In‐situ printing of gelma‐based hydrogels for cornea regeneration
Bibliographic record
Abstract
Aims/Purpose: Corneal perforation is a medical emergency that can lead to blindness. Treatment options encompass cyanoacrylate or fibrin glue, both linked to side effects, including cytotoxicity. Gelatin methacryloyl‐based biomaterials offer an alternative to these adhesives [1, 2]. Achieving optical clarity and smooth surface integrity is crucial for the restoration of vision in treated corneal wounds. Methods: Currently, the delivery of corneal biomaterials utilizes standard syringe systems, which lack the accuracy to reconstruct the cornea's shape. Therefore, the use of a laser bioprinting technology, developed in the Boutopoulos lab [3], for achieving precise in‐situ corneal wound repair was examined. We used a photocrosslinkable ink comprising Gelatin Methacryloyl (GelMa), hydroxyethyl acrylate (HEA), and Lithium phenyl‐2,4,6‐trimethylbenzoylphosphinate (LAP) as a photoinitiator. Results: Printability was optimized by generating nanoliter‐volume individual droplets using 230 μJ laser energy, a flow rate 20 microliter per minute, and a temperature of 37°C. Rheology, optical characterization, and bursting pressure measurements assessed its potential to seal corneal perforations. Results indicated a hydrogel storage modulus of 1.31 ± 0.31 KPa for printed LiQD cornea and a bursting pressure of 38 ± 6 mmHg when used to seal full thickness cornea perforation in cadaveric pig eyes. Optical clarity akin to the native cornea was observed (%light transmission: 93.12 ± 1.02 printed vs 92.61 ± 1.50). The OCT results showed that the LIST technique could potentially fill the corneal wounds and reconstruct the natural curvature of the wounded cornea. Conclusions: In conclusion, a precise printing system for delivering adhesive corneal regenerative biomaterials to wounds was developed and characterized. References 1. Sharifi, S., et al., 2021. 6(11): p. 3947‐3961. 2. Barroso, I.A., et al., 2022. 9(2). 3. Ebrahimi Orimi, H., et al., Sci Rep, 2020. 10(1): p. 9730.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| 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) | 0.000 | 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 teacher head, 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".