Real-Time and Multiplexed Photoacoustic Imaging of Internally Normalized Mixed-Targeted Nanoparticles
Bibliographic record
Abstract
Photoacoustic (PA) imaging is a developing diagnostic technique where multiple species can be simultaneously imaged with high spatial resolution in 3D if the absorbance spectrum of each species is distinct and separable. However, multiplexed PA imaging has been greatly limited by the availability of spectrally separable contrast agents that can be used in vivo. Toward this end, we present the formation and application of a series of poly ethylene glycol (PEG)-coated nanoparticles (NPs) with unique separable absorbance profiles suitable for simultaneous multiplexed imaging. As a proof-of-concept, we demonstrate this form of mixed-sample multiplexed imaging, using cRGD peptide surface-modified NPs with nonmodified NPs in a murine subcutaneous Lewis lung carcinoma tumor model. The simultaneous imaging of nonmodified NPs provides an "internal standard", to deconvolute the contributions of active-ligand and passive-NP targeting effects. Particles with 25% surface cRGD modification display 52 ± 22 fold higher liver to tumor ratio accumulation levels, while the same set of particles display only 9.8 ± 4 fold accumulation levels when internally normalized. The pharmacokinetic profiles of targeted and nontargeted NPs can be simultaneously tracked in real-time to study how biodistribtions of particles are affected by ligand modification. The internal normalization of control particles greatly enhances the precision and decreases the number of animals needed in studies of nanoparticle targeting. These new dyes are an enabling technology for PA imaging of NP fate and targeting. This is the first demonstration of real-time multiplexed PA imaging of mixed-targeted samples in vivo.
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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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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 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".