Laguerre-based method for analysis of time-resolved fluorescence data: application to in-vivo characterization and diagnosis of atherosclerotic lesions
Bibliographic record
Abstract
We report the application of the Laguerre deconvolution technique (LDT) to the analysis of in-vivo time-resolved laser-induced fluorescence spectroscopy (TR-LIFS) data and the diagnosis of atherosclerotic plaques. TR-LIFS measurements were obtained in vivo from normal and atherosclerotic aortas (eight rabbits, 73 areas), and subsequently analyzed using LDT. Spectral and time-resolved features were used to develop four classification algorithms: linear discriminant analysis (LDA), stepwise LDA (SLDA), principal component analysis (PCA), and artificial neural network (ANN). Accurate deconvolution of TR-LIFS in-vivo measurements from normal and atherosclerotic arteries was provided by LDT. The derived Laguerre expansion coefficients reflected changes in the arterial biochemical composition, and provided a means to discriminate lesions rich in macrophages with high sensitivity (>85%) and specificity (>95%). Classification algorithms (SLDA and PCA) using a selected number of features with maximum discriminating power provided the best performance. This study demonstrates the potential of the LDT for in-vivo tissue diagnosis, and specifically for the detection of macrophages infiltration in atherosclerotic lesions, a key marker of plaque vulnerability.
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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 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.001 |
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".