Effect of Artificial Tear Formulations on the Metabolic Activity of Human Corneal Epithelial Cells after Exposure to Desiccation
Bibliographic record
Abstract
Artificial lipid-containing tear formulations are developed to reduce tear evaporation by the restoration of a deficient tear lipid layer. Artificial tear formulations that prevent cell desiccation will result in ocular surface protection and the maintenance of cell metabolic activity. During dehydration, cells undergo the process of loss of metabolic activity and subsequently cell death. This work describes a method for assessing the efficacy of artificial tear formulations. The metabolic dye (i.e., alamarBlue) changes from a low fluorescent molecule resazurin to a fluorescent molecule resorufin in viable cells. The biological performance of an artificial tear formulation is measured as the ability of the formulation to (a) maintain cell viability and (b) provide cell protection from desiccation. Growth media and saline are used as controls for the cell viability/desiccation tests. Cells are incubated with test solutions for 30 min and then desiccated for 0 or 5 min at 37 °C and 45% relative humidity. Cell metabolic activity after initial exposure and after cell desiccation is then determined. The results show the comparative effects of eye drop formulations on cell metabolic activity and desiccation protection. This method can be used to test dry eye formulations that are designed to treat individuals with evaporative dry eye.
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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".