Fluorescence‐Tagged Gold Nanoparticles for Rapidly Characterizing the Size‐Dependent Biodistribution in Tumor Models
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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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) | 0.000 | 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 | 0.001 | 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".