Advanced Biomaterials for Lacrimal Tissue Engineering: A Review
Bibliographic record
Abstract
The lacrimal gland (LG) is vital for ocular health, producing tears that lubricate and protect the eye. Dysfunction of the LG leads to aqueous-deficient dry eye disease (DED), significantly impacting quality of life. Current treatments mainly address symptoms rather than the underlying LG dysfunction, highlighting the need for regenerative therapies. Tissue engineering offers a promising solution, with biomaterials playing crucial roles in scaffolding and supporting cell growth for LG regeneration. This review focuses on recent advances in biomaterials used for tissue engineering of the lacrimal gland. We discuss both natural and synthetic biomaterials that mimic the extracellular matrix and provide structural support for cell proliferation and differentiation. Natural biomaterials, such as Matrigel, decellularized extracellular matrices, chitosan, silk fibroin hydrogels, and human amniotic membrane are evaluated for their biocompatibility and ability to support lacrimal gland cells. Synthetic biomaterials, like polyethersulfone, polyesters, and biodegradable polymers (PLLA and PLGA), are assessed for their mechanical properties and potential to create scaffolds that replicate the complex architecture of the LG. We also explore the integration of growth factors and stem cells with these biomaterials to enhance tissue regeneration. Challenges such as achieving proper vascularization, innervation, and long-term functionality of engineered tissues are discussed. Advances in 3D bioprinting and scaffold fabrication techniques are highlighted as promising avenues to overcome current limitations.
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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 0.003 |
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".