Bibliographic record
Abstract
The goal of nanomedicine is to use nanoparticles to carry drugs to specific target site in the body. For cancer nanomedicine, a recent meta-analysis showed that only 0.7% of the injected nanoparticles reach the tumour. To address this delivery inefficiency, it is important to examine each biological barrier to determine its impact on delivery. In this thesis, first the body was modelled as a series of barriers that nanoparticles need to overcome successively in order to access the target site. The model shows that the number and strength of barriers limits what is available to be delivered. The macrophages of the liver can sequester up to 99% of the injected nanoparticles, and thus are the biggest barrier for targeted delivery. Next, clodronate-liposomes were used to remove the liver macrophages and showed that both nanoparticle tumour delivery and hepatobiliary elimination can be improved. Specifically, nanoparticle tumour delivery can be increased up to 50× and hepatobiliary elimination up to 10×. Removal of the liver macrophages then allowed the exploration of other secondary barriers to delivery such as tumour pathophysiology and the liver sinusoidal endothelium. Together, these studies define concepts and strategies that can improve nanoparticle delivery and reduce unwanted bioaccumulation to pave the way for their clinical translation and regulatory approval.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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".