Optical Detection of Triggered Atherosclerotic Plaque Disruption by Fluorescence Emission Analysis¶
Bibliographic record
Abstract
Fluorescence emission analysis (FEA) has proven to be very sensitive for the detection of elastin, collagen and lipids, which are recognized as the major sources of autofluorescence in vascular tissues. FEA has also been reported to detect venous thromboemboli. In this paper we have tested the hypothesis that FEA can reproducibly detect in vivo and in vitro triggered plaque disruption and thrombosis in a rabbit model. Fluorescence emission (FE) spectra, recorded in vivo, detected Russell's viper venom (RVV)-induced transformation of atherosclerotic plaque. FE intensity at 410-490 nm 4 weeks after angioplasty was significantly lower (P < 0.0033 by analysis of variance) in RVV-treated rabbits when compared to control animals with stable plaque. FE spectral profile analyses also demonstrated a significant change in curve shape as demonstrated by polynomial regression analysis (R2 from 0.980 to 0.997). We have also demonstrated an excellent correlation between changes in FE intensity and the structural characteristics detected at different stages of "unstable atherosclerotic plaque" development using multiple regression analysis (R2 = 0.989). Thus, FEA applied in vivo is a sensitive and highly informative diagnostic technique for detection of triggered atherosclerotic plaque disruption and related structural changes, associated with plaque transformation, in a rabbit model.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 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) | 0.000 | 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 teacher head, 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".