Effects of a Bioactive Scaffold Containing a Sustained TGF-β1-releasing Nanoparticle System on the Migration and Differentiation of Stem Cells from the Apical Papilla
Bibliographic record
Abstract
The study aimed to develop and characterize a novel chitosan-based scaffold (CMCS) containing TGF-β1-releasing chitosan nanoparticles (TGF-β1-CSnp) to enhance migration and differentiation of SCAP. Part I concerned synthesis and characterization of the scaffold (CMCS) and TGF-β1-CSnp. Part II examined the effect of sustained TGF-β1 release from scaffold containing TGF-β1-CSnp on odontogenic differentiation of SCAP. The scaffold demonstrated properties conducive to cellular activities. Incorporation of TGF-β1 in CSnp allowed sustained release of TGF-β1 facilitating delivery of a critical concentration of TGF-β1 at the opportune time. SCAP showed greater viability, migration and biomineralization in the presence of TGF-β1-CSnp than in the presence of Free TGF-β1. SCAP cultured in TGF-β1-CSnp + scaffold showed significantly higher dentin matrix protein (DMP)-1 and dentin sialophosphoprotein (DSPP) signals compared to Free TGF-β1 + scaffold or CSnp + scaffold. These experiments highlighted the potential of a CMCS based scaffold with growth factor releasing nanoparticles to promote migration and differentiation of SCAP.
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 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) | 0.000 | 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 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".