Prazosin Prevents the Glucocorticoid‐Induced Capillary Rarefaction in Skeletal Muscle
Bibliographic record
Abstract
Prolonged exposure to elevated glucocorticoids (GC) results in skeletal muscle capillary rarefaction, which may promote insulin resistance and limb ischemia. Conversely, treatment with the a1‐adrenergic receptor inhibitor prazosin increases muscle blood flow and stimulates angiogenesis. We hypothesized that prazosin treatment would prevent GC‐induced capillary rarefaction and improve markers of insulin resistance. Corticosterone (Cort) or placebo (wax) pellets (400mg/rat) were implanted subcutaneously in young male Sprague‐Dawley rats. Two days after, prazosin was administered in the animal's drinking water (50mg/L). Plasma levels of Cort correlated significantly with fasting plasma insulin levels (r=0.76), suggesting that sustained elevations in Cort resulted in insulin resistance; this relationship was unaffected by prazosin (r=0.78). Skeletal muscle capillary‐to‐fiber ratio (C:F) correlated inversely with fasted plasma insulin levels (r=‐0.77) and with plasma Cort levels (r=‐0.62). In 16‐day Cort‐treated animals, prazosin significantly increased mRNA levels of pro‐angiogenic VEGF‐A but not levels of anti‐angiogenic thrombospondin‐1 (TSP1), thus resulting in an elevated ratio of VEGF‐A to TSP1. Most notably, the Cort‐induced capillary rarefaction (2.0 + 0.08 vs. 1.6 + 0.08) was prevented with prazosin treatment (1.6 + 0.08 vs. 2.0 + 0.11). Fasting plasma insulin increased dramatically with Cort (0.51 + 0.02 vs. 4.1 + 0.5ng/ml, P <0.05), and improved slightly with prazosin treatment (4.1 + 0.5 vs. 3.7 + 0.3ng/ml, P <0.05). This study demonstrates the potential benefits of prazosin in preventing the GC‐induced loss of capillaries and lowering fasting plasma insulin levels (Funding: NSERC and HSF Canada).
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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 | 0.001 |
| 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".