Poly(ethyl glyoxylate)-Poly(ethylene oxide) Nanoparticles: Stimuli-Responsive Drug Release via End-to-End Polyglyoxylate Depolymerization
Bibliographic record
Abstract
The ability to disrupt polymer assemblies in response to specific stimuli provides the potential to release drugs selectively at certain sites or conditions in vivo. However, most stimuli-responsive delivery systems require many stimuli-initiated events to release drugs. “Self-immolative polymers” offer the potential to provide amplified responses to stimuli as they undergo complete end-to-end depolymerization following the cleavage of a single end-cap. Herein, linker end-caps were developed to conjugate self-immolative poly(ethyl glyoxylate) (PEtG) with poly(ethylene oxide) (PEO) to form amphiphilic block copolymers. These copolymers were self-assembled to form nanoparticles in aqueous solution. Cleavage of the linker end-caps were triggered by a thiol reducing agent, UV light, H 2 O 2, and combinations of these stimuli, resulting in nanoparticle disintegration. Low stimuli concentrations were effective in rapidly disrupting the nanoparticles. Nile red, doxorubin, and curcumin were encapsulated into the nanoparticles and were selectively released upon application of the appropriate stimulus. The ability to tune the stimuli-responsiveness simply by changing the linker end-cap makes this new platform highly attractive for applications in drug delivery.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 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 | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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 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".