Abstract 487: Systemic Administration of Target-Seeking, Vessel Penetrating Recombinant Decorin Fusion Protein, Car-DCN, Reduces Severity of Abdominal Aortic Aneurysm in Mice
Bibliographic record
Abstract
Objective: Decorin (DCN) is a small leucine-rich proteoglycan that mediates collagen fibrollogenesis, organization, and tensile strength. DCN is reduced in abdominal aortic aneurysm (AAA) through a Granzyme B-dependent mechanism resulting in vessel wall instability and aneurysm formation. A recombinant decorin fusion protein CAR-DCN was engineered with an extended C-terminus comprised of CAR homing peptide that recognizes inflammatory blood vessels and penetrates deep into the vessel wall. In the present study, we sought to evaluate the role of systemically administered CAR-DCN in AAA progression and rupture rate in a murine model. Approach and Results: To induce aneurysm, apolipoprotein E knockout (ApoE-KO) mice were infused with 28 days of angiotensin II (AngII). CAR-DCN or vehicle was systemically administrated until day 15. We observed a significant increase in the survival of CAR-DCN-treated mice (93%) compared to vehicle controls (60%). Although the incidence of AAA onset was similar between vehicle and CAR-DCN groups, the severity of aneurysm in the CAR-DCN group was significantly reduced. Furthermore, histological analysis revealed that CAR-DCN treatment significantly increased DCN and collagen levels in aortic walls compares to vehicle controls. Conclusions: CAR-DCN administration attenuated the severity of Ang II-induced AAA in mice by reinforcing the vessel wall. CAR-DCN may represent a novel therapeutic strategy to attenuate AAA progression and rupture.
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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 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 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".