93 research outputs found

    Metformin and asarone inhibit HepG2 cell proliferation in a high glucose environment by regulating AMPK and Akt signaling pathway.

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    Background: Metabolic dysregulation is one of the hallmarks of tumor cell proliferation. Evidence indicates the potential role of the 5′adenosine monophosphate-activated protein kinase (AMPK) and protein kinase B/Akt signaling pathway in regulating cell proliferation, survival, and apoptosis. The present study explores the effect of metformin HCl and the combination of α- and β-asarone on the proliferation of HepG2 cells in the presence of high glucose levels simulating the diabetic-hepatocellular carcinoma (HCC) condition. Results: The metformin and asarone reduced HepG2 cell viability in a dose-dependent manner and induced morphological changes as indicated by methyl thiazolyl tetrazolium (MTT) assay. The metformin and asarone arrested the cells at the G0/G1 phase, upregulated the expression of AMPK, and downregulated Akt expression in high glucose conditions as identified by the flow cytometry technique. Further, the upregulated AMPK led to a decrease in the expression of phosphoenolpyruvate carboxykinase-2 (PCK-2) and sterol regulatory element-binding protein-1 (SREBP-1). Conclusion: The anti-proliferative effect of metformin and asarone in the diabetic-HCC condition is mediated via AMPK and Akt pathway

    Competing risk multistate censored data modeling by propensity score matching method

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    The potential contribution of the paper is the use of the propensity score matching method for updating censored observations within the context of multi-state model featuring two competing risks.The competing risks are modelled using cause-specific Cox proportional hazard model.The simulation findings demonstrate that updating censored observations tends to lead to reduced bias and mean squared error for all estimated parameters in the risk of cause-specific Cox model.The results for a chemoradiotherapy real dataset are consistent with the simulation results

    Competing risk multistate censored data modeling by propensity score matching method

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    The potential contribution of the paper is the use of the propensity score matching method for updating censored observations within the context of multi-state model featuring two competing risks.The competing risks are modelled using cause-specific Cox proportional hazard model.The simulation findings demonstrate that updating censored observations tends to lead to reduced bias and mean squared error for all estimated parameters in the risk of cause-specific Cox model.The results for a chemoradiotherapy real dataset are consistent with the simulation results

    Fast Subject Specific Finite Element Mesh Generation of Knee Joint from Biplanar X-ray Images

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    Numerous finite element (FE) models of the knee joint have been developed to investigate knee pathology, post-surgery assessment and natural knee biomechanics. However, because of the extensive computational effort required for preparing subject specific model from CT-scan or MRI data, most of the models in literature are done only for one subject resulting in poor validation of the model and limits the predictive power of the conclusions. Biplanar X-ray is a promising alternative to perform 3D reconstruction of bony structures because of low radiation dose and very less reconstruction time [1]. Moreover, an accurate and fast computational mesh is a prerequisite for generating subject specific mesh in order to perform personalized FE analysis. Traditionally, both triangular/tetrahedral and quadrilateral/hexahedral FE elements are used for 3D mesh generation. But because of distinct numerical advantages quadrilateral/hexahedral elements are preferred to avoid numerical instability, specifically for problems involving high strains at soft tissues [2]. The aim of the current study is to develop fast and automatic subject specific mesh for knee joint from biplanar X-ray images. This approach was successfully tested for 6 cadaveric specimen, where from the biplanar radiographic images of each, 3D reconstruction models were built with a mean time of about 10 min for each specimen by adapting the strategy of [1]. From the reconstruction models, subject specific mesh (4 noded shell) for bony and cartilage structures were generated based on the mapping from the generic model to subject specific model with about 10 sec of time for each specimen (Fig. 1). Both the meniscus were meshed with 8 noded hex elements using the nodes of femoral and tibial cartilage in a dedicated Matlab code with numerical cost of almost 1 min. So, a total of about 12 min computational time was required to build each subject specific knee from 3D reconstruction to mesh generation which is promising for clinical applications. Quality of mesh for individual specimen was also checked using mesh quality indicators (Jacobian ratio, aspect ratio etc.) and surface representation accuracy, which showed less than 1% (warning only) and 0.8 mm (at soft tissue regions) respectively for individual specimen

    Design for additive manufacturing: Trends, opportunities, considerations, and constraints

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    The past few decades have seen substantial growth in Additive Manufacturing (AM) technologies. However, this growth has mainly been process-driven. The evolution of engineering design to take advantage of the possibilities afforded by AM and to manage the constraints associated with the technology has lagged behind. This paper presents the major opportunities, constraints, and economic considerations for Design for Additive Manufacturing. It explores issues related to design and redesign for direct and indirect AM production. It also highlights key industrial applications, outlines future challenges, and identifies promising directions for research and the exploitation of AM's full potential in industry

    Development and evaluation of a new procedure for subject-specific tensioning of finite element knee ligaments

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    Subject-specific tensioning of ligaments is essential for the stability of the knee joint and represents a challenging aspect in the development of finite element models. We aimed to introduce and evaluate a new procedure for the quantification of ligament prestrains from biplanar X-ray and CT data. Subject-specific model evaluation was performed by comparing predicted femorotibial kinematics with the in vitro response of six cadaveric specimens. The differences obtained using personalized models were comparable to those reported in similar studies in the literature. This study is the first step toward the use of simplified, personalized knee FE models in clinical context such as ligament balancing
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