Fluorescence‐Tagged Gold Nanoparticles for Rapidly Characterizing the Size‐Dependent Biodistribution in Tumor Models
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
Nanoparticle vehicles may improve the delivery of contrast agents and therapeutics to diseased tissues, but their rational design is currently impeded by a lack of robust technologies to characterize their in vivo behavior in real-time. This study demonstrates that fluorescent-labeled gold nanoparticles can be optimized for in vivo detection, perform pharmacokinetic analysis of nanoparticle designs, analyze tumor extravasation, and clearance kinetics in tumor-bearing animals. This optical imaging approach is non-invasive and high-throughput. Interestingly, these fluorescent gold nanoparticles can be used for multispectral imaging to compare several nanoparticle designs simultaneously within the same animal and eliminates the host-dependent variabilities across measured data. Together these results describe a novel platform for evaluating the performance of tumor-targeting nanoparticles, and provide new insights for the design of future nanotherapeutics.
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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 | 0.002 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| 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