Determination of biodistribution of ultrasmall, near-infrared emitting gold nanoparticles by photoacoustic and fluorescence imaging
Bibliographic record
Abstract
This study compares fluorescence and photoacoustic (PA) imaging of ex vivo tumors and organs from tumor-bearing mice injected intravenously with ultrasmall (<3 nm ) tiopronin-capped Au nanoparticles and compares the data with inductively coupled plasma mass spectrometry (ICP-MS). Good agreement is seen in particle distributions and concentrations at the organ level. The spatial resolution from the imaging techniques allows for localization of the particles within organ structures. Although the particles do not have a plasmon peak, their absorbance in the near-infrared (NIR) is sufficient for PA excitation. PA imaging shows an increase of signal as particle concentrations increase, with changes in spectrum if particles aggregate. Fluorescence imaging using the particles’ native NIR emission shows agreement in general intensity in each organ, though quenching of emission can be seen at very high concentrations. Both of these imaging techniques are noninvasive and labor-saving alternatives to organ digestion and ICP-MS and may provide insight into cellular distribution of particles. The simple construct avoids the use of toxic semiconductor materials or dyes, relying upon the gold itself for both the fluorescence and PA signal. This provides a useful alternative to more complex approaches to multimodal imaging and one that is readily translatable to the clinic.
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How this classification was reachedexpand
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 | 0.001 | 0.001 |
| 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".