Co‐Delivery of Ca‐MOF and Mg‐MOF Using Nanoengineered Hydrogels to Promote In Situ Mineralization and Bone Defect Repair: In Vitro and In Vivo Analysis
Bibliographic record
Abstract
Abstract Severe bone defects resulting from traumatic injuries or infections are severe skeletal deficiencies that are unable to regenerate on their own. Despite their effectiveness, current treatments including allografts and artificial bone substitutes, have several drawbacks. This includes poor osseointegration, low biocompatibility and biodegradability, limited cell infiltration, and adverse side effects arising from drug‐loaded substitutes. To overcome these challenges, mineral‐based metal–organic frameworks (MOFs) nanoparticles are successfully synthesized and incorporated into polymeric hydrogels to promote bone healing. The study demonstrates that the combination of Ca‐MOF and Mg‐MOF (Ca/Mg‐MOF) nanoparticles, when incorporated into a hydrogel scaffold, can take various forms: sprayable, injectable, and coating material for orthopedic implants. Furthermore, nanoengineered hydrogels significantly enhance osteogenic differentiation and mineral deposition of preosteoblast cells compared to control groups and individual MOFs. This osteogenic property can be attributed to the cumulative release of Ca 2+ and Mg 2+ that reached 62.89% ± 3.05 and 18.60% ± 0.65 by day 8, respectively. Micro‐computed tomography and histological analysis of rat model with critical‐size bone defects demonstrate that the bioactive hydrogel can significantly improve new bone formation without using any supplemental drug molecules. These findings underscore the clinical significance of nanoengineered mineral‐based hydrogels to promote osteogenesis and accelerate bone healing.
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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) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 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".