An Injectable Hydrogel Prepared Using a PEG/Vitamin E Copolymer Facilitating Aqueous-Driven Gelation
Bibliographic record
Abstract
Hydrogels have been widely explored for biomedical applications, with injectable hydrogels being of particular interest for their ability to precisely deliver drugs and cells to targets. Although these hydrogels have demonstrated satisfactory properties in many cases, challenges still remain for commercialization. In this paper, we describe a simple injectable hydrogel based on poly(ethylene glycol) (PEG) and a vitamin E (Ve) methacrylate copolymer prepared via simple free radical polymerization and delivered in a solution of low molecular weight PEG and Ve as the solvent instead of water. The hydrogel formed immediately in an aqueous environment with a controllable gelation time. The driving force for gelation is attributed to the self-assembly of hydrophobic Ve residues upon exposure to water to form a physically cross-linked polymer network via polymer chain rearrangement and subsequent phase separation, a spontaneous process with water uptake. The hydrogels can be customized to give the desired water content, mechanical strength, and drug release kinetics simply by formulating the PEGMA-co-Ve polymer with an appropriate solvent mixture or by varying the molecular weight of the polymer. The hydrogels exhibited no significant cytotoxicity in vitro using fibroblasts and good tissue compatibility in the eye and when injected subcutaneously. These polymers thus have the potential to be used in a variety of applications where injection of a drug or cell containing depot would be desirable.
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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".