In vivo assembly of nanoparticle components to improve targeted cancer imaging
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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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) | 0.001 | 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 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.001 |
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".