Wall Tissue Remodeling Regulates Longitudinal Tension in Arteries
Bibliographic record
Abstract
Changes in blood pressure or flow induce arterial remodeling that normalizes mechanical loads that are imposed on arterial tissue. Arteries are also under substantial longitudinal stretch (axial strain) that may be altered by growth or atrophy of tissues to which they are attached. We therefore tested whether axial strain is also regulated in a negative feedback manner through arterial remodeling. Axial strain in rabbit carotid arteries was increased from 62+/-2% to 97+/-2% without altering other mechanical loads on wall tissues. Strain was reduced within 3 days and completely normalized by 7 days. Remodeling involved tissue elaboration, endothelial cell replication rates were increased by >50-fold and smooth muscle cell replication rates were increased by >15-fold, and substantially elevated DNA, elastin, and collagen contents were recorded. Also, increased rates of apoptosis were indicated by degradation of DNA into oligonucleosomes, and matrix remodeling was reflected in enlarged fenestrae in the internal elastic lamina and increased expression and activation of gelatinases, especially matrix metalloproteinase-2. Intriguingly, reduced axial strain was not normalized, presumably because remodeling processes, apart from cell contraction, are ineffective in decreasing strain, and arterial smooth muscle orientation precludes large effects of contraction on axial strain.
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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 | 0.000 | 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".