In Vivo Near-Infrared Fluorescence Imaging of Atherosclerosis Using Local Delivery of Novel Targeted Molecular Probes
Bibliographic record
Abstract
This study aimed to evaluate the feasibility and accuracy of a technique for atherosclerosis imaging using local delivery of relatively small quantities (0.04-0.4 mg/kg) of labeled-specific imaging tracers targeting ICAM-1 and unpolymerized type I collagen or negative controls in 13 rabbits with atheroma induced by balloon injury in the abdominal aorta and a 12-week high-cholesterol diet. Immediately after local infusion, in vivo intravascular ultrasonography (IVUS)-NIRF imaging was performed at different time-points over a 40-minute period. The in vivo peak NIRF signal was significantly higher in the molecular tracer-injected rabbits than in the control-injected animals (P < 0.05). Ex vivo peak NIRF signal was significantly higher in the ICAM-1 probe-injected rabbits than in controls (P = 0.04), but not in the collagen probe-injected group (P = 0.29). NIRF signal discrimination following dual-probe delivery was also shown to be feasible in a single animal and thus offers the possibility of combining several distinct biological imaging agents in future studies. This innovative imaging strategy using in vivo local delivery of low concentrations of labeled molecular tracers followed by IVUS-NIRF catheter-based imaging holds potential for detection of vulnerable human coronary artery plaques.
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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 | 0.000 |
| 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 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".