Tough Composite Hydrogels with High Loading and Local Release of Biological Drugs
Bibliographic record
Abstract
Abstract Hydrogels are under active development for controlled drug delivery, but their clinical translation is limited by low drug loading capacity, deficiencies in mechanical toughness and storage stability, and poor control over the drug release that often results in burst release and short release duration. This work reports a design of composite clay hydrogels, which simultaneously achieve a spectrum of mechanical, storage, and drug loading/releasing properties to address the critical needs from translational perspectives. The clay nanoparticles provide large surface areas to adsorb biological drugs, and assemble into microparticles that are physically trapped within and toughen hydrogel networks. The composite hydrogels demonstrate feasibility of storage, and extended release of large quantities of an insulin‐like growth factor‐1 mimetic protein (8 mg mL−1) over four weeks. The release rate is primarily governed by ionic exchange and can be upregulated by low pH, which is typical for injured tissues. A rodent model of Achilles tendon injury is used to demonstrate that the composite hydrogels allow for highly extended and localized release of biological drugs in vivo, while demonstrating biodegradation and biocompatibility. These attributes make the composite hydrogel a promising system for drug delivery and regenerative medicine.
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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".