Semi-Quantitative Analysis of Atherosclerotic Plaque using Optical Coherence Tomography and Time-of-Flight Secondary Ion Mass Spectrometry
Bibliographic record
Abstract
Atherosclerosis is the underlying vascular pathology that initiates arterial thromboembolic occlusions (myocardial infarctions, strokes and peripheral vessel blockage). Two imaging modalities, Optical Coherence Tomography (OCT) and Time-of-Flight Secondary Ion Mass Spectrometry (ToF-SIMS), were investigated for detection and compositional analysis of unstable plaque associated with plaque erosion and sudden occlusion. OCT produces high resolution images whereas mass spectrometry images provide information on the spatial distribution of chemical elements. Diseased carotid arteries taken from patients with high-risk lesions were imaged with OCT and ToF-SIMS to give molecular and metabolic information, and matched with histopathology. OCT results show clear indications of vascular remodeling by the presence of fatty acid deposits, fibrous tissue and calcifications. ToF-SIMS further characterized changes based on secondary ion topography analysis where a high 23Na/39K ratio was indicative of arterial tissue degradation and the amount of 40Ca corresponded with late stage atherosclerosis. This pilot experiment has demonstrated that in vitro OCT imaging and ToF-SIMS of diseased carotid arteries have scientific merit for targeting clinically relevant morphology and metabolic changes to compare stable and unstable plaque. These optical techniques provide complimentary metabolic and molecular information on unstable plaque, specifically cell break-down with altered ion ratios of 23Na, 39K and 40Ca.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| 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".