The Impact of Silicone Hydrogel–Solution Combinations on Corneal Epithelial Cells
Bibliographic record
Abstract
Silicone hydrogel (SiHy) contact lenses were introduced on the market over 10 years ago, and several multipurpose solutions (MPS) have since been developed for their cleaning and disinfection. Depending on the combination of lens and solution, clinical and retrospective studies have shown that different combinations may be more biocompatible than others. In vivo, sodium fluorescein is used to assess the corneal response, whereas in vitro studies typically investigate MPS toxicity by incubating diluted MPS with cells. This difference between in vivo and in vitro measurements makes it difficult to gain a better understanding of the biocompatibility of SiHy-solution combinations. This review discusses the recent progress in characterizing the interactions between sodium fluorescein and corneal epithelial cells and in vitro MPS cytotoxicity using solution on both monolayer and stratified epithelial models. As interactions between MPS and contact lens materials lead to uptake and release of various solution components, in vitro models that take into account the effect of lens material are also presented. With the improvement of advanced characterization methods and new in vitro models, we are moving in the right direction, but more effort is required to fully elucidate the interactions between contact lens, disinfecting solution, and the cornea.
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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.001 |
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".