Interleukin-18 Enhances Atherosclerosis in Apolipoprotein E <sup>−/−</sup> Mice Through Release of Interferon-γ
Bibliographic record
Abstract
We have previously shown that interferon-gamma (IFN-gamma) is a potent enhancer of atherogenesis. Interleukin-18 (IL-18) promotes inflammatory responses through release of IFN-gamma, although it can also exert direct actions on other inflammatory mediators. In this present study, we determined the effects of IL-18 on atherogenesis and the role of IFN-gamma in this response. Male apolipoprotein E(-/-) mice (apoE(-/-); aged 16 weeks, n=10/group) were fed a normal diet and injected intraperitoneally for 30 days with either recombinant IL-18 (30 ng/g/day) or saline. Atherosclerotic lesion size was quantified in 2 vascular beds: the ascending aorta and the aortic arch. IL-18 administration did not affect serum cholesterol concentrations or lipoprotein-cholesterol distribution; however, exogenous IL-18 administration increased lesion size 2-fold in both the ascending aorta (50 642 +/- 12 515 versus 112 399 +/- 13 227 microm(2) P=0.004; saline versus IL-18 groups, respectively) and the aortic arch (3.1 +/- 0.3% versus 6.2 +/- 0.9% area, P=0.006). Exogenous IL-18 promoted a 4-fold increase in the number of lesion-associated T lymphocytes (11 +/- 3 versus 50 +/- 5 cells; P<0.0001) and cells expressing major histocompatability complex class II (9 +/- 3 versus 40 +/- 6 cells; P=0.0002). To determine the role of IFN-gamma production in this response, exogenous IL-18 was administered to apoE(-/-) mice that were IFN-gamma deficient. These studies demonstrated that lack of endogenous IFN-gamma ablated the effects of IL-18 on atherosclerosis. Therefore, these data strongly implicates IL-18 in the atherogenic process and suggests that IL-18 increases lesion development through enhancement of an inflammatory response involving an IFN-gamma-dependent mechanism. The full text of this article is available at http://www.circresaha.org.
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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 | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 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".