The Nature of Iron Deposits Differs between Symptomatic and Asymptomatic Carotid Atherosclerotic Plaques
Bibliographic record
Abstract
Iron within atherosclerotic plaque has been implicated as a catalyst of oxidative stress that causes progression of plaque, and plaque rupture. Iron is believed to accumulate within plaque by incorporation of erythrocytes following plaque rupture and hemorrhage. There is only indirect evidence to support this hypothesis. Plaque specimens were obtained from ten symptomatic and fifteen asymptomatic patients undergoing carotid endarterectomy at a single institution. Plaques were sectioned for study using synchrotron radiation induced X-ray fluorescence the study the distribution of zinc, calcium and iron. Histologic staining was carried out with Prussian Blue, and immunohistochemical staining was done to localize macrophages with CD68. Data were compared against patient clinical variables. Ten symptomatic (15 ± 10 days between index symptoms and surgery) and fifteen asymptomatic carotid plaques were studied. Zinc and calcium co-localized in mineralized areas of symptomatic and asymptomatic plaque. Iron was identified away from zinc and calcium in both symptomatic and asymptomatic plaques. Within the symptomatic plaques, iron was found within the thrombus associated with plaque rupture and hemorrhage. It did not stain with Prussian Blue, but was found in association with CD68 positive macrophages. In symptomatic plaques, the abundance of iron showed an association with the source patient's LDL cholesterol (R2 = 0.39, Significance F = 0.05). Iron in asymptomatic plaque was present as hemosiderin/ferritin that stained positive with Prussian Blue, and was observed in association with CD68 positive macrophages. Iron in acutely symptomatic plaques is found within thrombus, in the presence of macrophages. The abundance of iron in symptomatic plaques is associated with the source patient's LDL cholesterol. Within asymptomatic plaques, iron is found in association with macrophages, as hemosiderin/ferritin.
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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 | 0.001 | 0.002 |
| 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 | 0.001 |
| 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".