Specific loss of Toll-like receptor 2 on bone marrow derived cells decreases atherosclerosis in LDL receptor null mice*The senior author, Stewart C. Whitman, passed away on 19 February 2010. The manuscript has been communicated by Ross W. Milne (e-mail: rmilne@ottawaheart.ca) and Yves L. Marcel (e-mail: ylmarcel@ottawaheart.ca), University of Ottawa Heart Institute, 40 Ruskin Street, Ottawa, ON K1Y 4W7, Canada.
Bibliographic record
Abstract
Innate immunity and, notably, Toll-like receptors (TLR), have an important role in atherogenesis. We have tested the hypothesis that the selective loss of TLR-2 by cells of bone marrow (BM) origin will protect low-density receptor-deficient (Ldlr (-/-)) mice from both early- and late-stage atherosclerosis. BM cells from Tlr2(+/+) and Tlr2(-/-) littermates were used to reconstitute lethally irradiated Ldlr(-/-) mice. Following a recovery period, mice were placed either on a diet containing 21% saturated fat - 0.15% cholesterol for 8 weeks to study early-stage atherosclerosis, or on a diet richer in cholesterol (1.5%) for 16 weeks to study late-stage atherosclerosis. Donor cell Tlr2 genotype did not alter serum cholesterol levels or lipoprotein profiles in recipient animals. After 8 weeks on the 0.15% cholesterol diet, deficiency of TLR-2 expression on cells of BM origin reduced atherosclerosis in the aortic root and the aortic arch in both genders of mice. In contrast, the BM recipients who received the 1.5% cholesterol diet for 16 weeks showed much larger lesions in the aortic root, and TLR-2 deficiency in BM cells failed to provide protection. Thus, TLR-2 expression in BM-derived cells contributes primarily to early stage 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 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".