G
Georg N. Duda
Researcher at Charité
Publications - 613
Citations - 31004
Georg N. Duda is an academic researcher from Charité. The author has contributed to research in topics: Bone healing & Bone regeneration. The author has an hindex of 81, co-authored 563 publications receiving 25802 citations. Previous affiliations of Georg N. Duda include Humboldt University of Berlin & University of Ulm.
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Skeletal muscle regeneration using mesenchymal stem cells
TL;DR: In this article, the use of mesenchymal stem cells (MSCs) for skeletal muscle regeneration using a therapeutic substance and/or a medicament is described, which can be applied directly or shortly after muscle damage or injury.
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The specialist in regeneration—the Axolotl—a suitable model to study bone healing?
A. Polikarpova,Agnes Ellinghaus,Oskar Schmidt-Bleek,Lidia Grosser,Christian H. Bucher,Georg N. Duda,Eiichi Tanaka,Kate Schmidt-Bleek +7 more
TL;DR: In this article , a plate-fixated femur was used to stabilize the osteotomized axolotl femur with a fixator plate and compared it to a non-stabilized osteotomy and to limb amputation.
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ACL Deficient Patients With Passive Knee Joint Instability Overcompensate During Active Movements
Tobias Jung,Markus O. Heller,Sebastian Kopf,R. Doyscher,Sven Scheffler,Georg N. Duda,William R. Taylor,Heide Boeth +7 more
TL;DR: This study aimed to define the role of passive joint stability on active tibio-femoral kinematics during walking and demonstrated the high repeatability of the novel approach for assessing tibiospecific kinematic between trials.
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Overstretching Expectations May Endanger the Success of the “Millennium Surgery”
TL;DR: Age was proven to be a discriminator in joint loading, with working age patients presenting with increased loads compared to retirement age patients, already during daily activities, and the need to provide critical feedback to patient expectations when returning to work and sports activities is illustrated.
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Aging and Injury Affect Nuclear Shape Heterogeneity in Tendon.
TL;DR: In this paper , the authors combined a VISually Aided Morpho-Phenotyping Image Recognition (VAMPIRE) analysis that automatically segments cells based on their shape with an added capacity to further discriminate between cells in certain protein-rich extracellular matrix regions.