Dendritic polyglycerol-conjugated gold nanostars with different densities of functional groups to regulate osteogenesis in human mesenchymal stem cells
Bibliographic record
Abstract
Nanomaterials play an important role in mimicking the biochemical and biophysical cues of the extracellular matrix in human mesenchymal stem cells (MSCs). Increasing studies have demonstrated the crucial impact of functional groups on MSCs, while limited research is available on how the functional group's density on nanoparticles regulates MSC behavior. Herein, the effects of dendritic polyglycerol (dPG)-conjugated gold nanostars (GNSs) with different densities of functional groups on the osteogenesis of MSCs are systematically investigated. dPG@GNS nanocomposites have good biocompatibility and the uptake by MSCs is in a functional group density-dependent manner. The osteogenic differentiation of MSCs is promoted by all dPG@GNS nanocomposites, in terms of alkaline phosphatase activity, calcium deposition, and expression of osteogenic protein and genes. Interestingly, the dPGOH@GNSs exhibit a slight upregulation in the expression of osteogenic markers, while the different charged densities of sulfate and amino groups show more efficacy in the promotion of osteogenesis. Meanwhile, the sulfated nanostars dPGS20@GNSs show the highest enhancement. Furthermore, various dPG@GNS nanocomposites exerted their effects by regulating the activation of Yes-associated protein (YAP) to affect osteogenic differentiation. These results indicate that dPG@GNS nanocomposites have functional group density-dependent influence on the osteogenesis of MSCs, which may provide a new insight into regulating stem cell fate.
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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) | 0.000 | 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".