Activation of AMP‐activated protein kinase (AMPK) causes vasorelaxation in isolated mesenteric arteries of hypertensive and normotensive rats
Bibliographic record
Abstract
Previous studies have shown that AMPK activation causes relaxation in isolated vessels from healthy animals but this has not been investigated in dysfunctional resistance arteries of hypertensive animals. Here we investigate the effects of AMPK activator 5‐aminoimidazole‐4‐carboxamide‐1‐β‐D‐ribofuranoside (AICAR) on resistance arteries from normotensive Wistar Kyoto rats (WKY) and Spontaneously Hypertensive rats (SHR). Mesenteric arteries were mounted on a small vessel myograph, pre‐contracted with 10 −5 M norepinephrine and exposed to increasing concentrations of AICAR (10 −6 , 10 −4 , 10 −2 M). Dose‐dependent relaxation occurred (~10% at 10 −6 M and ~90% at 10 −2 M [AICAR], respectively), and did not differ between SHR and WKY. Incubation with the NOS inhibitor Nω‐nitro‐L‐arginine methyl ester (L‐NAME, 10 −4 M) blunted relaxation in the WKY at 10 −6 M AICAR (by ~17% vs. CON, n=6, P<0.01), but did not affect relaxation at higher [AICAR]. Conversely, L‐NAME significantly blunted the relaxation response of SHR rings across all [AICAR] (by ~15–25% vs. CON, n=8, all P<0.01). Thus AICAR causes relaxation in both SHR and WKY mesenteric artery segments, but the relaxation response to AICAR is more NO‐dependent in vessels from SHR compared to WKY. Collectively these findings suggest that AMPK could influence the regulation of resistance vasculature in normotensive and hypertensive animals in an NO‐dependent manner.
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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) | 0.001 | 0.001 |
| 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 | 0.002 |
| 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".