Relationship between the morphological, mechanical and permeability properties of porous bone scaffolds and the underlying microstructure

Bone scaffolds are widely used as one of the main bone substitute materials. However, many bone scaffold microstructure topologies exist and it is still unclear which topology to use when designing scaffold for a specific application. The aim of the present study was to reveal the mechanism of the microstructure-driven performance of bone scaffold and thus to provide guideline on scaffold design. Finite element (FE) models of five TPMS (Diamond, Gyroid, Schwarz P, Fischer-Koch S and F-RD) and three traditional (Cube, FD-Cube and Octa) scaffolds were generated. The effective compressive and shear moduli of scaffolds were calculated from the mechanical analysis using the FE unit cell models with the periodic boundary condition. The scaffold permeability was calculated from the computational fluid dynamics (CFD) analysis using the 4×4×4 FE models. It is revealed that the surface-to-volume ratio of the Fischer-Koch S-based scaffold is the highest among the scaffolds investigated. The mechanical analysis revealed that the bending deformation dominated structures (e.g., the Diamond, the Gyroid, the Schwarz P) have higher effective shear moduli. The stretching deformation dominated structures (e.g., the Schwarz P, the Cube) have higher effective compressive moduli. For all the scaffolds, when the same amount of change in scaffold porosity is made, the corresponding change in the scaffold relative shear modulus is larger than that in the relative compressive modulus. The CFD analysis revealed that the structures with the simple and straight pores (e.g., Cube) have higher permeability than the structures with the complex pores (e.g., Fischer-Koch S). The main contribution of the present study is that the relationship between scaffold properties and the underlying microstructure is systematically investigated and thus some guidelines on the design of bone scaffolds are provided, for example, in the scenario where a high surface-to-volume ratio is required, it is suggested to use the Fischer-Koch S based scaffold

Group Network Hawkes Process

In this work, we study the event occurrences of individuals interacting in a network. To characterize the dynamic interactions among the individuals, we propose a group network Hawkes process (GNHP) model whose network structure is observed and fixed. In particular, we introduce a latent group structure among individuals to account for the heterogeneous user-specific characteristics. A maximum likelihood approach is proposed to simultaneously cluster individuals in the network and estimate model parameters. A fast EM algorithm is subsequently developed by utilizing the branching representation of the proposed GNHP model. Theoretical properties of the resulting estimators of group memberships and model parameters are investigated under both settings when the number of latent groups $G$ is over-specified or correctly specified. A data-driven criterion that can consistently identify the true $G$ under mild conditions is derived. Extensive simulation studies and an application to a data set collected from Sina Weibo are used to illustrate the effectiveness of the proposed methodology.Comment: 35 page

Modeling Social Media User Content Generation Using Interpretable Point Process Models

In this article, we study the activity patterns of modern social media users on platforms such as Twitter and Facebook. To characterize the complex patterns we observe in users' interactions with social media, we describe a new class of point process models. The components in the model have straightforward interpretations and can thus provide meaningful insights into user activity patterns. A composite likelihood approach and a composite EM estimation procedure are developed to overcome the challenges that arise in parameter estimation. Using the proposed method, we analyze Donald Trump's Twitter data and study if and how his tweeting behavior evolved before, during and after the presidential campaign. Additionally, we analyze a large-scale social media data from Sina Weibo and identify interesting groups of users with distinct behaviors; in this analysis, we also discuss the effect of social ties on a user's online content generating behavior

A review on the mechanical metamaterials and their applications in the field of biomedical engineering

Metamaterials are a group of materials/structures which possess novel behaviors not existing in nature. The metamaterials include electromagnetic metamaterials, acoustic metamaterials, mechanical metamaterials, etc. among which the mechanical metamaterials are widely used in the field of biomedical engineering. The mechanical metamaterials are the ones that possess special mechanical behaviors, e.g., lightweight, negative Poisson’s ratio, etc. In this paper, the commonly used mechanical metamaterials are reviewed and their applications in the field of biomedical engineering, especially in bone tissue engineering and vascular stent, are discussed. Finally, the future perspectives of this field are given

Predictive assembling model reveals the self-adaptive elastic properties of lamellipodial actin networks for cell migration

Branched actin network supports cell migration through extracellular microenvironments. However, it is unknown how intracellular proteins adapt the elastic properties of the network to the highly varying extracellular resistance. Here we develop a three-dimensional assembling model to simulate the realistic self-assembling process of the network by encompassing intracellular proteins and their dynamic interactions. Combining this multiscale model with finite element method, we reveal that the network can not only sense the variation of extracellular resistance but also self-adapt its elastic properties through remodeling with intracellular proteins. Such resistance-adaptive elastic behaviours are versatile and essential in supporting cell migration through varying extracellular microenvironments. The bending deformation mechanism and anisotropic Poisson’s ratios determine why lamellipodia persistently evolve into sheet-like structures. Our predictions are confirmed by published experiments. The revealed self-adaptive elastic properties of the networks are also applicable to the endocytosis, phagocytosis, vesicle trafficking, intracellular pathogen transport and dendritic spine formation

Effect of parathyroid hormone on the structural, densitometric and failure behaviours of mouse tibia in the spatiotemporal space

Parathyroid hormone (PTH) is an anabolic bone drug approved by the US Food and Drug Administration (FDA) to treat osteoporosis. However, previous studies using cross-sectional designs have reported variable and sometimes contradictory results. The aim of the present study was to quantify the localized effect of PTH on the structural and densitometric behaviors of mouse tibia and their links with the global mechanical behavior of bone using a novel spatiotemporal image analysis approach and a finite element analysis technique. Twelve female C57BL/6J mice were divided into two groups: the control and PTH treated groups. The entire right tibiae were imaged using an in vivo micro-computed tomography (μCT) system eight consecutive times. Next, the in vivo longitudinal tibial μCT images were rigidly registered and divided into 10 compartments across the entire tibial space. The bone volume (BV), bone mineral content (BMC), bone tissue mineral density (TMD), and tibial endosteal and periosteal areas (TEA and TPA) were quantified in each compartment. Additionally, finite element models of all the tibiae were generated to analyze the failure behavior of the tibia. It was found that both the BMC and BV started to increase in the proximal tibial region, and then the increases extended to the entire tibial region after two weeks of treatment (p < 0.05). PTH intervention significantly reduced the TEA in most tibial compartments after two weeks of treatment, and the TPA increased in most tibial regions after four weeks of treatment (p < 0.05). Tibial failure loads significantly increased after three weeks of PTH treatment (p < 0.01). The present study provided the first evidence of the localized effect of PTH on bone structural and densitometric properties, as well as their links with the global mechanical behaviors of bone, which are important pieces of information for unveiling the mechanism of PTH intervention

Stem Cell Tracking Technologies for Neurological Regenerative Medicine Purposes

The growing field of stem cell therapy is moving toward clinical trials in a variety of applications, particularly for neurological diseases. However, this translation of cell therapies into humans has prompted a need to create innovative and breakthrough methods for stem cell tracing, to explore the migration routes and its reciprocity with microenvironment targets in the body, to monitor and track the outcome after stem cell transplantation therapy, and to track the distribution and cell viability of transplanted cells noninvasively and longitudinally. Recently, a larger number of cell tracking methods in vivo were developed and applied in animals and humans, including magnetic resonance imaging, nuclear medicine imaging, and optical imaging. This review has been intended to summarize the current use of those imaging tools in tracking stem cells, detailing their main features and drawbacks, including image resolution, tissue penetrating depth, and biosafety aspects. Finally, we address that multimodality imaging method will be a more potential tracking tool in the future clinical application

Designing anisotropic porous bone scaffolds using a self-learning convolutional neural network model

The design of bionic bone scaffolds to mimic the behaviors of native bone tissue is crucial in clinical application, but such design is very challenging due to the complex behaviors of native bone tissues. In the present study, bionic bone scaffolds with the anisotropic mechanical properties similar to those of native bone tissues were successfully designed using a novel self-learning convolutional neural network (CNN) framework. The anisotropic mechanical property of bone was first calculated from the CT images of bone tissues. The CNN model constructed was trained and validated using the predictions from the heterogonous finite element (FE) models. The CNN model was then used to design the scaffold with the elasticity matrix matched to that of the replaced bone tissues. For the comparison, the bone scaffold was also designed using the conventional method. The results showed that the mechanical properties of scaffolds designed using the CNN model are closer to those of native bone tissues. In conclusion, the self-learning CNN framework can be used to design the anisotropic bone scaffolds and has a great potential in the clinical application

The Special Neuraminidase Stalk-Motif Responsible for Increased Virulence and Pathogenesis of H5N1 Influenza A Virus

The variation of highly pathogenic avian influenza H5N1 virus results in gradually increased virulence in poultry, and human cases continue to accumulate. The neuraminidase (NA) stalk region of influenza virus varies considerably and may associate with its virulence. The NA stalk region of all N1 subtype influenza A viruses can be divided into six different stalk-motifs, H5N1/2004-like (NA-wt), WSN-like, H5N1/97-like, PR/8-like, H7N1/99-like and H5N1/96-like. The NA-wt is a special NA stalk-motif which was first observed in H5N1 influenza virus in 2000, with a 20-amino acid deletion in the 49th to 68th positions of the stalk region. Here we show that there is a gradual increase of the special NA stalk-motif in H5N1 isolates from 2000 to 2007, and notably, the special stalk-motif is observed in all 173 H5N1 human isolates from 2004 to 2007. The recombinant H5N1 virus with the special stalk-motif possesses the highest virulence and pathogenicity in chicken and mice, while the recombinant viruses with the other stalk-motifs display attenuated phenotype. This indicates that the special stalk-motif has contributed to the high virulence and pathogenicity of H5N1 isolates since 2000. The gradually increasing emergence of the special NA stalk-motif in H5N1 isolates, especially in human isolates, deserves attention by all