Recent trends in gelatin methacryloyl nanocomposite hydrogels for tissue engineering
Bibliographic record
Abstract
Gelatin methacryloyl (GelMA), a photocrosslinkable gelatin-based hydrogel, has been immensely used for diverse applications in tissue engineering and drug delivery. Apart from its excellent functionality and versatile mechanical properties, it is also suitable for a wide range of fabrication methodologies to generate tissue constructs of desired shapes and sizes. Despite its exceptional characteristics, it is predominantly limited by its weak mechanical strength, as some tissue types naturally possess high mechanical stiffness. The use of high GelMA concentrations yields high mechanical strength, but not without the compromise in its porosity, degradability, and three-dimensional (3D) cell attachment. Recently, GelMA has been blended with various natural and synthetic biomaterials to reinforce its physical properties to match with the tissue to be engineered. Among these, nanomaterials have been extensively used to form a composite with GelMA, as they increase its biological and physicochemical properties without affecting the unique characteristics of GelMA and also introduce electrical and magnetic properties. This review article presents the recent advances in the formation of hybrid GelMA nanocomposites using a variety of nanomaterials (carbon, metal, polymer, and mineral-based). We give an overview of each nanomaterial's characteristics followed by a discussion of the enhancement in GelMA's physical properties after its incorporation. Finally, we also highlight the use of each GelMA nanocomposite for different applications, such as cardiac, bone, and neural regeneration.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.002 |
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".