<i>In vitro</i>and<i>in vivo</i>osteogenesis up-regulated by two-dimensional nanosheets through a macrophage-mediated pathway
Bibliographic record
Abstract
Two-dimensional (2D) nanomaterials are attracting more and more interest in regenerative medicine due to their unique properties; however 2D biomimetic calcium mineral has not yet been developed and demonstrated application for bone tissue engineering. Here we described a novel calcium phosphate material with a 2D nanostructure that was synthesized using collagen and sodium alginate as the template. In vitro performance of the nanocrystalline material was evaluated, and we found that 2D CaP nanoparticles (NPs) enhanced the in vitro osteogenic differentiation of rat mesenchymal stem cells (rMSCs) through a macrophage-mediated signal pathway, when co-cultured with RAW 264.7 cells, rather than direct NP/stem cell interaction. A 2D topology structured surface was constructed by encapsulating the CaP nanomaterials in a gelatin hydrogel, which was demonstrated to be able to mediate in vivo ossification through a macrophage polarization related pathway in a femur defect rat model, and allowed the optimal therapeutic outcome compared to normal CaP counterparts. Our current work may have enlightened a new mechanism regarding NP-induced stem cell differentiation through immunoregulation, and the 2D CaP encapsulated hydrogel scaffold may serve as a potential alternative to autograft bone for orthopedic applications.
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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".