Absence of Akt1 Reduces Vascular Smooth Muscle Cell Migration and Survival and Induces Features of Plaque Vulnerability and Cardiac Dysfunction During Atherosclerosis
Bibliographic record
Abstract
OBJECTIVE: Deletion of Akt1 leads to severe atherosclerosis and occlusive coronary artery disease. Vascular smooth muscle cells (VSMCs) are an important component of atherosclerotic plaques, responsible for promoting plaque stability in advanced lesions. Fibrous caps of unstable plaques contain less collagen and ECM components and fewer VSMCs than caps from stable lesions. Here, we investigated the role of Akt1 in VSMC proliferation, migration, and oxidative stress-induced apoptosis. In addition, we also characterized the atherosclerotic plaque morphology and cardiac function in an atherosclerosis-prone mouse model deficient in Akt1. METHODS AND RESULTS: Absence of Akt1 reduces VSMC proliferation and migration. Mechanistically, the proliferation and migratory phenotype found in Akt1-null VSMCs was linked to reduced Rac-1 activity and MMP-2 secretion. Serum starvation and stress-induced apoptosis was enhanced in Akt1 null VSMCs as determined by flow cytometry using Annexin V/PI staining. Immunohistochemical analysis of atherosclerotic plaques from Akt1(-/-ApoE-/-) mice showed a dramatic increase in plaque vulnerability characteristics such as enlarged necrotic core and reduced fibrous cap and collagen content. Finally, we show evidence of myocardial infarcts and cardiac dysfunction in Akt1(-/-ApoE-/-) mice analyzed by immunohistochemistry and echocardiography, respectively. CONCLUSIONS: Akt1 is essential for VSMC proliferation, migration, and protection against oxidative stress-induced apoptosis. Absence of Akt1 induces features of plaque vulnerability and cardiac dysfunction in a mouse model of atherosclerosis.
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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) | 0.000 | 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) | 0.002 | 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".