Ciprofloxacin Interaction with Silicon-Based and Conventional Hydrogel Contact Lenses
Bibliographic record
Abstract
PURPOSE: Hydrogel contact lenses can be used as bandage lenses to protect the corneal surface after injury. The use of novel silicon-based hydrogel lens materials as bandage lenses has not gained widespread acceptance. As a first step toward advocating their usefulness as bandage lenses, their interaction with ocular pharmaceuticals must be understood because topical agents are often administered in conjunction with bandage lenses. METHODS: The in vitro uptake and release of ciprofloxacin from silicone-based hydrogel (SH) and conventional pHEMA-based (CH) hydrogel contact lenses was examined by spectrophotometric evaluation of the drug concentration in saline solution. RESULTS: The hydrogel contact lenses tested showed similar drug uptake (average 1800 microg/lens) but different levels of drug release. Multiphoton laser microscopy indicated that ciprofloxacin was distributed throughout the lens thickness, with higher levels of drug at the surface owing to drug precipitation. The drug adsorption onto the lenses was partially reversible. The SH lenses released a lower amount of drug than CH lenses (72 vs. 168 microg/lens). Ionic lenses released less drug than non-ionic lenses (127 vs. 151 microg/lens). CONCLUSIONS: The differences in ciprofloxacin uptake and release between SH and CH materials may not be clinically significant because the amount of drug released from all lenses would be above the MIC(90) of ciprofloxacin for common ocular pathogens. These results indicate that material properties have a significant impact on drug-lens interactions.
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 | 0.001 |
| 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".