Development of a targeted CT contrast agent: assessment of cellular interactions using novel integrated optical labels
Bibliographic record
Abstract
Computed tomography (CT) enables high resolution, whole-body imaging with excellent depth penetration. The development of new targeted radiopaque CT contrast agents can provide the required sensitivity and localization for the successful detection and diagnosis of smaller lesions representing earlier disease. Nanoscale, perfluorooctylbromide (C8F17Br, PFOB) droplets have previously been used as untargeted contrast agents in X-ray imaging, and form the basis of a promising new group of agents that can be developed for targeted CT imaging. For successful targeting to disease sites, new PFOB droplet formulations tailored for ideal in vivo performance (e.g., biodistribution, toxicity, and pharmacokinetics) must be developed. However, the direct assessment of PFOB agents in biological environments early in their development is difficult using CT, as its sensitivity is not adequate for identification of single probes in vitro or in vivo. In order to allow single droplet interactions with cells to be directly assessed using standard cellular imaging tools, we integrate an optical marker within the PFOB agent. In this work, a new method to label a PFOB agent with fluorescent quantum dot (QD) nanoparticles is presented. These composite PFOB-QD droplets loaded into macrophage cells result in fluorescence on a cellular level that correlates well to the strong CT contrast exhibited in corresponding tissue-mimicking cell pellets. QD loading within the PFOB droplet core allows optical labeling without influencing the surface-dependent properties of the PFOB droplets in vivo, and may be used to follow PFOB localization from in vitro cell studies to histopathology.
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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) | 0.001 | 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 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 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".