Changes in Tear Cytokines Following a Short Period of Daily and Overnight Silicone Hydrogel Lens Wear
Bibliographic record
Abstract
Background and Objective: To investigate changes in ocular surface inflammatory markers after daily and overnight silicone hydrogel contact lens wear in healthy wearers. Material and Methods: Twenty-six experienced soft contact lens subjects were evaluated at baseline, after 1-day of silicone hydrogel lens wear, and after 1-night of wear. Basal tears were collected at each visit and tear cytokine concentrations were quantified using multiplex [interleukin (IL)-1β, IL-6, IL-10, IL-12(p70), IL-17A and tumor necrosis factor (TNF)-α] or ELISA (IL-8) kits. A historical control group of 27 non-contact lens wearers was used to compare absolute concentrations and diurnal variations in tear cytokine concentrations. Changes in cytokine concentrations were analyzed using linear mixed models. Linear regression with bootstrapping was used to assess whether changes in IL-1β concentrations were associated with changes in other cytokines. Results: IL-8 concentrations decreased after 1 day of silicone hydrogel contact lens wear and returned to baseline levels the next morning (p=0.04). This same diurnal fluctuation was seen in non-contact lens wearers (p=0.03). With daily contact lens wear, there was a significant positive correlation between the changes in IL-1β and IL-8, TNF-α, IL-10 and IL-12(p70) (all p<0.03). With overnight contact lens wear, there were significant positive correlations between the changes in IL-1β and IL-6, IL-17A and TNF-α (all p<0.01). Conclusion: A short period of daily and overnight silicone hydrogel lens wear does not significantly alter the inflammatory status in adapted soft contact lens wearers.
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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 | 0.001 |
| 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".