Abstract 669: SR-BI in Bone Marrow--Derived Cells Protects Mice From Diet-Induced Coronary Artery Atherosclerosis and Myocardial Infarction
Bibliographic record
Abstract
Background: SR-BI deficient, apolipoprotein E hypomorphic mice develop diet induced occlusive coronary artery atherosclerosis, myocardial infarction and early death. Methods: To test the role of SR-BI in bone marrow derived cells, SR-BI-null; apoE- hypomorphic mice were transplanted with SR-BI +/+ apoE-hypomorphic, or control, autologous SR-BI-null; apoE-hypomorphic bone marrow. Four weeks later, mice were fed a high-fat, high-cholesterol, cholate-containing diet to induce coronary artery atherosclerosis. Results: Mice transplanted with autologous bone marrow developed extensive aortic atherosclerosis and severe occlusive coronary artery atherosclerosis after 4 weeks of feeding. This was accompanied by myocardial fibrosis and increased heart weights. In contrast, restoration of SR-BI expression in bone marrow derived-cells reduced diet induced aortic and coronary artery atherosclerosis, myocardial fibrosis and the increase in heart weights in SR-BI-null; apoE-hypomorphic mice. Restoration of SR-BI in bone marrow derived cells did not, however, affect steady state lipoprotein cholesterol levels, but reduced plasma levels of IL-6. Monocytes from SR-BI-null mice exhibited a greater capacity to bind to VCAM-1 and ICAM-1 than those from SR-BI +/+ mice. Furthermore, restoration of SR-BI expression in bone marrow derived cells attenuated monocyte recruitment into atherosclerotic plaques in mice fed high fat, high cholesterol cholate containing diet. Conclusion: These data demonstrate directly that SR-BI in bone marrow-derived cells protects against both aortic and coronary artery 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) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 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".