Recombinant Decorin Fusion Protein Attenuates Murine Abdominal Aortic Aneurysm Formation and Rupture
Bibliographic record
Abstract
Decorin (DCN) is a small-leucine rich proteoglycan that mediates collagen fibrillogenesis, organization, and tensile strength. Adventitial DCN is reduced in abdominal aortic aneurysm (AAA) resulting in vessel wall instability thereby predisposing the vessel to rupture. Recombinant DCN fusion protein CAR-DCN was engineered with an extended C-terminus comprised of CAR homing peptide that recognizes inflamed blood vessels and penetrates deep into the vessel wall. In the present study, the role of systemically-administered CAR-DCN in AAA progression and rupture was assessed in a murine model. Apolipoprotein E knockout (ApoE-KO) mice were infused with angiotensin II (AngII) for 28 days to induce AAA formation. CAR-DCN or vehicle was administrated systemically until day 15. Mortality due to AAA rupture was significantly reduced in CAR-DCN-treated mice compared to controls. Although the prevalence of AAA was similar between vehicle and CAR-DCN groups, the severity of AAA in the CAR-DCN group was significantly reduced. Histological analysis revealed that CAR-DCN treatment significantly increased DCN and collagen levels within the aortic wall as compared to vehicle controls. Taken together, these results suggest that CAR-DCN treatment attenuates the formation and rupture of Ang II-induced AAA in mice by reinforcing the aortic wall.
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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 | 0.000 | 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 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
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".