Xenogenic Transplantation of Human Mesenchymal Stem Cells in a Critical Size Defect of the Sheep Tibia for Bone Regeneration

2010 ◽  
Vol 16 (1) ◽  
pp. 33-43 ◽  
Author(s):  
Philipp Niemeyer ◽  
Thomas S. Schönberger ◽  
Joachim Hahn ◽  
Philip Kasten ◽  
Joerg Fellenberg ◽  
...  
Bone ◽  
2010 ◽  
Vol 47 (1) ◽  
pp. 117-126 ◽  
Author(s):  
Giorgio Burastero ◽  
Sonia Scarfì ◽  
Chiara Ferraris ◽  
Chiara Fresia ◽  
Nadia Sessarego ◽  
...  

Oncotarget ◽  
2017 ◽  
Vol 8 (13) ◽  
pp. 21031-21043 ◽  
Author(s):  
Deting Xue ◽  
Erman Chen ◽  
Wei Zhang ◽  
Xiang Gao ◽  
Shengdong Wang ◽  
...  

2020 ◽  
Author(s):  
Laurence Burroughs ◽  
Mahetab H. Amer ◽  
Matthew Vassey ◽  
Britta Koch ◽  
Grazziela P Figueredo ◽  
...  

AbstractHuman mesenchymal stem cells (hMSCs) are widely represented in ongoing regenerative medicine clinical trials due to their ease of autologous implantation. In bone regeneration, crosstalk between macrophages and hMSCs is critical with macrophages playing a key role in the recruitment and differentiation of hMSCs. However, engineered biomaterials able to both direct hMSC fate and modulate macrophage phenotype have not yet been identified. A novel combinatorial chemistry-microtopography screening platform, the ChemoTopoChip, is used to identify materials suitable for bone regeneration by screening with human immortalized mesenchymal stem cells (hiMSCs) and human macrophages. The osteoinduction achieved in hiMSCs cultured on the “hit” materials in basal media is comparable to that seen when cells are cultured in osteogenic media, illustrating that these materials offer a materials-induced alternative in bone-regenerative applications. These also exhibit immunomodulatory effects, concurrently polarizing macrophages towards a pro-healing phenotype. Control of cell response is achieved when both chemistry and topography are recruited to instruct the required cell phenotype, combining synergistically. The large library of materials reveals that the relative roles of microtopography and material chemistry are similar, and machine learning identifies key material and topographical features for cell-instruction.


Author(s):  
Shah Sarita ◽  
Tatara Alexander ◽  
Santoro Marco ◽  
Henslee Allan ◽  
Guldberg Robert ◽  
...  

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