Antibacterial Activity of Preservative-Free Topical Anesthetic Drops in Current Use in Ophthalmology Departments
Bibliographic record
Abstract
AIM: The antibacterial effect of topical anesthetics may lead to false-negative cultures from corneal specimens of bacterial keratitis. This in vitro study compared the antibacterial effect of 3 unpreserved topical anesthetics to indicate the most appropriate agent for corneal scrapes. METHODS: Four bacterial strains (Staphylococcus epidermidis, Staphylococcus aureus, Pseudomonas aeruginosa, and Streptococcus pneumoniae) derived from the most frequently isolated microorganisms from corneal ulcers were cultured from stored control stocks and clinical specimens. These strains were used to determine the minimum inhibitory concentration (MIC) of 3 preservative-free anesthetic eyedrops: proxymetacaine 0.5%, oxybuprocaine 0.4%, and tetracaine 1%. RESULTS: There was no inhibition of growth seen with proxymetacaine 0.5% (5000 microg/mL) with any of the organisms except S. epidermidis, which demonstrated an MIC of 2500 microg/mL (equivalent to a dilution of (1/2)). Tetracaine 1% (10,000 microg/mL) produced an MIC ranging between 625 and 1250 microg/mL, inhibiting all 4 strains at the commercially available dilution. Oxybuprocaine 0.4% (4000 microg/mL) resulted to be the second most inhibitory preparation with an MIC ranging between 1000 and 2000 microg/mL. CONCLUSIONS: Currently used preservative-free topical anesthetics differ in bacterial growth inhibition. This in vitro study showed that proxymetacaine 0.5% is the least inhibitory on bacterial growth and therefore the most appropriate to be used before corneal scrapes.
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 | 0.002 |
| 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) | 0.002 | 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".