Adkinshobbs0037

Autoimmune encephalitis (AIE) encompasses a range of inflammatory disorders manifesting with some combination of encephalopathy, seizures, behavioral changes, movement disorders, dysautonomia or other neurologic symptoms. Anti-N-methyl-d-aspartate receptor encephalitis (NMDARE) is the most common AIE and is an autoantibody mediated disorder, often paraneoplastic. Untreated or undertreated AIE has a high degree of morbidity and mortality. Immunosuppressive treatment regimens including glucocorticoids, plasma exchange (PLEX), intravenous immunoglobulin (IVIG) and rituximab used alone or in combination for such patients. Patients' refractory to such treatments requires more aggressive and potentially toxic therapies. We report favorable outcomes in patients with refractory AIE who received intrathecal methotrexate (IT-MTX) as part of treatment.

Cases at our institution seen between 2010 and 2020 were reviewed. We identified 5 patients in our clinical practice whose clinical presentation was compatible with Nhis retrospective review demonstrates the efficacy of intrathecal methotrexate in the treatment of severe AIE who had failed other immunosuppressive regimens.We aimed to explore the potential biomarkers and susceptible population for early diagnosis and treatment of tuberculosis (TB). Ten hub differentially expressed TB-related genes (DETRGs) from GSE83456 dataset were screened with the "limma" package and the GeneCards database. Unsupervised clustering was utilized to identify susceptible population among TB patients based on 10 hub DETRGs. TRANSFAC, MirTarbase, miRanda and TargetScan was used to predict microRNAs and transcription factors (TFs) and construct TF-miRNA-mRNA regulatory network. The results showed that a total of 266 DEGs were identified. Functional analysis mainly enriched in interferon pathway, cytokine and receptor interaction and host defense response to virus, while the four-module genes screened were closely related to interferon-γ signal transduction pathway as well. Based on 10 DETRGs, TB patients were divided into two clusters with significant differences in neutrophil function and 16 hub miRNAs and 10 hub TFs were predicted. Finally, NFATc1- (miR145) - STAT1 regulatory pathway was identified as the critical regulatory pathway, which mediates cytokine receptor binding, interleukin-1 receptor binding and TNF signaling pathway. Hence, we concluded that immunoheterogeneity exists among TB patients and NFATC1-(miR145)-STAT1 regulatory pathway might be associated with tuberculosis infection, which may be valuable targets for prevention and treatment of tuberculosis.Additive manufactured porous biomaterials based on triply periodic minimal surfaces (TPMS) are a highly discussed topic in the literature. With their unique properties in terms of open porosity, large surface area and surface curvature, they are considered to have bone mimicking properties and remarkable osteogenic potential. In this study, scaffolds of gyroid unit cells of different sizes consisting of a Ti6Al4V alloy were manufactured additively by electron beam melting (EBM). The scaffolds were analysed by micro-computed tomography (micro-CT) to determine their morphological characteristics and, subsequently, subjected to mechanical tests to investigate their quasi-static compressive properties and fatigue resistance. All scaffolds showed an average open porosity of 71-81%, with an average pore size of 0.64-1.41 mm, depending on the investigated design. Y-27632 The design with the smallest unit cell shows the highest quasi-elastic gradient (QEG) as well as the highest compressive offset stress and compression strength. Furthermore, the fatigue resistance of all unit cell size (UCS) variations showed promising results. In detail, the smallest unit cells achieved fatigue strength at 106 cycles at 45% of their compressive offset stress, which is comparatively good for additively manufactured porous biomaterials. In summary, it is demonstrated that the mechanical properties can be significantly modified by varying the unit cell size, thus enabling the scaffolds to be specifically tailored to avoid stress shielding and ensure implant safety. Together with the morphological properties of the gyroid unit cells, the fabricated scaffolds represent a promising approach for use as a bone substitute material.This paper presents a convenient and efficient method to predict the mechanical solutions of a laminated Liquid Crystal Elastomers (LCEs) system subjected to combined thermo-mechanical load, based on a back propagation (BP) neural network which is trained by machine learning from a database established by analytical solutions. Firstly, the general solutions of temperature, displacement, and stress of any single layer in the LCEs system are obtained by solving the two-dimensional (2D) governing equations of both heat conduction and thermoelasticity. Then, the unknown coefficients in above general solutions are determined by a transfer-matrix method based on the continuity condition at the interface of adjacent layers and the combined thermo-mechanical loads condition at the surface of the LCEs system. The formula derivation and calculator program are verified through convergence studies and comparisons with FEM results. Finally, a database with displacements of LCEs system in a temperature field subjected to 561 sets of mechanical loads is established based on the presented analytical model. The BP neural network based on above database is further applied to establish the relationship between deformation and mechanical load to predict the elastic deformation of the LCEs system in a temperature field subjected to a mechanical load. Moreover, the BP network can also inverse the coefficients of mechanical load which induces the specific deformation in a temperature field. The numerical examples show that (1) The deformation of a laminated LCEs system due to thermal load is limited within the range of human temperature changes from 36 °C to 40 °C. (2) The thickness of the LCE is a sensitive parameter on the deformation at the bottom surface of the system. (3) The accuracy of predicted displacements induced by the thermo-mechanical load and the inversed mechanical load based on deformation of the LCEs system in a temperature field using BP neural network reaches 99.6% and 98.5% respectively.Currently, preclinical mechanical wear testing of total knee replacements (TKRs) is done using ideally aligned components using standardized TKR level walking under either force or displacement-control regimes. To understand the influence of implant alignment and testing control regime, we studied the effect of nine component alignment parameters on TKR volumetric wear in silico. We used a computational framework combining Latin Hypercube sampling design of experiments, finite element analysis, and a numerical model of polyethylene wear, to create a predictive model of how component alignment affects wear rate for each control regime. Nine component alignment parameters were investigated, five femoral variables and four tibial variables. To investigate perturbations of the nine implant alignment variables, two separate 300-point designs were executed, one for each control regime. The results were then used to generate surrogate statistical models using stepwise multiple linear regression. Wear at the neutral position was 4.5mm3/million cycle and 8.6mm3/million cycle for displacement and force-control, respectively. Stepwise multiple linear regression surrogate models were highly significant for each control regime, but force-control generated a stronger predictive model, with a higher R2, more included terms, and a lower RMSE. Both models predicted transverse plane rotational mismatch can lead to large changes in predicted wear; a transverse plane alignment mismatch of 15° can elicit a change in wear of up to 5mm3/million cycle, almost double that of neutral alignment. Therefore, transverse plane alignment is particularly important when considering failure of the implant due to wear.Most of the mechnoregulatory computational models appearing so far in tissue engineering for bone healing predictions, utilize as regulators for cell differentiation mainly the octahedral volume strains and the interstitial fluid velocity calculated at any point of the fractured bone area and controlled by empirical constants concerning these two parameters. Other stimuli like the electrical and chemical signaling of bone constituents are covered by those two regulatory fields. It is apparent that the application of the same mechnoregulatory computational models for bone healing predictions in scaffold-aided regeneration is questionable since the material of a scaffold disturbs the signaling pathways developed in the environment of bone fracture. Thus, the goal of the present work is to evaluate numerically two fields developed in the body of two different compressed scaffolds, which seem to be proper for facilitating cell sensing and improving cell viability and cell seeding efficiency. These two fields concern the surface octahedral strains that the cells attached to the scaffold can experience and the internal strain gradients that create electrical pathways due to flexoelectric phenomenon. Both fields are evaluated with the aid of the Boundary Element Method (BEM), which is ideal for evaluating with high accuracy surface strains and stresses as well as strain gradients appearing throughout the analyzed elastic domain.Sintering is a comprehensive process that involves the complex evolution of material microstructures and properties, being recognized as a critical factor to improve the machinability of ceramics. The present work aims to address the evolution of the material removal mechanisms of the 3 mol% yttria-stabilized tetragonal zirconia polycrystal (3Y-TZP) during the sintering process based on the micro scratching tests. The impacts of sintering temperatures on the material removal behaviors, including scratching forces, scratch morphologies, specific scratching energies, and critical transition depths, were rigorously studied. The acquired results indicate that the intergranular bonding strength is a critical factor that determinines the material removal mechanisms of 3Y-TZP, and 1100 °C signifies the transition threshold for the material removal mode. After 1100 °C, the material removal mechanism has gradually converted into the typical ductile-brittle removal regime. Moreover, the critical depth in ductile regime at 1200 °C is about 1.89 times that at 1500 °C, and the critical depth of ductile-brittle transition at 1200 °C is approximately 2.08 times that at 1500 °C.Capitalizing on features including high resolution, smooth surface finish, large build volume, and simultaneous multi-color/multi-material printing, material jetting additive manufacturing enables the fabrication of full-scale anatomic models. The ability to print materials that resemble relevant, compliant tissues has especially motivated implementation of material jetting for patient-specific surgical planning or training models. In an effort to broaden the material selection for the material jetting process, and to provide materials that more closely mimic the functional needs for a wider variety of tissues, a composite material system is explored that uses non-curing fluid dispersed into a photo-curable medium. The material properties of the composites are examined through both thermal and mechanical analysis (dynamic mechanical analysis, Shore hardness testing, puncture testing, and tensile testing). Higher contributions of non-curing fluid generally reduce part strength and stiffness, and exponential and second-order polynomial expressions are appropriate fits for many of the mechanical properties as functions of non-curing fluid concentration.