Remodelling and pathology development associated with aneurysmal ascending aortic tissues
Bibliographic record
Abstract
Abstract The human ascending aorta (AA) is exposed to very high shear and pressure stresses exerted by the blood flow ejected from the left ventricle outflow tract. This vessel has a unique structural behaviour which adequately redistributes the energy captured from the blood flow ejection to sustain a more continuous blood flow through the entire vascular system. Unfortunately, this vessel is prone to a pathological dilation process involving significant structural changes that can lead to fundamental modification of its mechanical behaviour and functions. Genetic and/or environmental factors have been implicated in the disease process. It is believed that in particular the forces created by blood flow (hemodynamics) can be a stimulus for vessel remodelling. For patients suffering from this deadly condition, surgical replacement or repair is the best solution to increase life expectancy. However, the replacement materials available as a treatment have a significant impact on the blood flow, the biomechanics of the aortic arch, and the entire vascular system. In this review we summarise the current understanding of the pathogenesis mechanisms involved in the dilation of the AA from a mechanical and biochemical point of view. We will also underline the needs for better replacement materials in surgical repair to improve graft patency.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".