3D Volumetric Photoacoustic (PA) Imaging of Multimodal Porphysome Nanoparticles
Bibliographic record
Abstract
Photoacoustic imaging adds functional information on the conventional B-mode ultrasound images. Combining photoacoustic information with micro-ultrasound has potential to aid localization and delineation of cancerous lesions to be targeted for focal therapies. The photoacoustic signal in the tumor is primarily generated by hemoglobin but exogenous photoacoustic contrast agents such as novel, biocompatible porphysome nanoparticles can be used to enhance photoacoustic signal. This work demonstrates 3D in vivo micro-ultrasound and photoacoustic imaging of porphysome nanoparticles in a subcutaneous mouse tumor model up to 48 hours after the tail-vein injection. Linear spectral unmixing was used to separate the photoacoustic signal contribution from oxygenated, deoxygenated hemoglobin and porphysomes. 3D imaging revealed the spatial distribution of nanoparticles in the tumor over time. Longitudinal imaging with porphysomes showed that the photoacoustic signal from contrast agent increased after the injection and was co-localized with oxygenated hemoglobin. The presence of nanoparticles in the tumor was also confirmed by the fluorescence imaging which showed the signal peak at 24 hours post-injection potentially due to disassociation of the particle in vivo. Fluorescence histology also confirmed the presence of nanoparticles in the tumor.
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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".