Loss of Cellular Inhibitor of Apoptosis Protein 2 Reduces Atherosclerosis in Atherogenic apoE <sup>−/−</sup> C57BL/6 Mice on High‐Fat Diet
Bibliographic record
Abstract
BACKGROUND: Cellular inhibitor of apoptosis protein 2 (cIAP2) is predicted to participate in atherosclerosis; however, its direct role in atherosclerosis development has not been investigated. We aimed to examine and assess the loss of cIAP2 on atherosclerosis lesion development. METHODS AND RESULTS: We used apoE-/- C57BL/6 male mice, either cIAP2-/- or cIAP2+/+. At 8 weeks, mice were fed a high-fat diet (HFD) for 4 and 12 weeks. Aortic root was serially sectioned and stained with Sudan IV, CD68, α-actin, and terminal deoxynucleotidyl transferase dUTP nick end labeling (TUNEL). cIAP2-/- mice displayed a significant decrease in atherosclerotic lesion's macrophage number after 4 weeks of HFD. Similarly, decrease in lesion area at 4 and 12 weeks HFD was detected by use of en face analysis (cIAP2-/- 0.58 ± 0.37% versus cIAP2+/+ 1.51 ± 0.79% [P = 0.0056]); (cIAP2-/- 9.34 ± 4.88% versus cIAP2+/+ 17.65 ± 6.24% [P = 0.0019]). Aortic root lesion area after 4 and 12 weeks of HFD also decreased (cIAP2-/- 0.0328 ± 0.014 mm2 versus cIAP2+/+ 0.0515 ± 0.021 mm2 [P = 0.022]); (cIAP2-/- 0.3614 ± 0.1157 mm2 versus cIAP2+/+ 0.4901 ± 0.125 mm2 [P = 0.065]). TUNEL analysis after 4 and 12 weeks of HFD showed a 2.5-fold increase in TUNEL+ cells (cIAP2-/- 4.47 ± 2.26% versus cIAP2+/+ 1.74 ± 0.98% [P = 0.036]); (cIAP2-/- 2.39 ± 0.75% versus cIAP2+/+ 1.29 ± 0.47% [P = 0.032]). Smooth muscle cell content in cIAP2-/- mice was 3.075 ± 3.3% compared with cIAP2+/+ with 0.085 ± 0.1% (P = 0.0071). CONCLUSIONS: Results uncover a key role for cIAP2 in atherosclerotic lesion development, and targeting it may represent a novel therapeutic strategy.
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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 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.002 |
| 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".