Electromagnetic shielding properties of LPBF produced Fe2.9wt.%Si alloy

Ferromagnetic materials are used in various applications such as rotating electrical machines, wind turbines, electromagnetic shielding, transformers, and electromagnets. Compared to hard magnetic materials, their hysteresis cycles are featured by low values of coercive magnetic field and high permeability. The application of additive manufacturing to ferromagnetic materials is gaining more and more attraction. Indeed, thanks to a wider geometrical freedom, new topological optimized shapes for stator/rotor shapes can be addressed to enhance electric machines performances. However, the properties of the laser powder bed fusion (LPBF) processed alloy compared to conventionally produced counterpart must be still addressed. Accordingly, this paper presents for the first time the use of the LPBF for the manufacturing of Fe2.9wt.%Si electromagnetic shields. The process parameter selection material microstructure and the magnetic shielding factor are characterized

Characterization of LPBF Produced Fe2.9wt.%Si for Electromagnetic Actuator

This study aims to produce Fe2.9wt.%Si ferromagnetic material via laser powder bed fusion (L-PBF) for the realization of electromagnetic actuators (EMA). This study is necessary as there are no documents in scientific literature regarding the manufacturing of Iron-Silicon plungers using the L-PBF additive manufacturing (AM) technique. The microstructure, and magnetic properties were characterized using various techniques. The results indicate that the samples produced via L-PBF process exhibit good magnetic properties (μ = 748, H C= 87.7 [A/m] ) especially after annealing treatment at 1200° C for 1h (μ = 3224, H C= 69.1 [A/m]), making it a promising material for use in electromagnetic actuators

Two patients with history of STEC-HUS, posttransplant recurrence and complement gene mutations

Hemolytic uremic syndrome (HUS) is a disease of microangiopathic hemolytic anemia, thrombocytopenia and acute renal failure. About 90% of cases are secondary to infections by Escherichia coli strains producing Shiga-like toxins (STEC-HUS), while 10% are associated with mutations in genes encoding proteins of complement system (aHUS). We describe two patients with a clinical history of STEC-HUS, who developed end-stage renal disease (ESRD) soon after disease onset. They received a kidney transplant but lost the graft for HUS recurrence, a complication more commonly observed in aHUS. Before planning a second renal transplantation, the two patients underwent genetic screening for aHUS-associated mutations that revealed the presence of a heterozygous CFI mutation in patient #1 and a heterozygous MCP mutation in patient #2, and also in her mother who donated the kidney. This finding argues that the two cases originally diagnosed as STEC-HUS had indeed aHUS triggered by STEC infection on a genetic background of impaired complement regulation. Complement gene sequencing should be performed before kidney transplantation in patients who developed ESRD following STEC-HUS since they may be undiagnosed cases of aHUS, at risk of posttransplant recurrence. Furthermore, genetic analysis of donors is mandatory before living-related transplantation to exclude carriers of HUS-predisposing mutations. Two patients with a clinical history of D+ hemolytic uremic syndrome associated with Shiga-toxin-producing 0157:H7 E. coli and recurrence in the kidney graft carry heterozygous mutations in the genes encoding complement factor I (patient 1) and membrane cofactor protein (patient 2). © Copyright 2013 The American Society of Transplantation and the American Society of Transplant Surgeons

Association between sagittal alignment and loads at the adjacent segment in the fused spine: a combined clinical and musculoskeletal modeling study of 205 patients with adult spinal deformity

Fusion surgery; Sagittal alignment; Spine surgeryCirugía de fusión; Alineación sagital; Cirugía de columnaCirurgia de fusió; Alineació sagital; Cirurgia de columnaPurpose Sagittal malalignment is a risk factor for mechanical complications after surgery for adult spinal deformity (ASD). Spinal loads, modulated by sagittal alignment, may explain this relationship. The aims of this study were to investigate the relationships between: (1) postoperative changes in loads at the proximal segment and realignment, and (2) absolute postoperative loads and postoperative alignment measures. Methods A previously validated musculoskeletal model of the whole spine was applied to study a clinical sample of 205 patients with ASD. Based on clinical and radiographic data, pre-and postoperative patient-specific alignments were simulated to predict loads at the proximal segment adjacent to the spinal fusion. Results Weak-to-moderate associations were found between pre-to-postop changes in lumbar lordosis, LL (r =  − 0.23, r =  − 0.43; p < 0.001), global tilt, GT (r = 0.26, r = 0.38; p < 0.001) and the Global Alignment and Proportion score, GAP (r = 0.26, r = 0.37; p < 0.001), and changes in compressive and shear forces at the proximal segment. GAP score parameters, thoracic kyphosis measurements and the slope of upper instrumented vertebra were associated with changes in shear. In patients with T10-pelvis fusion, moderate-to-strong associations were found between postoperative sagittal alignment measures and compressive and shear loads, with GT showing the strongest correlations (r = 0.75, r = 0.73, p < 0.001). Conclusions Spinal loads were estimated for patient-specific full spinal alignment profiles in a large cohort of patients with ASD pre-and postoperatively. Loads on the proximal segments were greater in association with sagittal malalignment and malorientation of proximal vertebra. Future work should explore whether they provide a causative mechanism explaining the associated risk of proximal junction complications.Study funding was provided by Maxi Foundation. Open access funding was provided by Swiss Federal Institute of Technology Zurich

In Situ Hexavalent Chromium Reduction by Injection of Organic Substrates in the Aquifer

Among the innovative technologies for in situ remediation of hexavalent chromium in groundwater, bio-induced reduction is under investigation. In this process the reduction of Cr(VI) is stimulated by a strongly reducing environment, created by the injection of organic substrates that are rapidly degraded by autochthonous heterotrophic microorganisms. Tests were performed at the laboratory scale to investigate the behavior of two different organic substrates from food industry (permeate from cheese whey ultrafiltration and a waste from the brewing process), in terms of dissolved Cr(VI) abatement and kinetics, also as a function of the initial Cr(VI) concentration (5000 or 10000 μg/L). The tests showed that, under proper conditions, very low Cr(VI) concentrations (1.3 g/L) and removal efficiency up to about 100% can be obtained after 36 d incubation

Multiple sclerosis cortical and WM lesion segmentation at 3T MRI: a deep learning method based on FLAIR and MP2RAGE.

The presence of cortical lesions in multiple sclerosis patients has emerged as an important biomarker of the disease. They appear in the earliest stages of the illness and have been shown to correlate with the severity of clinical symptoms. However, cortical lesions are hardly visible in conventional magnetic resonance imaging (MRI) at 3T, and thus their automated detection has been so far little explored. In this study, we propose a fully-convolutional deep learning approach, based on the 3D U-Net, for the automated segmentation of cortical and white matter lesions at 3T. For this purpose, we consider a clinically plausible MRI setting consisting of two MRI contrasts only: one conventional T2-weighted sequence (FLAIR), and one specialized T1-weighted sequence (MP2RAGE). We include 90 patients from two different centers with a total of 728 and 3856 gray and white matter lesions, respectively. We show that two reference methods developed for white matter lesion segmentation are inadequate to detect small cortical lesions, whereas our proposed framework is able to achieve a detection rate of 76% for both cortical and white matter lesions with a false positive rate of 29% in comparison to manual segmentation. Further results suggest that our framework generalizes well for both types of lesion in subjects acquired in two hospitals with different scanners

Generative models : an upcoming innovation in musculoskeletal radiology? A preliminary test in spine imaging

Background Deep learning is a ground-breaking technology that is revolutionising many research and industrial fields. Generative models are recently gaining interest. Here, we investigate their potential, namely conditional generative adversarial networks, in the field of magnetic resonance imaging (MRI) of the spine, by performing clinically relevant benchmark cases. Methods First, the enhancement of the resolution of T2-weighted (T2W) images (super-resolution) was tested. Then, automated image-to-image translation was tested in the following tasks: (1) from T1-weighted to T2W images of the lumbar spine and (2) vice versa; (3) from T2W to short time inversion-recovery (STIR) images; (4) from T2W to turbo inversion recovery magnitude (TIRM) images; (5) from sagittal standing x-ray projections to T2W images. Clinical and quantitative assessments of the outputs by means of image quality metrics were performed. The training of the models was performed on MRI and x-ray images from 989 patients. Results The performance of the models was generally positive and promising, but with several limitations. The number of disc protrusions or herniations showed good concordance (\u3ba = 0.691) between native and super-resolution images. Moderate-to-excellent concordance was found when translating T2W to STIR and TIRM images (\u3ba 65\u20090.842 regarding disc degeneration), while the agreement was poor when translating x-ray to T2W images. Conclusions Conditional generative adversarial networks are able to generate perceptually convincing synthetic images of the spine in super-resolution and image-to-image translation tasks. Taking into account the limitations of the study, deep learning-based generative methods showed the potential to be an upcoming innovation in musculoskeletal radiology

Biomechanics of the lumbar spine after dynamic stabilization

Target of the study was to predict the biomechanics of the instrumented and adjacent levels due to the insertion of the DIAM spinal stabilization system (Medtronic Ltd). For this purpose, a 3-dimensional finite element model of the intact L3/ S1 segment was developed and subjected to different loading conditions (flexion, extension, lateral bending, axial rotation). The model was then instrumented at the L4/L5 level and the same loading conditions were reapplied. Within the assumptions of our model, the simulation results suggested that the implant caused a reduction in range of motion of the instrumented level by 17% in flexion and by 43% in extension, whereas at the adjacent levels, no significant changes were predicted. Numerical results in terms of intradiscal pressure, relative to the intact condition, predicted that the intervertebral disc at the instrumented level was unloaded by 27% in flexion, by 51% in extension, and by 6% in axial rotation, while no variations in pressure were caused by the device in lateral bending. At the adjacent levels, a change of relative intradiscal pressure was predicted in extension, both at the L3/L4 level, which resulted unloaded by 26% and at the L5/S1 level, unloaded by 8%. Furthermore, a reduction in terms of principal compressive stress in the annulus fibrosus of the L4/L5 instrumented level was predicted, as compared with the intact condition. These numerical predictions have to be regarded as a theoretical representation of the behavior of the spine, because any finite element model represents only a simplification of the real Structure

Automatic Calculation of Cervical Spine Parameters Using Deep Learning: Development and Validation on an External Dataset

STUDY DESIGN Retrospective data analysis. OBJECTIVES This study aims to develop a deep learning model for the automatic calculation of some important spine parameters from lateral cervical radiographs. METHODS We collected two datasets from two different institutions. The first dataset of 1498 images was used to train and optimize the model to find the best hyperparameters while the second dataset of 79 images was used as an external validation set to evaluate the robustness and generalizability of our model. The performance of the model was assessed by calculating the median absolute errors between the model prediction and the ground truth for the following parameters: T1 slope, C7 slope, C2-C7 angle, C2-C6 angle, Sagittal Vertical Axis (SVA), C0-C2, Redlund-Johnell distance (RJD), the cranial tilting (CT) and the craniocervical angle (CCA). RESULTS Regarding the angles, we found median errors of 1.66° (SD 2.46°), 1.56° (1.95°), 2.46° (SD 2.55), 1.85° (SD 3.93°), 1.25° (SD 1.83°), .29° (SD .31°) and .67° (SD .77°) for T1 slope, C7 slope, C2-C7, C2-C6, C0-C2, CT, and CCA respectively. As concerns the distances, we found median errors of .55 mm (SD .47 mm) and .47 mm (.62 mm) for SVA and RJD respectively. CONCLUSIONS In this work, we developed a model that was able to accurately predict cervical spine parameters from lateral cervical radiographs. In particular, the performances on the external validation set demonstrate the robustness and the high degree of generalizability of our model on images acquired in a different institution