In vivo assembly of nanoparticle components to improve targeted cancer imaging
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
Many small molecular anticancer agents are often ineffective at detecting or treating cancer due to their poor pharmacokinetics. Using nanoparticles as carriers can improve this because their large size reduces clearance and improves retention within tumors, but it also slows their rate of transfer from circulation into the tumor interstitium. Here, we demonstrate an alternative strategy whereby a molecular contrast agent and engineered nanoparticle undergo in vivo molecular assembly within tumors, combining the rapid influx of the smaller and high retention of the larger component. This strategy provided rapid tumor accumulation of a fluorescent contrast agent, 16- and 8-fold faster than fluorescently labeled macromolecule or nanoparticle controls achieved. Diagnostic sensitivity was 3.0 times that of a passively targeting nanoparticle, and this improvement was achieved 3 h after injection. The advantage of the in vivo assembly approach for targeting is rapid accumulation of small molecular agents in tumors, shorter circulation time requirements, possible systemic clearance while maintaining imaging sensitivity in the tumor, and nanoparticle anchors in tumors can be utilized to alter the pharmacokinetics of contrast agents, therapeutics, and other nanoparticles. This study demonstrates molecular assembly of nanoparticles within tumors, and provides a new basis for the future design of nanomaterials for medical applications.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 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