Iron( <scp>ii</scp> )-catalysed tyrosinase crosslinked hyaluronic acid hydrogel for the controlled release of human antibodies
Bibliographic record
Abstract
Tyrosinase is a common crosslinker used in the formation of in situ hydrogels, often resulting in significantly longer gelation times. The rate-determining step for the interconversion between the four discrete states of the enzyme is characterized by a lag phase, which contributes to its slow gelation kinetics. In this study, we report, for the first time, the use of a catalytic amount of iron(II) to produce fast in situ-gellable tyramine-conjugated hyaluronic acid hydrogels (HATA), which are prospectively applicable for nasal drug delivery. We observed gelation times ranging from 886 to 538 seconds, depending on the polymer and enzyme concentrations, irrespective of the pH level tested. The presence of iron(II) significantly reduced the gelation time by an order of magnitude, ranging from 86 seconds to 25.46 seconds, depending on the polymer concentration, pH, and enzyme activity. Based on our findings, we propose a double crosslinking mechanism involving catechol-catechol coupling and catechol-iron(II) complex formation, as evidenced by improvements in the rheological properties of the hydrogels. These novel hydrogels can encapsulate antibodies and provide prolonged release for up to two weeks. Additionally, we confirmed that the crosslinking chemistry did not affect the bioactivity of the antibodies. Given their improved mucoadhesive properties, we envision these hydrogels as promising candidates for the formulation of bioadhesive drug delivery systems.
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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".