Angiotensin II type 1 receptor blocker inhibits arterial calcification in a pre-clinical model
Bibliographic record
Abstract
AIMS: Arterial calcification is a common complication of several disorders and is a strong predictor of mortality. The mechanism underlying arterial calcification is not fully understood and as such, no pharmaceutical therapies are currently available which impede its progression. The aim of this study was to investigate the effects of an angiotensin II (AngII) type 1 receptor blocker (ARB) on arterial calcification. METHODS AND RESULTS: Male New Zealand White rabbits were fed an atherogenic diet to induce atherosclerosis and arterial calcification over a period of 12 weeks, with an ARB administered in the final 4 weeks. Using clinically relevant micro-computed tomography, we found that animals fed the atherogenic diet displayed extensive arterial calcification when compared with control. In contrast, administration of the ARB completely inhibited calcification (2.80 ± 1.17 vs. 0.01 ± 0.01% calcified tissue in cholesterol and ARB-treated, respectively; n = 6 and 5; P < 0.05). Calcified regions were characterized by up-regulation of bone morphogenetic protein 2, osteocalcin, and the AngII type 1 receptor and concomitant down-regulation of α-smooth muscle actin, consistent with a phenotypic switch from vascular to osteoblast-like cells. CONCLUSION: These data provide the first evidence that angiotensin receptor blockade can inhibit arterial calcification by disrupting vascular osteogenesis and suggest that ARBs may be a novel treatment option for patients suffering from vascular calcification.
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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 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| 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 | 0.001 |
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".