3D-Printed Contact Lenses to Release Polyvinyl Alcohol as a Therapeutic Agent for the Treatment of Dry Eyes
Bibliographic record
Abstract
Purpose: Dry eye disease is highly prevalent, and the most common treatment, lubricating eye drops, only remains effective for a very short period of time. This project aims to 3D print a proof-of-concept, custom-fit, polyvinyl alcohol (PVA)-eluting contact lens (CL) for the treatment of dry eye disease. PVA is a commonly used viscosity enhancer in eye drops, with the capability of reducing symptoms of dry eye by stabilizing the tear film and reducing tear evaporation. The protective effects of PVA could be attributed to its water-retaining ability, which provides moisturization and prevents the loss of water. Method: In this work, a low-cost stereolithography-based 3D printer was retrofitted with a humidity and temperature control kit to 3D print a PVA-loaded custom-fit CL. To evaluate the print quality of the 3D-printed CL, circularity was used to evaluate the shape fidelity in 3D printing. The PVA release from these lenses was assessed, along with its role in acting as a viscosity enhancer. The effect of PVA was further analyzed by a dry eye disease (desiccation stress) cell model. Results: The shape fidelity evaluation of the 3D-printed CL displayed excellent circularity. The diameter, sagittal depth, and base curve of the 3D-printed lenses were measured to be 14.27 ± 0.06 mm, 3.77 ± 0.16 mm, and 6.4 ± 0.24 mm, respectively. The PVA release curves showed that approximately 1300 µg of PVA was released over the study duration of 24 h. Conclusions: Overall, this work demonstrates that a 3D-printed PVA-eluting CL is a promising candidate for the treatment of 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 | 0.001 |
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".