Multifunctional Perfluorocarbon Nanoemulsions for Cancer Therapy and Imaging
Bibliographic record
Abstract
Nanoemulsions have been used as theranostic agents for both imaging cancer and therapy by the ability of perfluorocarbon nanoemulsions to vaporize. However, many of the current imaging contrast agents are not stable enough to be used for long term monitoring of tumor growth/regression during therapy. The current work uses biocompatible perfluorohexane nanoemulsions (PFH-NEs) with silica coated gold nanoparticles (scAuNPs) (PFH-NEs-scAuNPs) that can be used to enhance both the photoacoustic (PA) as well as the nonlinear ultrasound (NL US) signals from the resulting PFH bubbles formed from laser excitation. Results show the ability of PFH-NEs-scAuNPs to be used for imaging of cancer using various imaging platforms (i.e., photoacoustic, nonlinear ultrasound, fluorescence) as well as for destroying cancer cells through vaporization of PFH-NEs. Compared to other conventional PA and NL US contrast agents, the signals from PFH-NEs-scAuNPs are more stable with the added benefit that they can be used effectively to treat different tumors through efficient encapsulation of different therapeutic agents and vaporization. In vivo results show that the signals from PFH-NEs and PFH bubbles are stable for several days where the nanoparticles can be used for their theranostic ability to efficiently localize and treat cancer, serving to improve outcomes in cancer and showing strong potential for clinical applications.
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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".