Differential impact of three β‐adrenergic receptor antagonists on the vascular dysfunction of atherosclerotic mice
Bibliographic record
Abstract
β‐adrenergic receptor (β‐AR) antagonists reduce heart rate (HR) but differentially alter intracellular pathways in vitro through bias signaling. To assess the functional consequences of these diverse pharmacological properties, we treated for 3 months (n=7 per group) or not (n=6) 4‐mo atherosclerotic LDLr −/− :hApoB +/+ (ATX) male mice with carvedilol (CARV, β‐AR antagonist and partial α 1 ‐AR antagonist; 25mg/kg/d), nebivolol (NEBI, β 1 ‐AR antagonist with antioxidant property; 5mg/kg/d) or propranolol (PROP, β‐AR antagonist; 100mg/kg/d). Reduction (~15%) of HR at 2 weeks was similar. In pressurized cerebral arteries, CARV prevented endothelial dysfunction by improving endothelium‐dependent flow‐mediated dilations (FMD) by 57% (P<0.05) and normalized wall stiffness (P<0.05). In contrast, PROP and NEBI worsened FMD by 54% and 41% (P<0.05) and did not correct stiffness. In addition, stiffening of carotid arteries was only limited by NEBI (P<0.05), while acetylcholine‐induced endothelium‐dependent relaxation was similar in all groups. We conclude that chronic in vivo β‐AR antagonisms lead to differential vascular responses that could lead to long‐term differential clinical outcomes. CIHR MOP89733 .
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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 | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 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".