Synthesis of Stable Multifunctional Perfluorocarbon Nanoemulsions for Cancer Therapy and Imaging
Bibliographic record
Abstract
Nanotechnology provides a promising platform for drug-delivery in medicine. Nanostructured materials can be designed with desired superparamagnetic or fluorescent properties in conjunction with biochemically functionalized moieties (i.e., antibodies, peptides, and small molecules) to actively bind to target sites. These multifunctional properties make them suitable agents for multimodal imaging, diagnosis, and therapy. Perfluorohexane nanoemulsions (PFH-NEs) are novel drug-delivery vehicles and contrast agents for ultrasound and photoacoustic imaging of cancer in vivo, offering higher spatial resolution and deeper penetration of tissue when compared to conventional optical techniques. Compared to other theranostic agents, our PFH-NEs are one of the smallest of their kind (<100 nm), exhibit minimal aggregation, long-term stability at physiological conditions, and provide a noninvasive cancer imaging and therapy alternative for patients. Here, we show, using high-resolution imaging and correlative techniques, that our PFH-NEs, when in tandem with silica-coated gold nanoparticles (scAuNPs), can be used as a drug-loaded therapeutic via endocytosis and as a multimodal imaging agent for photoacoustic, ultrasound, and fluorescence imaging of tumor growth.
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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) | 0.001 | 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".