Cholate-Containing High-Fat Diet Induces the Formation of Multinucleated Giant Cells in Atherosclerotic Plaques of Apolipoprotein E−/− Mice
Bibliographic record
Abstract
OBJECTIVE: To determine the role of multinucleated giant cells (MGCs) in cardiovascular diseases. METHODS AND RESULTS: MGCs are a hallmark of giant cell arteritis. They are also described in atherosclerotic plaques from aortic aneurysms and carotid and coronary arteries. Herein, we demonstrate that the cholate-containing Paigen diet yields many MGCs in atherosclerotic plaques of apolipoprotein E-/- mice. These mice revealed a 4-fold increase in MGC numbers when compared with mice on a Western or Paigen diet without cholate. Most of the MGCs stained intensively for cathepsin K and were located at fibrous caps and close to damaged elastic laminae, with associated medial smooth muscle cell depletion. During in vitro experiments, MGCs demonstrated a 6-fold increase in elastolytic activity when compared with macrophages and facilitated transmigration of smooth muscle cells through a collagen-elastin matrix. An elastin-derived hexapeptide (Val-Gly-Val-Ala-Pro-Gly [VGVAPG]) significantly increased the rate of macrophage fusion, providing a possible mechanism of in vivo MGC formation. Comparable to the mouse model, human specimens from carotid arteries and aortic aneurysms contained cathepsin K-positive MGCs. CONCLUSIONS: Apolipoprotein E-/- mice fed a Paigen diet provide a model to analyze the tissue-destructive role of MGCs in vascular diseases.
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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 | 0.001 | 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 | 0.001 |
| 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".