution of microbubble flow among capillaries already perfused with erythrocytes and plasma, resulting in 25% more microbubbles flowing through capillaries. CONCLUSIONS: Our model suggests increase in CEU signal during hyperinsulinemia reflects a redistribution of arteriolar flow and not de novo capillary recruitment. IVVM experiments support this prediction showing increases in erythrocyte and plasma flow and not capillary recruitment.
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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) | 0.000 | 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".