Laser activatable perfluorocarbon bubbles for imaging and therapy through enhanced absorption from coupled silica coated gold nanoparticles
Bibliographic record
Abstract
, in terms of size) affecting the amount of particles that can accumulate at the target site and the overall contrast that can be achieved. In this study, perfluorohexane (PFH) nanoemulsions (NEs) were synthesized with silica coated gold nanoparticles (PFH-NEs-scAuNPs) in order to give both stable and enhanced signals for cancer imaging by increasing vaporization of the emulsions into bubbles through the process of optical droplet vaporization (ODV). The resulting perfluorohexane bubbles could be imaged using nonlinear ultrasound (NL US) which significantly increases the signal to noise ratio due to the nonlinear scattering properties of oscillating bubbles. The NL US signals from PFH bubbles were found to be more stable compared to conventional bubbles used for contrast imaging. In addition, the vaporization of PFH NEs into bubbles was shown to cause significant cancer cell death reflecting the theranostic capabilities of the formed PFH bubbles. Since cell death is initiated with laser excitation of PFH-NEs-scAuNPs, these nanoparticles can specifically target cancer cells once they have accumulated at the tumor region. Due to the type of theranostic agent and imaging modality used, the PFH-NEs-scAuNPs can be used to provide higher specificity compared to other agents for locating the tumor region by minimizing tissue specific signals while at the same time being used to treat cancer.
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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".