<i>In Vivo</i> Optical Analysis of Quantitative Changes in Collagen and Elastin During Arterial Remodeling<sup>¶</sup>
Bibliographic record
Abstract
ABSTRACT Altered collagen and elastin content correlates closely with remodeling of the arterial wall after injury. Optical analytical approaches have been shown to detect qualitative changes in plaque composition, but the capacity for detection of quantitative changes in arterial collagen and elastin content in vivo is not known. We have assessed fluorescence spectroscopy for detection of quantitative changes in arterial composition in situ, in rabbit models of angioplasty and stent implant. Fluorescence emission intensity (FEI) recorded at sites remote from the primary implant site was correlated with immunohistochemical (IH) analysis and extracted elastin and collagen. FEI was significantly decreased (P < 0.05) after treatment with anti‐inflammatory agents, and plaque area decreased on comparison with saline‐treated rabbits after stent implant or angioplasty (P≤ 0.013). Excellent correlations for FEI with elastin and collagen I, III and IV content measured by IH (R2≥ 0.961) analysis were detected by multiple regression (MR) analysis. Good correlations also were found for FEI with elastin and collagen measured by high‐performance liquid chromatography; MR analysis provided highly predictive values for collagen and elastin (R2≥ 0.994). Fluorescence spectroscopic analysis detects quantitative compositional changes in arterial connective tissue in vivo, demonstrating changes at sites remote from primary angioplasty and stent implant sites.
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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 | 0.000 | 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) | 0.003 | 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".