Physical Entrapment of Hyaluronic Acid During Synthesis Results in Extended Release From Model Hydrogel and Silicone Hydrogel Contact Lens Materials
Bibliographic record
Abstract
OBJECTIVES: This study was designed to assess the duration of hyaluronic acid (HA) release from model contact lens materials when HA was physically incorporated into the hydrogel during synthesis and to assess the effects of the HA release on lysozyme sorption. METHODS: Model conventional and silicone hydrogel contact lens materials containing HA of various molecular weights as a releasable wetting agent were prepared. The HA was released into phosphate-buffered saline and MilliQ water, and the release was monitored using ultraviolet spectroscopy. Hyaluronic acid release was quantified by enzyme-linked immunosorbent assay. The effect of the releasable HA on lysozyme sorption to the materials was also analyzed using 125-I-labeled protein. RESULTS: Hyaluronic acid loaded into the materials using this method could be released from conventional hydrogel materials for 21 days; the model silicone hydrogels showed release of more than 7 weeks. With one exception, the releasable HA decreased lysozyme sorption. CONCLUSIONS: Hyaluronic acid physically incorporated into contact lens materials during synthesis may therefore be released for extended periods of time of up to 7 weeks. Hyaluronic acid release leads to decreased protein adsorption in general. This method has potential for modification of conventional and silicone hydrogel lenses with releasable HA as a wetting agent.
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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".