Development of bioactive short fiber-reinforced printable hydrogels with tunable mechanical and osteogenic properties for bone repair
Bibliographic record
Abstract
Personalized bone-regenerative materials have attracted substantial interest in recent years. Modern clinical settings demand the use of engineered materials incorporating patient-derived cells, cytokines, antibodies, and biomarkers to enhance the process of regeneration. In this work, we formulated short microfiber-reinforced hydrogels with platelet-rich fibrin (PRF) to engineer implantable multi-material core-shell bone grafts. By employing 3D bioprinting technology, we fabricated a core-shell bone graft from a hybrid composite hydroxyapatite-coated poly(lactic acid) (PLA) fiber-reinforced methacryolyl gelatin (GelMA)/alginate hydrogel. The overall concept involves 3D bioprinting of long bone mimic microstructures that resemble a core-shell cancellous-cortical structure, with a stiffer shell and a softer core with our engineered biomaterial. We observed a significantly enhanced stiffness in the hydrogel scaffold incorporated with hydroxyapatite (HA)-coated PLA microfibers compared to the pristine hydrogel construct. Furthermore, HA non-coated PLA microfibers were mixed with PRF and GelMA/alginate hydrogel to introduce a slow release of growth factors which can further enhance cell maturation and differentiation. These patient-specific bone grafts deliver cytokines and growth factors with distinct spatiotemporal release profiles to enhance tissue regeneration. The biocompatible and bio-responsive bone mimetic core-shell multi-material structures enhance osteogenesis and can be customized to have materials at a specific location, geometry, and material combination.
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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 | 0.001 | 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".