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1.  Magnesium Alloys as a Biomaterial for Degradable Craniofacial Screws 
Acta biomaterialia  2013;10(5):2323-2332.
Recently, magnesium (Mg) alloys have received significant attention as a potential biomaterial for degradable implants, and this study was directed at evaluating the suitability of Mg for craniofacial bone screws. The objective was to implant screws fabricated from commercially available Mg-alloys (pure Mg and AZ31) in-vivo in a rabbit mandible. First, Mg-alloy screws were compared to stainless steel screws in an in-vitro pull-out test and determined to have a similar holding strength (~40N). A finite element model of the screw was created using the pull-out test data, and the model can be used for future Mg-alloy screw design. Then, Mg-alloy screws were implanted for 4, 8, and 12 weeks, with two controls of an osteotomy site (hole) with no implant and a stainless steel screw implanted for 12 weeks. MicroCT (computed tomography) was used to assess bone remodeling and Mg-alloy degradation, both visually and qualitatively through volume fraction measurements for all time points. Histologic analysis was also completed for the Mg-alloys at 12 weeks. The results showed that craniofacial bone remodeling occurred around both Mg-alloy screw types. Pure Mg had a different degradation profile than AZ31, however bone growth occurred around both screw types. The degradation rate of both Mg-alloy screw types in the bone marrow space and the muscle were faster than in the cortical bone space at 12 weeks. Furthermore, it was shown that by alloying Mg, the degradation profile could be changed. These results indicate the promise of using Mg-alloys for craniofacial applications.
doi:10.1016/j.actbio.2013.12.040
PMCID: PMC3976705  PMID: 24384125
biodegradable metal; magnesium; craniofacial implants; finite element modeling
2.  A mechanistic model on the role of “radially-running” collagen fibers on dissection properties of human ascending thoracic aorta 
Journal of biomechanics  2014;47(5):981-988.
Aortic dissection (AoD) is a common condition that often leads to life-threatening cardiovaular emergency. From a biomechanics viewpoint, AoD involves failure of load-bearing microstructural components of the aortic wall, mainly elastin and collagen fibers. Delamination strength of the aortic wall depends on the load-bearing capacity and local micro-architecture of these fibers, which may vary with age, disease and aortic location. Therefore, quantifying the role of fiber micro-architecture on the delamination strength of the aortic wall may lead to improved understanding of AoD. We present an experimentally-driven modeling paradigm towards this goal. Specifically, we utilize collagen fiber microarchitecture, obtained in a parallel study from multi-photon microopy, in a predictive mechanistic framework to characterize the delamination strength. We then validate our model against peel test experiments on human aortic strips and utilize the model to predict the delamination strength of separate aortic strips and compare with experimental findings. We observe that the number density and failure energy of the radially-running collagen fibers control the peel strength. Furthermore, our model suggests that the lower delamination strength previously found for the circumferential direction in human aorta is related to a lower number density of radially-running collagen fibers in that direction. Our model sets the stage for an expanded future study that could predict AoD propagation in patient-specific aortic geometries and better understand factors that may influence propensity for occurrence.
doi:10.1016/j.jbiomech.2014.01.005
PMCID: PMC4082402  PMID: 24484644
Peel force; Aorta; Dissection; Collagen fibers; Fiber bridge failure model
3.  An integer programming formulation to identify the sparse network architecture governing differentiation of embryonic stem cells 
Bioinformatics  2010;26(10):1332-1339.
Motivation: Primary purpose of modeling gene regulatory networks for developmental process is to reveal pathways governing the cellular differentiation to specific phenotypes. Knowledge of differentiation network will enable generation of desired cell fates by careful alteration of the governing network by adequate manipulation of cellular environment.
Results: We have developed a novel integer programming-based approach to reconstruct the underlying regulatory architecture of differentiating embryonic stem cells from discrete temporal gene expression data. The network reconstruction problem is formulated using inherent features of biological networks: (i) that of cascade architecture which enables treatment of the entire complex network as a set of interconnected modules and (ii) that of sparsity of interconnection between the transcription factors. The developed framework is applied to the system of embryonic stem cells differentiating towards pancreatic lineage. Experimentally determined expression profile dynamics of relevant transcription factors serve as the input to the network identification algorithm. The developed formulation accurately captures many of the known regulatory modes involved in pancreatic differentiation. The predictive capacity of the model is tested by simulating an in silico potential pathway of subsequent differentiation. The predicted pathway is experimentally verified by concurrent differentiation experiments. Experimental results agree well with model predictions, thereby illustrating the predictive accuracy of the proposed algorithm.
Contact: ipb1@pitt.edu
Supplementary information: Supplementary data are available at Bioinformatics online.
doi:10.1093/bioinformatics/btq139
PMCID: PMC2865861  PMID: 20363729

Results 1-3 (3)