Two-Dimensional Magnesium Phosphate Nanosheets Form Highly Thixotropic Gels That Up-Regulate Bone Formation
Bibliographic record
Abstract
Hydrogels composed of two-dimensional (2D) nanomaterials have become an important alternative to replace traditional inorganic scaffolds for tissue engineering. Here, we describe a novel nanocrystalline material with 2D morphology that was synthesized by tuning the crystallization of the sodium-magnesium-phosphate system. We discovered that the sodium ion can regulate the precipitation of magnesium phosphate by interacting with the crystal's surface causing a preferential crystal growth that results in 2D morphology. The 2D nanomaterial gave rise to a physical hydrogel that presented extreme thixotropy, injectability, biocompatibility, bioresorption, and long-term stability. The nanocrystalline material was characterized in vitro and in vivo and we discovered that it presented unique biological properties. Magnesium phosphate nanosheets accelerated bone healing and osseointegration by enhancing collagen formation, osteoblasts differentiation, and osteoclasts proliferation through up-regulation of COL1A1, RunX2, ALP, OCN, and OPN. In summary, the 2D magnesium phosphate nanosheets could bring a paradigm shift in the field of minimally invasive orthopedic and craniofacial interventions because it is the only material available that can be injected through high gauge needles into bone defects in order to accelerate bone healing and osseointegration.
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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) | 0.000 | 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".