A Microfluidic Contact Lens to Address Contact Lens‐Induced Dry Eye
Bibliographic record
Abstract
The contact lens (CL) industry has made great strides in improving CL-wearing experiences. However, a large amount of CL wearers continue to experience ocular dryness, known as contact lens-induced dry eye (CLIDE), stemming from the reduction in tear volume, tear film instability, increased tear osmolarity followed by inflammation and resulting in ocular discomfort and visual disturbances. In this article, to address tear film thinning between the CL and the ocular surface, the concept of using a CL with microchannels to deliver the tears from the pre-lens tear film (PrLTF) to the post-lens ocular surface using in vitro eye-blink motion is investigated. This study reports an eye-blink mimicking system with microfluidic poly(2-hydroxyethyl methacrylate) (poly(HEMA)) hydrogel with integrated microchannels to demonstrate eye-blink assisted flow through microchannels. This in vitro experimental study provides a proof-of-concept result that tear transport from PrLTF to post-lens tear film can be enhanced by an artificial eyelid motion in a pressure range of 0.1-5 kPa (similar to human eyelid pressure) through poly(HEMA) microchannels. Simulation is conducted to support the hypothesis. This work demonstrates the feasibility of developing microfluidic CLs with the potential to help prevent or minimize CLIDE and discomfort by the enhanced transport of pre-lens tears to the post-lens ocular surface.
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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".