Medial Collagen Type and Quantity Influence Mechanical Properties of Aneurysm Wall in Bicuspid Aortic Valve Patients
Bibliographic record
Abstract
Collagen has an essential role in aortic biomechanics, and collagen remodeling has been associated with the development and progression of aortic aneurysm. However, the exact mechanisms behind collagen remodeling and the biomechanical implications are not well understood. This study presents an investigation of the relationship between collagen remodeling in the aortic wall and biomechanics, by means of collagen assays, smooth muscle cell gene expression, and mechanical testing on human aortic specimens collected from patients with bicuspid aortic valve. Collagen assay analysis was employed to determine collagen-I and total collagen content; quantitative real-time PCR was used to determine amountsCOL1A1andCOL3A1expression in the tissue. These parameters were compared with the local biomechanical properties determined from biaxial and uniaxial tensile testing. Collagen-I content was found to relate to improved mechanical properties, while total collagen content did not exhibit a relationship with biomechanics.COL1A1andCOL3A1expression were found to relate to the collagen-I content of the tissue, but not the total collagen content or biomechanical performance. Relationships between variables appeared to be dependent on the collagen content in specific layers of the aortic wall. The effect of age is also noted, as total collagen content and biomechanics were found to have significant associations with increasing age, while collagen-I content and collagen gene expression did not exhibit any correlation. Varying relationships were observed when looking at younger versus older patients. Findings highlight the importance of type and location in determining the influence of collagen on aortic biomechanics, as well as the role of gene expression in the onset and progression of collagen remodeling in aortic aneurysm.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".