Stable J-aggregation enabled dual photoacoustic and fluorescence nanoparticles for intraoperative cancer imaging
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
J-aggregates display nanoscale optical properties which enable their use in fluorescence and photoacoustic imaging applications. However, control over their optical properties in an in vivo setting is hampered by the conformational lability of the J-aggregate structure in complex biological environments. J-aggregating nanoparticles (JNP) formed by self-assembly of bacteriopheophorbide-lipid (Bchl-lipid) in lipid nanovesicles represents a novel strategy to stabilize J-aggregates for in vivo bioimaging applications. We find that 15 mol% Bchl-lipid embedded within a saturated phospholipid bilayer vesicle was optimal in terms of maximizing Bchl-lipid dye loading, while maintaining a spherical nanoparticle morphology and retaining spectral properties characteristic of J-aggregates. The addition of cholesterol maintains the stability of the J-aggregate absorption band for up to 6 hours in the presence of 90% FBS. In a proof-of-concept experiment, we successfully applied JNPs as a fluorescence contrast agent for real-time intraoperative detection of metastatic lymph nodes in a rabbit head-and-neck cancer model. Lymph node metastasis delineation was further verified by visualizing the JNP within the excised lymph node using photoacoustic imaging. Using JNPs, we demonstrate the possibility of using J-aggregates as fluorescence and photoacoustic contrast agents and may potentially spur the development of other nanomaterials that can stably induce J-aggregation for in vivo cancer bioimaging applications.
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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 | 0.000 |
| 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 it