Laser activatable perfluorocarbon bubbles for imaging and therapy through enhanced absorption from coupled silica coated gold nanoparticles
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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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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 | 0.001 |
| 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 it