Abstract 582: The CCR7+ PDGFRa+ Cell Population is Increased in Regressing Atherosclerotic Plaques
Bibliographic record
Abstract
Introduction: Atherosclerosis is characterized by the accumulation of lipids, cells and fibers in the arterial wall. Atherosclerotic plaques form in regions of low blood flow, whereas vessels exposed to high blood flow remain lesion-free. We created a surgical mouse model, arteriovenous fistula (AVF), which increases blood flow in the brachiocephalic artery (BCA) specifically, without altering serum lipid levels. Methods & Results: LDLR KO mice were placed on a high-fat diet (HFD). Control mice were sacrificed at week 12. Sham and AVF surgery was performed at week 12 and mice were kept on a HFD for a further 1-4 weeks. We found that high blood flow is beneficial and leads to a significant ~50% regression of BCA plaque size in AVF mice compared with Controls, by week 4. We performed flow cytometry to characterize the different cell populations within Sham and AVF plaques. At day 7 after surgery, there was no difference in macrophage (F4/80+) or dendritic cell (CD11c+) content between Sham and AVF. However, we found a significant, 4-fold increase in the total number of CD45-/CCR7+/PDGFRa+ cells in the BCA plaques of AVF mice vs Shams (p<0.01). No such change was observed in the aortic sinus plaques of AVF or Shams. CCR7 was previously found to be overexpressed in regressing plaques upon an abrupt lowering of plasma lipids, but in CD45+ cells. In our model, plasma lipids remained high and CCR7+ cells instead expressed PDGFRa, a perivascular and multi-lineage differentiation marker. This cell population also expressed mesenchymal stem cell markers (CD90, CD44, CD34) and CD68. Conclusion: Our data point to an unexpected increase in the CD45-/CCR7+/PDGFRa+ cell population in the early plaque regression process. They suggest that mesenchymal-type cells may promote regression in plaques exposed to high blood flow.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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) | 0.000 | 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 | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 0.002 |
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".