Highly NIR-II Scattering Gold Superclusters for Intravascular Optical Coherence Tomography Molecular Imaging
Bibliographic record
Abstract
In North America, intravascular optical coherence tomography (IV-OCT) is performed every few minutes to evaluate atherosclerotic plaques and guide stent placement. Currently, IV-OCT is limited to anatomical imaging, providing structural information about atherosclerotic plaque morphology, thrombus, and dissection. Earlier detection and risk stratification would be possible through molecular characterization of endothelium but necessitates a purpose-engineered IV-OCT contrast agent. We developed gold superclusters (AuSC) tailored to clinical instrumentation and integrated into clinically relevant workflows. AuSC are aqueously dispersible clusters of closely packed small gold nanoparticles, affording plasmon hybridization to maximize light scattering at the IV-OCT laser line (~1350 nm). A polymer coating fosters AuSC uniformity and provides a functionalizable handle, which we targeted to intravascular P-selectin, an early vascular endothelial marker of inflammation. In a rat model of intravascular inflammation, P-selectin-targeted AuSC facilitated IV-OCT molecular imaging, where the strength of the signal correlates with the severity of vascular inflammation. AuSC thus enable in vivo molecular imaging, advancing IV-OCT into the molecular age.
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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) | 0.002 | 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".