Immiscible Phase Separation‐Driven Microfabrication of Gelatin Methacryloyl Scaffolds for BMP‐2 Delivery and Osteogenic Enhancement
Bibliographic record
Abstract
Gelatin methacryloyl (GelMA) hydrogels are recognized for their biocompatibility, tunable mechanics, and ability to support cellular functions, making them attractive for tissue engineering. However, achieving uniform, structurally stable micro-scaffolds for minimally invasive delivery remains challenging. Injectable hydrogels provide targeted delivery but lack the micro-architectural complexity required for effective regeneration, while 3D printing offers precision yet faces resolution, handling, and mechanical limitations. To overcome these barriers, we developed injectable GelMA micro-scaffolds (mS-GelMA) with controlled porosity, stability, and reproducibility using stop-flow lithography (SFL). This technique enables precise control over shape, porosity, and degradation, surpassing conventional injection moulding and 3D bioprinting in micro-particle uniformity and reproducibility. Scaffold performance was optimized by incorporating trimethylolpropane triacrylate (TMPTA) into GelMA, enhancing drug delivery and regenerative potential. Cellular assays confirmed high biocompatibility and functionality, with human mesenchymal stem cells (hMSCs) exhibiting excellent viability, migration, and osteogenic differentiation within the mS-GelMA scaffolds. These findings demonstrate that SFL-fabricated GelMA scaffolds bridge the gap between injectability and structural complexity, offering a promising platform for minimally invasive tissue engineering. This work highlights the potential of SFL-engineered hydrogels to advance scaffold-based regenerative medicine by combining architectural precision, biological performance, and clinical applicability.
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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 | 0.001 |
| 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".