Injectable On-Demand Pulsatile Drug Delivery Hydrogels Using Alternating Magnetic Field-Triggered Polymer Glass Transitions
Bibliographic record
Abstract
Remote-controlled pulsatile or staged release has significant potential in a wide range of therapeutic treatments. However, most current approaches are hindered by the low resolution between the on- and off-states of drug release and the need for surgical implantation of larger controlled-release devices. Herein, we describe a method that addresses these limitations by combining injectable hydrogels, superparamagnetic iron oxide nanoparticles (SPIONs) that heat when exposed to an alternating magnetic field (AMF), and polymeric nanoparticles with a glass transition temperature ( T g ) just above physiological temperature. Miniemulsion polymerization was used to fabricate poly(methyl methacrylate- co -butyl methacrylate) (p(MMA- co -BMA)) nanoparticles loaded with a model hydrophobic drug and tuned to have a T g value just above physiological temperature (∼43 °C). Co-encapsulation of these drug-loaded nanoparticles with SPIONs inside a carbohydrate-based injectable hydrogel matrix (formed by rapid hydrazone cross-linking chemistry) enables injection and immobilization of the nanoparticles at the target site. Temperature cycling facilitated a 2.5:1 to 6:1 on/off rhodamine release ratio when the nanocomposites were switched between 37 and 45 °C; release was similarly enhanced by exposing the nanocomposite hydrogel to an AMF to drive heating, with enhanced release upon pulsing observed even 1 week after injection. Coupled with the apparent cytocompatibility of all of the nanocomposite components, these injectable nanocomposite hydrogels are promising as minimally invasive but remotely actuated release delivery vehicles capable of complex release kinetics with high on–off resolution.
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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".