Advances in bone tissue engineering using biomaterial based scaffolds, purine crosslinking and Wnt signaling
Bibliographic record
Abstract
The design of effective treatments for critical size bone defects, which result from various conditions such as trauma, infection, injury, or tumor resection, presents a significant challenge in clinical practice. While autologous grafts are commonly regarded as gold standard treatments in these complex healing scenarios, they are often associated with notable limitations, including donor site morbidity and limited graft volume. As a result, recent research trends have shifted towards developing biomaterials that better emulate the inherent complexity of the native bone structure and function through implementation of a “Diamond Concept” polytherapy strategy. Central to this approach is the utilization of biomaterials, increasingly composed of composite materials that integrate bioactive osteoinductive factors and cell sources to enhance healing outcomes. The usage of Wnt signaling specific agonists as osteoinductive mediators has been recently shown to be a promising strategy for promoting healing, as this pathway is well established to have an important role in both osteogenic differentiation and bone formation processes. Implementation of a localized delivery system through scaffold incorporation is necessary in this scenario, however, to minimize any potential off-target effects caused by the Wnt signaling cascade’s non-specificity to bone. Findings in the literature clearly show that this approach holds promise to improve clinical healing outcomes, paving the way for more effective treatment options. In this review, we will generally discuss the design of biomaterials, specifically bulk materials and composites, for the treatment of critical size bone defects. Additionally, we will highlight recent work on the design of chitosan-based scaffolds modified with purine crosslinking, to overcome cytotoxicity issues associated with other chemical crosslinkers. In this context, we focus on optimizing material design for this bone healing application and discuss the benefits of localized Wnt agonist as mediators to improve the scaffold’s osteoinductive behavior.
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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) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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 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".