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Placenta inflammation will be tightly related to gestational diabetes.
This work will prove useful to clinical blast injury researchers, blast protection engineers and clinical practitioners involved in the triage, diagnosis, and treatment of PBIs.Osteoporosis is characterized by reduced bone strength predisposing to an increased risk of fracture. Biomechanical computed tomography (BCT), predicting bone strength via CT-based finite element analysis (FEA), is now clinically available in the USA for diagnosing osteoporosis or assessing fracture risk. However, it has not been previously validated using a cohort of only Chinese subjects. Additionally, the effect of various modeling approaches on BCT outcomes remains elusive. To address these issues, we performed DXA and QCT scanning, compression testing, and BCT analyses on thirteen vertebrae derived from Chinese donors. Three BCT models were created (voxBCT and tetBCT voxel-based and tetrahedral element-based FE models generated by a commercial software; matBCT tetrahedral element-based FE model generated by a custom MATLAB program). BCT-computed outcomes were compared with experimental measures or between different BCT models. Results showed that, DXA-measured areal bone mineral density (aBMD) showed weak correlations with experimentally-measured vertebral stiffness (R2 = 0.28) and strength (R2 = 0.34). Compared to DXA-aBMD, BCT-computed stiffness provided improved correlations with experimentally-measured stiffness (voxBCT R2 = 0.82; tetBCT R2 = 0.77; matBCT R2 = 0.76) and strength (voxBCT R2 = 0.55; tetBCT R2 = 0.57; matBCT R2 = 0.53); BCT-computed mechanical parameters (stiffness, stress and strain) of the three different models were highly correlated with each other, with coefficient of determination (R2) values of 0.89-0.98. These results, based on a cohort of Chinese vertebral cadavers, suggest that BCT is superior over aBMD to consistently predict vertebral mechanical characteristics, regardless of the modeling approaches of choice.Ultimate force of the proximal human femur can be predicted using Finite Element Analysis (FEA), but the models rely on 3D computed tomography images. Landmark-based statistical appearance models (SAM) and B-Spline transformation-based statistical deformation models (SDM) have been used to estimate 3D images from 2D projections, which facilitates model generation and reduces the radiation dose. However, there is no literature on the accuracy of SDM-based FEA models of bones with respect to experimental results. In this study, a methodology for an enhanced SDM with textural information is presented. The statistical deformation and texture models (SDTMs) are based on a set of 37 quantitative CT (QCT) images. They were used to estimate 3D images from two or one projections of the set in a leave-one-out setup. These estimations where then used to create FEA models. The ultimate force predicted by FEA models estimated from two or one projection using the SDTMs were compared to the experimental ultimate force from a previous study on the same femora and to the results of standard QCT-based FEA models. High correlations between predictions and experimental measurements were found for FEA models reconstructed from 2D projections with R2=0.835 when based on two projections and R2=0.724 when using one projection. The correlations were comparable to those reached with standard QCT-based FE-models with the experimental results (R2=0.795). This study shows the high potential of SDTM-based 3D image reconstruction and FEA modelling from 2D projections to predict femoral ultimate force.The variability of human movement can be defined as normal variations occurring in motor activity and quantified using linear statistics or nonlinear methods. In the human movement field, linear and nonlinear measures of variability have been used to discriminate groups and conditions in different contexts. Indeed, some authors support the idea that these gait features provide complementary information about movement. However, it is unclear which type of gait variability measure best discriminates different groups or conditions, as a comparison of the discrimination capacity between linear and nonlinear gait variability features in different groups has not been assessed. read more Therefore, the main objective of this study was to test the discrimination capacity of linear and nonlinear gait features to determine which type of feature would be the most efficient for discriminating older and younger adults and between lower limb amputees and nonamputees using classification algorithms. Data from previously published studies were used. The classification task was performed using the k-nearest neighbors and random forest algorithms. Our results showed that using a combination of linear and nonlinear features resulted in the highest mean accuracy rates (>90%) in group classification, reinforcing the idea that these features are complementary and express different aspects of movement.Medical implants made of biodegradable materials are advantageous for short-term applications as fracture fixation and mechanical support during bone healing. After completing the healing process, the implant biodegrades without any long-term side effects nor any need for surgical removal. In particular, Magnesium (Mg) implants, while degrading, can cause physiological changes in the tissues surrounding the implant. The evaluation of structural remodeling is relevant, however, the functional assessment is crucial to provide information about physiological changes in tissues, which can be applied as an early marker during the healing process. Hence, non-invasive monitoring of structural and functional changes in the surrounding tissue during the healing process is essential, and the need for new assessing methods is emerging. This paper provides an assessment of Mg based implants, and an extensive review of the literature is presented with the focus on the imaging techniques for investigation of the Mg implants' biodegradation. The potential of a hybrid analysis, including Near-Infrared Spectroscopy (NIRS) and photoacoustic imaging (PAI) technology, is further discussed. A hybrid solution may play a significant role in monitoring implants and have several advantages for monitoring tissue oxygenation in addition to tissue's acidity, which is directly connected to the Mg implants degradation process. Such a hybrid assessment system can be a simple, ambulant, and less costly technology with the potential for clinically monitoring of Mg implants at site.
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