Tensile Residual Strains on the Elastic Lamellae along the Porcine Thoracic Aorta
Bibliographic record
Abstract
AIMS: This study determines the residual strains on the elastic lamellae in the porcine thoracic aorta to understand the distribution of strains amongst the components of the vascular wall. METHODS: Residual strains in aortic rings were released by cutting and purifying the elastin. Strains were calculated from lamellar contour lengths based on lamellar waviness and from mechanical tests. RESULTS: On the release of residual strains, waviness decreased 2-7%, the inner lamellae shortened 2.1 +/- 0.6% and the outer lamellae shortened 7.1 +/- 0.4% (p < 0.001), indicating that all lamellar elastin was under tension in fresh aortic tissue. Lamellar shortening was 3% greater in the distal than in the proximal tissue. Mechanical tests confirmed the morphological results and showed that the residual strains in fresh tissue required both the elastic tissue and the vascular smooth muscle. Tensile residual strains averaging 1.0% remained in the uncut elastin rings. CONCLUSION: When waviness is considered, the residual strains on the individual wall components can differ from the surface residual strains based only on the ring perimeter. The residual strains on the inner elastic lamellae are tensile, not compressive. The strain distribution amongst the individual components is non-uniform and not adequately understood to determine the physiological strains in the aortic wall.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 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 | 0.000 | 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".