Atomic force microscopy and Langmuir–Blodgett monolayer technique to assess contact lens deposits and human meibum extracts
Bibliographic record
Abstract
PURPOSE: The purpose of this exploratory study was to investigate the differences in meibomian gland secretions, contact lens (CL) lipid extracts, and CL surface topography between participants with and without meibomian gland dysfunction (MGD). METHODS: Meibum study: Meibum was collected from all participants and studied via Langmuir-Blodgett (LB) deposition with subsequent Atomic Force Microscopy (AFM) visualization and surface roughness analysis. CL Study: Participants with and without MGD wore both etafilcon A and balafilcon A CLs in two different phases. CL lipid deposits were extracted and analyzed using pressure-area isotherms with the LB trough and CL surface topographies and roughness values were visualized using AFM. RESULTS: Meibum study: Non-MGD participant meibum samples showed larger, circular aggregates with lower surface roughness, whereas meibum samples from participants with MGD showed more lipid aggregates, greater size variability and higher surface roughness. CL Study: Worn CLs from participants with MGD had a few large tear film deposits with lower surface roughness, whereas non-MGD participant-worn lenses had many small lens deposits with higher surface roughness. Balafilcon A pore depths were shallower in MGD participant worn lenses when compared to non-MGD participant lenses. Isotherms of CL lipid extracts from MGD and non-MGD participants showed a seamless rise in surface pressure as area decreased; however, extracts from the two different lens materials produced different isotherms. CONCLUSIONS: MGD and non-MGD participant-worn CL deposition were found to differ in type, amount, and pattern of lens deposits. Lipids from MGD participants deposited irregularly whereas lipids from non-MGD participants showed more uniformity.
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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 | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 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".