Influence of Protein Deposition on Bacterial Adhesion to Contact Lenses
Bibliographic record
Abstract
PURPOSE: The aim of the study is to determine the adhesion of Gram positive and Gram negative bacteria onto conventional hydrogel (CH) and silicone hydrogel (SH) contact lens materials with and without lysozyme, lactoferrin, and albumin coating. METHODS: Four lens types (three SH-balafilcon A, lotrafilcon B, and senofilcon A; one CH-etafilcon A) were coated with lysozyme, lactoferrin, or albumin (uncoated lenses acted as controls) and then incubated in Staphylococcus aureus (Saur 31) or either of two strains of Pseudomonas aeruginosa (Paer 6294 and 6206) for 24 h at 37 °C. The total counts of the adhered bacteria were determined using the H-thymidine method and viable counts by counting the number of colony-forming units on agar media. RESULTS: All three strains adhered significantly lower to uncoated etafilcon A lenses compared with uncoated SH lenses (p < 0.05). Lysozyme coating on all four lens types increased binding (total and viable counts) of Saur 31 (p < 0.05). However, lysozyme coating did not influence P. aeruginosa adhesion (p > 0.05). Lactoferrin coating on lenses increased binding (total and viable counts) of Saur 31 (p < 0.05). Lactoferrin-coated lenses showed significantly higher total counts (p < 0.05) but significantly lower viable counts (p < 0.05) of adhered P. aeruginosa strains. There was a significant difference between the total and viable counts (p < 0.05) that were bound to lactoferrin-coated lenses. Albumin coating of lenses increased binding (total and viable counts) of all three strains (p < 0.05). CONCLUSIONS: Lysozyme deposited on contact lenses does not possess antibacterial activity against certain bacterial strains, whereas lactoferrin possess an antibacterial effect against strains of P. aeruginosa.
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) | 0.000 | 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".