Near-Infrared Autofluorescence in Atherosclerosis Associates With Ceroid and Is Generated by Oxidized Lipid-Induced Oxidative Stress
Bibliographic record
Abstract
Objective: Near-infrared autofluorescence (NIRAF) of atherosclerosis associates with intraplaque hemorrhage and is detectable in living patients with coronary artery disease. However, further mechanisms underlying NIRAF generation have not been fully characterized. Here, we investigated the role of lipids and oxidative stress in NIRAF generation in atherosclerosis and in vitro in human macrophages. Approach and Results: In N=15 human carotid endarterectomy specimens, we investigated the spatial distribution of lipid, intraplaque hemorrhage, and NIRAF (ex/em 630/650 nm). Plaque NIRAF associated with both Sudan black-positive lipids ( r =0.53, P =0.023) and GPA (glycophorin A)-positive intraplaque hemorrhage ( r =0.48, P =0.043). Plaque NIRAF also localized with lipid and specifically insoluble lipid (ceroid) and iron. Intriguingly, some NIRAF-positive areas were Sudan black-positive but GPA-negative. Studies on human macrophages investigated further the role of lipids in NIRAF generation. OxLDL (Oxidized low-density lipoprotein) and hemoglobin, but not LDL, generated NIRAF in both THP-1 cells and monocyte-derived macrophages. In oxLDL-treated THP-1 cells, higher NIRAF, lipid peroxidation products, and intracellular oxidative stress markers evolved ( P <0.001 versus LDL). The antioxidants α-tocopherol and N-acetylcysteine suppressed NIRAF generation and oxidative stress. Conclusions: In human atherosclerosis and human macrophages in vitro, NIRAF colocalizes with lipid and specifically insoluble lipid or ceroid. In vitro studies further show that oxidized LDL generates NIRAF, oxidative stress, and lipid peroxidation products. These results demonstrate a new pathway for NIRAF generation through oxidized lipid-driven oxidative stress and support ceroid as a source of NIRAF in human atherosclerosis. These findings may inform future clinical intracoronary NIRAF imaging studies of patients with coronary artery disease.
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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".