Adventitial Fibroblasts in Aortic Aneurysm: Unraveling Pathogenic Contributions to Vascular Disease
Bibliographic record
Abstract
Aortic aneurysm (AA) is a degenerative vascular disease that involves aortic dilatation, and, if untreated, it can lead to rupture. Despite its significant impact on the healthcare system, its multifactorial nature and elusive pathophysiology contribute to limited therapeutic interventions that prevent the progression of AA. Thus, further research into the mechanisms underlying AA is paramount. Adventitial fibroblasts are one of the key constituents of the aortic wall, and they play an essential role in maintaining vessel structure and function. However, adventitial fibroblasts remain understudied when compared with endothelial cells and smooth muscle cells. Adventitial fibroblasts facilitate the production of extracellular matrix (ECM), providing structural integrity. However, during biomechanical stress and/or injury, adventitial fibroblasts can be activated into myofibroblasts, which move to the site of injury and secrete collagen and cytokines, thereby enhancing the inflammatory response. The overactivation or persistence of myofibroblasts has been shown to initiate pathological vascular remodeling. Therefore, understanding the underlying mechanisms involved in the activation of fibroblasts and in regulating myofibroblast activation may provide a potential therapeutic target to prevent or delay the progression of AA. This review discusses mechanistic insights into myofibroblast activation and associated vascular remodeling, thus illustrating the contribution of fibroblasts to the pathogenesis of AA.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| 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".