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 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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 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 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 0.006 |
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; both teacher heads agree on what is shown here.
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".