Narrow Absorption NIR Wavelength Organic Nanoparticles Enable Multiplexed Photoacoustic Imaging
Bibliographic record
Abstract
Photoacoustic (PA) imaging is an emerging hybrid optical-ultrasound based imaging technique that can be used to visualize optical absorbers in deep tissue. Free organic dyes can be used as PA contrast agents to concurrently provide additional physiological and molecular information during imaging, but their use in vivo is generally limited by rapid renal clearance for soluble dyes and by the difficulty of delivery for hydrophobic dyes. We here report the use of the block copolymer directed self-assembly process, Flash NanoPrecipitation (FNP), to form series of highly hydrophobic optical dyes into stable, biocompatible, and water-dispersible nanoparticles (NPs) with sizes from 38 to 88 nm and with polyethylene glycol (PEG) surface coatings suitable for in vivo use. The incorporation of dyes with absorption profiles within the infrared range, that is optimal for PA imaging, produces the PA activity of the particles. The hydrophobicity of the dyes allows their sequestration in the NP cores, so that they do not interfere with targeting, and high loadings of >75 wt % dye are achieved. The optical extinction coefficients (ε (mL mg(-1) cm(-1))) were essentially invariant to the loading of the dye in NP core. Co-encapsulation of dye with vitamin E or polystyrene demonstrates the ability to simultaneously image and deliver a second agent. The PEG chains on the NP surface were functionalized with folate to demonstrate folate-dependent targeting. The spectral separation of different dyes among different sets of particles enables multiplexed imaging, such as the simultaneous imaging of two sets of particles within the same animal. We provide the first demonstration of this capability with PA imaging, by simultaneously imaging nontargeted and folate-targeted nanoparticles within the same animal. These results highlight Flash NanoPrecipitation as a platform to develop photoacoustic tools with new diagnostic capabilities.
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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 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.001 |
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".