Silica-Coated Quantum Dots for Optical Evaluation of Perfluorocarbon Droplet Interactions with Cells
Bibliographic record
Abstract
There has been recent interest in developing new, targeted, perfluorocarbon (PFC) droplet-based contrast agents for medical imaging (e.g., magnetic resonance imaging, X-ray/computed tomography, and ultrasound imaging). However, due to the large number of potential PFCs and droplet stabilization strategies available, it is challenging to determine in advance the PFC droplet formulation that will result in the optimal in vivo behavior and imaging performance required for clinical success. We propose that the integration of fluorescent quantum dots (QDs) into new PFC droplet agents can help to rapidly screen new PFC-based candidate agents for biological compatibility early in their development. QD labels can allow the interaction of PFC droplets with single cells to be assessed at high sensitivity and resolution using optical methods in vitro, complementing the deeper depth penetration but lower resolution provided by PFC droplet imaging using in vivo medical imaging systems. In this work, we introduce a simple and robust method to miscibilize silica-coated nanoparticles into hydrophobic and lipophobic PFCs through fluorination of the silica surface via a hydrolysis-condensation reaction with 1H,1H,2H,2H-perfluorodecyltriethoxysilane. Using CdSe/ZnS core/shell QDs, we show that nanoscale, QD-labeled PFC droplets can be easily formed, with similar sizes and surface charges as unlabeled PFC droplets. The QD label can be used to determine the PFC droplet uptake into cells in vitro by fluorescence microscopy and flow cytometry, and can be used to validate the fate of PFC droplets in vivo in small animals via fluorescence microscopy of histological tissue sections. This is demonstrated in macrophage and cancer cells, and in rabbits, respectively. This work reveals the potential of using QD labels for rapid, preclinical, optical assessment of different PFC droplet formulations for their future use in patients.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| 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) | 0.000 | 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 teacher head, 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".