Cellularizing hydrogel-based scaffolds to repair bone tissue: How to create a physiologically relevant micro-environment?
Bibliographic record
Abstract
Tissue engineering is a promising alternative to autografts or allografts for the regeneration of large bone defects. Cell-free biomaterials with different degrees of sophistication can be used for several therapeutic indications, to stimulate bone repair by the host tissue. However, when osteoprogenitors are not available in the damaged tissue, exogenous cells with an osteoblast differentiation potential must be provided. These cells should have the capacity to colonize the defect and to participate in the building of new bone tissue. To achieve this goal, cells must survive, remain in the defect site, eventually proliferate, and differentiate into mature osteoblasts. A critical issue for these engrafted cells is to be fed by oxygen and nutrients: the transient absence of a vascular network upon implantation is a major challenge for cells to survive in the site of implantation, and different strategies can be followed to promote cell survival under poor oxygen and nutrient supply and to promote rapid vascularization of the defect area. These strategies involve the use of scaffolds designed to create the appropriate micro-environment for cells to survive, proliferate, and differentiate in vitro and in vivo. Hydrogels are an eclectic class of materials that can be easily cellularized and provide effective, minimally invasive approaches to fill bone defects and favor bone tissue regeneration. Furthermore, by playing on their composition and processing, it is possible to obtain biocompatible systems with adequate chemical, biological, and mechanical properties. However, only a good combination of scaffold and cells, possibly with the aid of incorporated growth factors, can lead to successful results in bone regeneration. This review presents the strategies used to design cellularized hydrogel-based systems for bone regeneration, identifying the key parameters of the many different micro-environments created within hydrogels.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".