P685In vivo near-infrared fluorescence (NIRF) molecular imaging of atherosclerosis
Bibliographic record
Abstract
Background: Near-infrared fluorescence (NIRF) is an imaging modality that allows in vivo visualization of molecular and cellular biomarkers using labeled, target-specific probes, which could provide new insights in the pathobiology of atherosclerosis by identifying high-risk features of plaque vulnerability. Purpose: We aimed to determine whether probes targeting ICAM-1 and collagen could be visualized in vivo following in situ injection at the site of atherosclerotic plaques using intravascular bimodal near-infrared fluorescence (NIRF)/ intravascular ultrasound (IVUS) imaging. Methods: Atherosclerotic lesions in 13 New Zealand white rabbits were induced by balloon injury in the distal abdominal aorta, followed by a 12-week cholesterol-enriched diet. At week 12, two separate proprietary imaging probes targeting ICAM-1 and unpolymerized collagen (0.2–0.46 mg/kg/probe), were injected at the injured site of the abdominal aorta using a porous balloon catheter. In vivo intravascular NIRF/IVUS imaging was performed at different time-points over a 40-minute period. Both probes were tested with their corresponding negative controls. ICAM-1 nanobodies, collagen probes and their negative control probes were each injected in a single animal (7 rabbits) and in dual-probe injections (6 rabbits). Animals were subsequently sacrificed and abdominal aortas were resected to perform ex vivo en face fluorescence imaging using an IVIS Lumina system, confocal microscopy, and histopathology analysis. Fluorescence quantification at the site of injection was performed using IVIS Lumina II Living Image 2.0 software and Mann-Whitney test using GraphPad Prism 7.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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) | 0.001 | 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".