VCAM‐<scp>1</scp>‐targeting gold nanoshell probe for photoacoustic imaging of atherosclerotic plaque in mice
Bibliographic record
Abstract
The development of molecular probes and novel imaging modalities, allowing better resolution and specificity, is associated with an increased potential for molecular imaging of atherosclerotic plaques especially in basic and pre-clinical research applications. In that context, a photoacoustic molecular probe based on gold nanoshells targeting VCAM-1 in mice (immunonanoshells) was designed. The molecular probe was validated in vitro and in vivo, showing no noticeable acute toxic effects. We performed the conjugation of gold nanoshells displaying near-infrared absorption properties with VCAM-1 antibody molecules and PEG to increase their biocompatibility. The resulting immunonanoshells obtained under different conditions of conjugation were then assessed for specificity and sensitivity. Photoacoustic tomography was performed to determine the ability to distinguish gold nanoshells from blood both in phantoms and in vivo. Ex vivo optical projection tomography of hearts and aortas from atherosclerotic and control mice confirmed the selective accumulation of the immunonanoshells in atherosclerotic-prone regions in mice, thus validating the utility of the probe in vivo in small animals for pre-clinical research. These immunonanoshells represent an adequate mean to target atherosclerotic plaques in small animals, leading to new tools to follow the effect of therapies on the progression or regression of the disease.
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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 | 0.001 | 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".