3D Bioprinted Scaffolds for Bone Tissue Engineering: State-Of-The-Art and Emerging Technologies
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Bibliographic record
Abstract
Treating large bone defects, known as critical-sized defects (CSDs), is challenging because they are not spontaneously healed by the patient’s body. Due to the limitations associated with conventional bone grafts, bone tissue engineering (BTE), based on three-dimensional (3D) bioprinted scaffolds, has emerged as a promising approach for bone reconstitution and treatment. Bioprinting technology allows for incorporation of living cells and/or growth factors into scaffolds aiming to mimic the structure and properties of the native bone. To date, a wide range of biomaterials (either natural or synthetic polymers), as well as various cells and growth factors, have been explored for use in scaffold bioprinting. However, a key challenge that remains is the fabrication of scaffolds that meet structure, mechanical, and osteoconductive requirements of native bone and support vascularization. In this review, we briefly present the latest developments and discoveries of CSD treatment by means of bioprinted scaffolds, with a focus on the biomaterials, cells, and growth factors for formulating bioinks and their bioprinting techniques. Promising state-of-the-art pathways or strategies recently developed for bioprinting bone scaffolds are highlighted, including the incorporation of bioactive ceramics to create composite scaffolds, the use of advanced bioprinting technologies ( e.g. , core/shell bioprinting) to form hybrid scaffolds or systems, as well as the rigorous design of scaffolds by taking into account of the influence of such parameters as scaffold pore geometry and porosity. We also review in-vitro assays and in-vivo models to track bone regeneration, followed by a discussion of current limitations associated with 3D bioprinting technologies for BTE. We conclude this review with emerging approaches in this field, including the development of gradient scaffolds, four-dimensional (4D) printing technology via smart materials, organoids, and cell aggregates/spheroids along with future avenues for related BTE.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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 it