The Impact of Tear Film Components on <i>In Vitro</i> Lipid Uptake
Bibliographic record
Abstract
PURPOSE: To analyze the influence of various tear film components on in vitro deposition of two lipids (cholesterol and phosphatidylcholine) on three contact lens materials. METHODS: Etafilcon A, balafilcon A, and senofilcon A were incubated in four different incubation solutions for 3 or 14 days: an artificial tear solution containing lipids and proteins, a protein tear solution containing proteins and the lipid of interest, a lipid tear solution containing lipids and no proteins, and a single lipid tear solution containing the lipid of interest only. Each incubation solution contained one of the two radiolabeled lipids: C-cholesterol (C) or C-phosphatidylcholine (PC). After soaking, lenses were removed from the incubation solution, the lipids were extracted and quantified using a beta counter, and masses of lipid were calculated using standard calibration curves. RESULTS: This experiment examined several different parameters influencing lipid deposition on contact lenses, including lens material, length of incubation, and the composition of the incubation solution. Overall, lipid deposited differently on different lens materials (p < 0.0005), with the order of deposition most commonly being balafilcon > senofilcon > etafilcon. Incubation solution had a large impact on how much lipid was deposited (p < 0.00001), although cholesterol and phosphatidylcholine demonstrated different deposition patterns. Lipid deposition after 14 days of incubation was consistently greater than after 3 days (p < 0.02). CONCLUSIONS: This in vitro study demonstrates that C and PC deposition are cumulative over time and that silicone hydrogel materials deposit more lipid than group IV conventional hydrogel materials. It also clearly demonstrates that deposition of C and PC is influenced by the composition of the incubation solution and that in vitro models must use more physiologically relevant incubation solutions that mimic the natural tear film if in vitro data is to be extrapolated to the in vivo situation.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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".