A comparison of collagen levels in aorto-renal and aorto-intercostal atherosclerotic lesions in the cholesterol-fed rabbit
Bibliographic record
Abstract
Collagen is the most abundant protein found in mammals. Its primary role is to give strength to structures such as bone, tendon and arteries. Collagen develops in atherosclerotic lesions after infiltration of the intima by lipids and monocytes. The authors have developed a video-microdensitometry method to measure, precisely, the collagen mass fraction in sections of atherosclerotic lesion. Four rabbits were fed a declining low level cholesterol diet for 6 months. Lesions were produced primarily to the sides and downstream from branch junctions. Collagen levels were measured in the periorificial lesion of the left aorto-renal and 3rd aorto-intercostal branch sites. Each lesion was sampled in three locations: immediately lateral to both sides of the branch orifice, downstream from the orifice and at the edge of the lesion. The renal values for the downstream, lateral and edge sites were 16.8/spl plusmn/2.1 (SE)%, 16.7/spl plusmn/1.1%, and 17.0/spl plusmn/1.5% respectively, while the corresponding intercostal values were less for all sites, 12.58/spl plusmn/0.85%, 14.5/spl plusmn/1.3% and 10.67/spl plusmn/0.72%. The edge and downstream sites were significantly different between branches (p<0.05 by individual t-tests). The lateral measurements were not shown to be different (p=0.30). These results suggest that collagen is laid down in different amounts according to its position along the artery wall. Physical factors such as local stress/strain distributions may influence collagen production in atherosclerotic lesions.
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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 | 0.000 |
| 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".