Analysis of Intimal Proteoglycans in Atherosclerosis-prone and Atherosclerosis-resistant Human Arteries by Mass Spectrometry
Bibliographic record
Abstract
The propensity to develop atherosclerosis varies markedly among different sites in the human vasculature. To determine a possible cause for such differences in atherosclerosis susceptibility, a proteomics-based approach was used to assess the extracellular proteoglycan core protein composition of intimal hyperplasia from both the atherosclerosis-prone internal carotid artery and the atherosclerosis-resistant internal thoracic artery. The intimal proteoglycan composition in these preatherosclerotic lesions was found to be more complex than previously appreciated with up to eight distinct core proteins present, including the large extracellular proteoglycans versican and aggrecan, the basement membrane proteoglycan perlecan, the class I small leucine-rich proteoglycans biglycan and decorin, and the class II small leucine-rich proteoglycans lumican, fibromodulin, and prolargin/PRELP (proline arginine-rich end leucine-rich repeat protein). Although most of these proteoglycans seem to be present in similar amounts at the two locations, there was a selective enhanced deposition of lumican in the intima of the atherosclerosis-prone internal carotid artery compared with the intima of the atherosclerosis-resistant internal thoracic artery. The enhanced deposition of lumican in the intima of an atherosclerosis prone artery has important implications for the pathogenesis of atherosclerosis.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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".