Injectable Self‐Polymerizing Hydrogel as Bone Filler for Bone Defect Treatment
Bibliographic record
Abstract
Poly(methylemethacrylate) (PMMA) bone cement treats bone defects that have a sol-gel behavior that allows injection application. However, it raises concerns such as leakage, toxicity, infection, and incompatible porous structure. Here, we report an injectable gel catalyzed by silver nanoparticles (AgNPs), allowing highly efficient and biocompatible in situ polymerization to fill up the defects and eliminate infection. This gel consists of gelatin methacryloyl (GelMA) and methacrylated chitosan (ChiMA), providing a similarity to the extracellular matrix for improved cell growth, where mussel-inspired polydopamine (PDA), reduced nano silver can be a catalyst for free radical polymerization due to its high electron activity. We further found the high surface/volume ratio of micro hydroxyapatite (HA) immobilized AgNPs for enhanced catalytic ability. The Ag-PDA-HA in GelMA/ChiMA produces gelation in a tunable time. After two months, the filling gel could completely eliminate S.aureus and raise bone volume fraction to 49.9% in infected skull models, which is approximately 30% more than contrast groups. The gel has not only increased the volume but also induced the maturation of the newly regenerated bone from H&E staining. Overall, this innovative bone filler has fast polymerization, anti-infection, and proven bone regeneration acceleration.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| 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".