Leukotriene B4 Receptor Antagonism Reduces Monocytic Foam Cells in Mice
Bibliographic record
Abstract
Leukotriene B4 (LTB4) is a potent chemotactic agent that activates monocytes through the LTB4 receptor (BLTR). We tested the hypothesis that LTB4 receptor blockade would slow atherosclerotic progression by inhibiting monocyte recruitment. Homozygous low-density receptor knockout (LDLr(-/-)) mice and apolipoprotein E deficient (apoE(-/-)) mice were treated with a specific LTB4 receptor antagonist, CP-105,696, for 35 days. In apoE(-/-)mice, treatment with the LTB4 antagonist did not affect plasma lipid concentrations but significantly reduced CD11b levels both in vascular lesions and whole blood. Compared with age-matched controls, lipid accumulation and monocyte infiltration were significantly reduced in treated apoE(-/-) mice at all time points tested. Lesion area reduction was also demonstrated in LDLr(-/-) mice maintained on a high-fat diet. LTB4 antagonism had no significant effect on lesion size in mice possessing the null alleles for another chemotactic agent, monocyte chemoattractant protein-1 (MCP-1(-/-)xapoE(-/-)), suggesting MCP-1 and LTB4 may either interact or exert their effects by a common mechanism. These results demonstrate that in a preclinical model of atherosclerosis LTB4 receptor blockade reduces lesion progression and further suggest a previously unrecognized role for LTB4 or other oxidized lipids recognized by the BLTR receptor in the pathogenesis of this disease.
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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 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 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".