Toward an artificial intelligence-assisted framework for reconstructing the digital twin of vertebra and predicting its fracture response.

This article presents an effort toward building an artificial intelligence (AI) assisted framework, coined ReconGAN, for creating a realistic digital twin of the human vertebra and predicting the risk of vertebral fracture (VF). ReconGAN consists of a deep convolutional generative adversarial network (DCGAN), image-processing steps, and finite element (FE) based shape optimization to reconstruct the vertebra model. This DCGAN model is trained using a set of quantitative micro-computed tomography (micro-QCT) images of the trabecular bone obtained from cadaveric samples. The quality of synthetic trabecular models generated using DCGAN are verified by comparing a set of its statistical microstructural descriptors with those of the imaging data. The synthesized trabecular microstructure is then infused into the vertebra cortical shell extracted from the patient's diagnostic CT scans using an FE-based shape optimization approach to achieve a smooth transition between trabecular to cortical regions. The final geometrical model of the vertebra is converted into a high-fidelity FE model to simulate the VF response using a continuum damage model under compression and flexion loading conditions. A feasibility study is presented to demonstrate the applicability of digital twins generated using this AI-assisted framework to predict the risk of VF in a cancer patient with spinal metastasis.

A digital twin for simulating the vertebroplasty procedure and its impact on mechanical stability of vertebra in cancer patients.

We present the application of ReconGAN, introduced in a previous study, for simulating the vertebroplasty (VP) operation and its impact on the fracture response of a vertebral body. ReconGAN consists of a Deep Convolutional Generative Adversarial Network (DCGAN) and a finite element based shape optimization algorithm to virtually reconstruct the trabecular bone microstructure. The VP procedure involves injecting shear-thinning liquid bone cement through a needle in the trabecular region to reinforce a diseased or fractured vertebra. To simulate this treatment modality, computational fluid dynamics (CFD) is employed to predict the morphology of the injected cement within the bone microstructure. A power-law equation is utilized to characterize the non-Newtonian shear-thinning behavior of the polymethyl methacrylate (PMMA) bone cement during injection simulations. The CFD model is coupled with the level-set method to simulate the motion of the interface separating bone cement and bone marrow. After predicting the cement morphology, a data co-registration algorithm is employed to transform the CFD model to a high-fidelity continuum damage mechanics (CDM) finite element model of the augmented vertebra for predicting the fracture response. A feasibility study is presented to demonstrate the ability of this CFD-CDM framework to investigate the effect of VP on the mechanical integrity of the vertebral body in a cancer patient with a lytic metastatic tumor.

Synthesis of biaryls using palladium nanoparticles immobilized on metformine-functionalized polystyrene resin as a reusable and efficient nanocatalyst.

This study includes the procedure performed for the Pd@PS-Met preparation as a novel polymeric nanocatalyst, in which to entrapped palladium nanoparticles (Pd NPs) without agglomeration, metformine groups are applied (as linkers). The obtained catalyst could be evaluated using transmission electron microscopy (TEM), wavelength-dispersive X-ray spectroscopy (WDX), scanning electron microscopy (SEM), X-ray powder diffraction (XRD), inductively coupled plasma (ICP), fourier transform infrared (FTIR), and Energy-dispersive X-ray spectroscopy (EDS). For Suzuki cross-coupling reaction, the catalyst showed an excellent catalytic stability and activity in water under ambient condition. Additionally, this novel entrapped palladium catalyst was recovered by an easy filtration and reapplied several times without considerable activity loss.

Apoptotic and anti-apoptotic genes transcripts patterns of graphene in mice.

Recent studies showed that a large amount of graphene oxide accumulated in kidney and liver when it injected intravenously. Evaluation of lethal and apoptosis gene expression in these tissues, which are under stress is very important. In this paper the in vivo dose-dependent effects of graphene oxide and reduced graphene oxide nanoplatelets on kidney and liver of mice were studied. Balb/C mice were treated by 20mg/kg body weight of nanoplatelets. Molecular biology analysis showed that graphene nanoplatelets injected intravenously lead to overexpression of BAX gene in both kidney and liver tissues (P≥0.01). In addition these nanoparticles significantly increase BCL2 gene expression in both kidney and liver tissues (P≥0.05). Graphene significantly increase level of SGPT in groups 1 (220.64±13), 2 (164.44±9.3) in comparison to control group (P≤0.05). Also in comparison with control group (148.11±10.4), (P≤0.05), the level of SGOT in groups 1(182.01±12.6) and 2 (178.2±2.2) significantly increased.