Polymeric Nanoparticles and Microcapsules for Biomedical Applications
Bibliographic record
Abstract
Nanoparticle-based delivery vehicles have received substantial interest in the field of drug delivery particularly pertaining to chemotherapeutics. By virtue of their size, nanoscale drug delivery vehicles overcome many obstacles encountered by traditional systems. Moreover, nanocarriers can be fabricated to be ‘smart’, meaning they can be responsive to internal stimuli relating to the microenvironment of the tumor and/or external stimuli that can be delivered non-invasively from outside of the body. One such external trigger is ultrasound, well-known for its role in biomedical imaging based on its wide availability, non-invasiveness, and safety but increasingly being applied for drug delivery. This thesis proposes solutions to two key challenges associated with locally-targeted polymer-based drug delivery: enhanced tumor accumulation and externally-triggered control over release kinetics. In the former case, brush polymer PLA-PEG analogues are synthesized and explored to correlate how the architecture of these brush blocks affects the resulting self-assembled nanoparticle size, zeta potential, cytotoxicity in vitro, circulation time, and accumulation profiles in vivo. Indeed, brush copolymer analogues allow for copolymerization with additional monomers while conserving ‘stealth properties of linear copolymers, as well as exhibit superior circulation times and longer-term tumor accumulation. In the latter case, a new ultrasound-triggered drug delivery platform is designed consisting of a hollow polymeric shell in which silica “corks” are entrapped; the application of ultrasound can exploit the high difference in the compressibility between the polymeric shell and the silica corks to pop out or otherwise perturb the cork particles, allowing for both on-demand drug release as well as a pulsatile release profiles to be achieved. Overall, by manipulating the surface properties and/or morphologies of polymer-based micro/nanoparticles, the results of this thesis show that key challenges in local drug delivery can be addressed and applied specifically to applications in cancer therapy.
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 | 0.000 | 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 | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.009 | 0.007 |
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".