IFN- <i>γ</i> Deficiency Exerts Gender-Specific Effects on Atherogenesis in Apolipoprotein E <sup>-/-</sup> Mice
Bibliographic record
Abstract
We have shown recently that administration of exogenous interferon-gamma (IFN-gamma) to apolipoprotein E (apoE)(-/-) mice augmented atherogenesis. In the present study, we examined whether deficiency of endogenous IFN-gamma would reduce atherosclerosis in apoE(-/-) mice. Compound-deficient mice were generated by crossing strain-matched IFN-gamma(-/-) and apoE(-/-) mice and comparing them to apoE(-/-) mice. Groups of both genders were fed either a normal or a high-fat diet. IFN-gamma deficiency did not affect serum cholesterol concentrations or lipoprotein-cholesterol distributions in any groups. IFN-gamma deficiency had no effect on serum triglyceride concentrations, except for an increase noted in males fed a normal diet. The extent of atherosclerosis was determined in tissue sections of the ascending aorta and on the surface of the aortic arch. During feeding of normal diets, IFN-gamma deficiency had no effect on the extent of atherosclerosis in female mice in either vascular bed. In contrast, in male mice fed normal diet, IFN-gamma deficiency markedly decreased lesion size in both vascular beds. During feeding of high-fat diets, IFN-gamma deficiency also had no effect on lesion size in females but profoundly decreased lesion size in the aortic root of male mice. IFN-gamma deficiency had no effect on the abundance of T lymphocytes or MHC class II-positive cells in aortic root lesions of females. By comparison, both these parameters were reduced in lesions of male mice. Therefore, IFN-gamma deficiency decreased atherogenesis, potentially by decreasing T lymphocyte presence and cell activation, without influencing serum cholesterol concentrations. However, this effect is strikingly restricted to male mice.
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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) | 0.000 | 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) | 0.002 | 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".