Generic, Anthracene‐Based Hydrogel Crosslinkers for Photo‐controllable Drug Delivery
Bibliographic record
Abstract
Light-responsive polymers with controllable, reversible crosslink mechanisms have the potential to create unique biomaterials with stimulus-controlled swelling, degradation and diffusion properties useful in tissue engineering and drug delivery applications. Generic photodimerizing polyethylene glycol-anthracene macromolecules that may be grafted to various polymers to effectively control their crosslinking via a photodimerization mechanism have been developed. These generic crosslinkers were shown to effectively introduce photoresponsive properties into hyaluronate and alginate as model hydrophilic polymers. In vitro testing using human corneal epithelial cells was used to demonstrate cytocompatibility of the resulting photogels. The effective crosslinking density of the photogels could be increased resulting in a decrease in the release rate of small and large molecules from the photogels following exposure to 365 nm light. This tuneable crosslinking has the potential to manipulate the delivery rates of therapeutics resulting in control over treatment profiles and may lend itself to various applications, which may benefit from light induced changes in crosslinking.
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) | 0.000 | 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 | 0.001 |
| 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".