Impact of Pollutant Ozone on the Biophysical Properties of Tear Film Lipid Layer Model Membranes
Bibliographic record
Abstract
Ozone exposure from environmental smog has been implicated as a risk factor for developing dry eye disease (DED). The tear film lipid layer (TFLL), which is the outermost layer of the tear film and responsible for surface tension reduction while blinking, is in direct contact with the environment and serves as the first line of defense against external aggressors such as environmental pollution. The impact of exposure to ozone on the biophysical properties of three TFLL model membranes was investigated. These model membranes include a binary mixture of cholesteryl oleate (CO) and L-α-phosphatidylcholine (egg PC), a ternary mixture of CO, glyceryl trioleate (GT) and PC, as well as a quaternary mixture of CO, GT, a mixture of free fatty acids palmitic acid and stearic acid (FFAs) and PC. Biophysical impacts were evaluated as changes to the surface activity, respreadability, morphology and viscoelastic properties of the films. Expansion to higher molecular areas was observed in all the TFLL model membrane films which is attributable to the accommodation of the cleaved chains in the film. Significant morphological changes were observed, namely fluidization and the disruption of the phase transition behaviour of GT, and multilayer formation of CO. This fluidization reduces the hysteresis loops for the model membranes. On the other hand, the viscoelastic properties of the films exhibited differential impacts from ozone exposure as a function of composition. These findings are correlated to chemical changes to the lipids determined using ESI-MS.
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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 | 0.000 |
| 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".