Gold Nanoparticles for Efficient Tumour Targeting: Materials, Biology & Application
Bibliographic record
Abstract
As of 2010, cancer remains the leading cause of death in Canada, and second in the United States of America. This is despite decades of research into development of chemotherapeutics and diagnostics. A number of major challenges have prevented new discoveries from translating into a reduction in mortality rates. One challenge is the poor efficiency with which anti-cancer agents (diagnostic contrast agents and therapeutics) reach deregulated cells in the body. Therefore, development of new methods and technologies for improving efficiency of delivery has been a focus of research. Nanoparticles are leading candidates for improving the efficiency of delivery because they can act as payload vehicles for anti-cancer agents, because it is possible to mediate their interaction with biological systems and thus their pharmaockinetics, and because they can exploit inherent vulnerabilities of tumours. This thesis describes the results from a series of research projects designed to progress our understanding of how nanoparticles behave in vivo, and how their design can be optimized to improve tumour targeting.
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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 | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 0.002 |
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".