Injectable cell‐laden hybrid bioactive scaffold containing bioactive glass microspheres
Bibliographic record
Abstract
The rising incidence of bone disorders has resulted in the need for minimally invasive therapies to meet this demand. Injectable bioactive filler, alone or with cells, could be applied in a minimally invasive manner to fulfill irregular cavities in non-load bearing sites, which do not require high mechanical properties. Thermosensitive chitosan hydrogels that transition from a liquid to a mechanically stable solid at body temperature provide interesting features as in-situ injectable cytocompatible biomaterials, but they are not osteoconductive. Osteoconductivity can be applied in combination with bioactive ceramics e.g., 45S5-Bioglass® (BG). However, BG addition in chitosan hydrogels results in pH elevation, due to rapid ions release, which adversely affects gel formation, mechanical properties, and cytocompatibility. To address this, we created hybrid hydrogels, where BG is concentrated in chitosan-based microbeads, incorporated in in-situ gelling chitosan hydrogels. We then compared the hybrid hydrogels' properties to chitosan hydrogels with homogenously distributed BG. By varying the stirred emulsification process, BG percentage, and CH formulation, we could tune the microbeads' properties. Incorporation of BG microbeads drastically improved the hydrogel's compressive modulus in comparison to homogeneously distributed BG. It also strongly increased the survival and metabolic activities of encapsulated cells. Calcium/phosphate increase on BG microbeads suggests hydroxyapatite formation. The small diameter of microbeads allows minimally invasive injection through small needles. The feasibility of freezing and thawing microbeads provides the possibility of long-term storage for potential clinical applications. These data indicate that this hybrid hydrogel forms a promising injectable cell-laden bioactive biomaterial for the treatment of unloaded bone defects.
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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) | 0.000 | 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) | 0.001 | 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".