Stem cell-based therapies for treatment of abdominal aortic aneurysm: development, application, and future potential
Bibliographic record
Abstract
Abstract Abdominal aortic aneurysms (AAA), with approximately 200,000 new diagnoses each year, represent a prevalent clinical concern. Current treatment includes monitoring and surgical procedures once the aneurysm reaches a certain size. However, the lack of effective, timely therapies leads to a high mortality rate due to rupture. With recent advancements and innovations in biomedical science, stem cell therapy has moved closer to widespread clinical use, with the field experiencing rapid growth since its inception in the late 20 th century. Given the pathophysiology of AAA, stem cell therapies have high potential impact in the treatment for both early and late-stage disease, targeting underlying mechanisms such as inflammation, vascular degeneration, and extracellular matrix degradation. There are many considerations and innovative potential approaches being explored in this type of treatment, such as strategically leveraging cell properties and their associated secretome and incorporating biomaterials-based strategies. This review article summarizes and critically assesses the efficacy of cell-based therapies in AAA preclinical models, current clinical trials in this area, and other emerging bioengineering approaches for the treatment of AAA.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".