Nanoparticle Drug Formulations for Cancer Diagnosis and Treatment
Bibliographic record
Abstract
Over the past ten years, more than a billion dollars in U.S. government funding has been awarded to the development of nanomaterials for clinical diagnosis and therapy. In this article we will focus on one subset of nanotechnology: nanoparticle formulations of drugs intended to diagnose or treat cancer. Several nanoparticle drug preparations are now in widespread clinical use, and dozens are in the pipeline. In some cases the nanoparticles are simply passive drug carriers or contrast agents; in others, the nanoparticles have active therapeutic properties. Cancer, particularly solid tumors, is one of nanotechnology's key targets. The specific challenges involved in cancer treatment are those addressed by multifunctional materials, in particular, inaccessibility, widespread metastasis, low oxygen concentrations, and resistance to drugs and radiation. Nonetheless, major barriers still remain to effective nanoparticle design and approval.
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How this classification was reachedexpand
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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.003 | 0.001 |
| 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 | 0.000 | 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".