Tailoring nanoparticle designs to target cancer based on tumor pathophysiology
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
Nanoparticles can provide significant improvements in the diagnosis and treatment of cancer. How nanoparticle size, shape, and surface chemistry can affect their accumulation, retention, and penetration in tumors remains heavily investigated, because such findings provide guiding principles for engineering optimal nanosystems for tumor targeting. Currently, the experimental focus has been on particle design and not the biological system. Here, we varied tumor volume to determine whether cancer pathophysiology can influence tumor accumulation and penetration of different sized nanoparticles. Monte Carlo simulations were also used to model the process of nanoparticle accumulation. We discovered that changes in pathophysiology associated with tumor volume can selectively change tumor uptake of nanoparticles of varying size. We further determine that nanoparticle retention within tumors depends on the frequency of interaction of particles with the perivascular extracellular matrix for smaller nanoparticles, whereas transport of larger nanomaterials is dominated by Brownian motion. These results reveal that nanoparticles can potentially be personalized according to a patient's disease state to achieve optimal diagnostic and therapeutic outcomes.
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Direct model labels (unvalidated)
Per-model category and study-design labels from the labeling rounds. They are machine output, unvalidated, and the disagreement between models ships as data. No study design here is MEDLINE-validated yet.
| Model arm | Categories | Study design | Confidence |
|---|---|---|---|
| gpt | no category Domain: not available · Genre: Empirical About the Canadian research system: no · About a Canadian topic: no | Bench or experimental | high |
| opus | no category Domain: not available · Genre: Empirical About the Canadian research system: no · About a Canadian topic: no | Bench or experimental | high |
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 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| 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