Use of transforming growth factor‐β loaded onto β‐tricalcium phosphate scaffold in a bone regeneration rat calvaria model
Bibliographic record
Abstract
Abstract Background Transforming growth factor‐β (TGF‐β1) enhances mesenchymal stem cell (MSC) differentiation into osteoblasts. Purpose The aim of the study was to assess whether TGF‐β1 loaded onto β‐tricalcium phosphate (β‐TCP) synthetic scaffold enhances bone regeneration in a rat calvaria model. The release kinetics of TGF‐β1 from β‐TCP scaffold was evaluated in vitro. Materials and Methods TGF‐β1 in various concentrations (1‐40 ng/mL) was loaded onto the β‐TCP scaffold, and release kinetics was monitored by ELISA. The effect of TGF‐β1 on the proliferation of MSCs was assessed using AlamarBlue, and MSC differentiation was evaluated by Alizarin Red quantification assay.Bone augmentation following transplantation of TGF‐β1 loaded onto β‐TCP in a rat calvaria model was evaluated in vivo. Results Greater TGF‐β1 release from the 40 ng/mL concentration was found. A suppressive effect of TGF‐β on the MSCs proliferation was observed with maximum inhibition obtained with 40 ng/mL compared to the control group (P = .028). A positive effect on MSCs osteogenic differentiation was found.Bone height and bone area fraction in vivo were similar with or without TGF‐β1; however, blood vessel density and degradation of the scaffold were significantly higher in the TGF‐β1 group. Conclusion TGF‐β1 adsorbed to β‐TCP stimulated angiogenesis and scaffold degradation that may enhance bone formation.
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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 | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| 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".