Injectable and 3D Extrusion Printable Hydrophilic Silicone-Based Hydrogels for Controlled Ocular Delivery of Ophthalmic Drugs
Bibliographic record
Abstract
While silicone elastomers have found widespread use in the biomedical industry, 3D printing them has proven to be difficult due to the material’s slow drying time, low viscosity, and hydrophobicity. Herein, we arrested the hydrophilic silicone (HS) macrochains into a semi-interpenetrating polymer network (semi-IPN) via an in situ photogelation-assisted 3D microextrusion printing technique. The flow behavior of the pregel solutions and the mechanical properties of the printed HS hydrogels were tested, showing a high elastic modulus (approximately 15 kPa), a low tan δ, high elasticity, and delayed network rupturing. The uniaxial compression tests demonstrated a nearly negligible permanent deformation, suggesting that the printed hybrid hydrogel maintained its elastic properties. Drug loading and diffusion in the microporous hydrogel are shown via the non-Fickian anomalous transport mechanism, leading to highly tunable loading/releasing profiles (approximately 20% cumulative release) depending on the HS concentration. The drug encapsulation exhibits exceptional stability, remaining intact without any degradation even after a storage period of 1 month. As far as we know, this is the first soft biomaterial based on HS that functions as an exceptional controlled drug delivery device.
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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".