An anatomical subject-specific FE-model for hip fracture load prediction

In order to reduce the socio-economic burden induced by osteoporotic hip fractures, finite element models have been evaluated as an additional diagnostic tool for fracture prediction. For a future clinical application, the challenge is to reach the best compromise between model relevance and computing time. Based on this consideration, the current study focused on the development and validation of a subject-specific FE-model using an original parameterised generic model and a specific personalization method. A total of 39 human femurs were tested to failure under a quasi-static compression in stance configuration. The corresponding FE-models were generated and for each specimen the numerical fracture load (F _FEM) was compared with the experimental value (F _EXP), resulting in a significant correlation (F _EXP = 1.006 F _FEM with r ² = 0.87 and SEE = 1220 N, p < 0.05) obtained with a reasonable computing time (30 mn). Further in vivo study should confirm the ability of this FE-model to improve the fracture risk prediction.     

Numerical investigations of rib fracture failure models in different dynamic loading conditions

Rib fracture is one of the most common thoracic injuries in vehicle traffic accidents that can result in fatalities associated with seriously injured internal organs. A failure model is critical when modelling rib fracture to predict such injuries. Different rib failure models have been proposed in prediction of thorax injuries. However, the biofidelity of the fracture failure models when varying the loading conditions and the effects of a rib fracture failure model on prediction of thoracic injuries have been studied only to a limited extent. Therefore, this study aimed to investigate the effects of three rib failure models on prediction of thoracic injuries using a previously validated finite element model of the human thorax. The performance and biofidelity of each rib failure model were first evaluated by modelling rib responses to different loading conditions in two experimental configurations: (1) the three-point bending on the specimen taken from rib and (2) the anterior–posterior dynamic loading to an entire bony part of the rib. Furthermore, the simulation of the rib failure behaviour in the frontal impact to an entire thorax was conducted at varying velocities and the effects of the failure models were analysed with respect to the severity of rib cage damages. Simulation results demonstrated that the responses of the thorax model are similar to the general trends of the rib fracture responses reported in the experimental literature. However, they also indicated that the accuracy of the rib fracture prediction using a given failure model varies for different loading conditions.     

Right-hand-side updating for fast computing of genomic breeding values

Background

Since both the number of SNPs (single nucleotide polymorphisms) used in genomic prediction and the number of individuals used in training datasets are rapidly increasing, there is an increasing need to improve the efficiency of genomic prediction models in terms of computing time and memory (RAM) required.

Methods

In this paper, two alternative algorithms for genomic prediction are presented that replace the originally suggested residual updating algorithm, without affecting the estimates. The first alternative algorithm continues to use residual updating, but takes advantage of the characteristic that the predictor variables in the model (i.e. the SNP genotypes) take only three different values, and is therefore termed “improved residual updating”. The second alternative algorithm, here termed “right-hand-side updating” (RHS-updating), extends the idea of improved residual updating across multiple SNPs. The alternative algorithms can be implemented for a range of different genomic predictions models, including random regression BLUP (best linear unbiased prediction) and most Bayesian genomic prediction models. To test the required computing time and RAM, both alternative algorithms were implemented in a Bayesian stochastic search variable selection model.

Results

Compared to the original algorithm, the improved residual updating algorithm reduced CPU time by 35.3 to 43.3%, without changing memory requirements. The RHS-updating algorithm reduced CPU time by 74.5 to 93.0% and memory requirements by 13.1 to 66.4% compared to the original algorithm.

Conclusions

The presented RHS-updating algorithm provides an interesting alternative to reduce both computing time and memory requirements for a range of genomic prediction models.     

Load transfer analysis of the distal radius from in-vivo high-resolution CT-imaging.

Prevention of osteoporotic bone fractures requires accurate diagnostic methods to detect the increase in bone fragility at an early stage of osteoporosis. However, today's bone fracture risk prediction, primarily based on bone density measurement, is not sufficiently precise. There is increasing evidence that, in addition to bone density, also the bone microarchitecture and its mechanical loading conditions are important factors determining the fracture risk. Recently, it has been shown that new high-resolution imaging techniques in combination with new computer modeling techniques based on the finite-element (FE) method can account for these additional factors. These techniques might provide information that is more relevant for the prediction of bone fracture risk. So far, however, these new imaged-based FE techniques have not been feasible in-vivo. The objectives of this study were to quantify the load transfer through the trabecular network in a distal radius using a computer model based on in-vivo high-resolution images and to determine if common regions of fractures can be explained as a result of high tissue loading in these regions. The left distal radius and the two adjacent carpal bones of a healthy volunteer were imaged using a high-resolution three-dimensional CT system providing an isotropic resolution of 165 microm. The bone representation was converted into a FE-model that was used to calculate stresses and strains in the trabecular network. The two carpal bones were loaded using different load ratios (for each load case 1000 N in total) representing impact forces on the hand either in near-neutral position or ulnar/radial deviation. The load transfer through the trabecular network of the radius was characterized by the tissue strain energy density (SED) distribution for all load cases. It was found that the distribution of the tissue loading depends on the ratio of the forces acting on the carpal bones. For all load cases the higher SED values (on average: 0.02 +/- 0.08 (S.D.) N mm(-2)) are found in a 10 mm region adjacent to the articular surface which corresponds well with the region where Colles- or Chauffeur-fractures occur. We expect that, eventually, this new approach can lead to a better prediction of the fracture risk than methods based on bone density alone since it accounts for the bone microstructure as well as its loading conditions.     

Caprini血栓风险评分及血清D-D、VEGF、PDGF-B对创伤性骨折患者术后深静脉血栓形成的预测价值及模型构建

摘要 目的：探讨Caprini血栓风险评分及血清D-二聚体（D-D）、血管内皮生长因子（VEGF）、血小板衍生生长因子B（PDGF-B）对创伤性骨折患者术后深静脉血栓形成（DVT）的预测价值,并构建风险预测模型。方法：选取2019年6月至2022年5月徐州医科大学附属医院收治的216例创伤性骨折住院患者为研究对象,依据术后DVT发生情况分为DVT组（62例）和非DVT组（154例）,对比两组Caprini血栓风险评分、血清D-D、VEGF、PDGF-B及临床资料的差异,多因素Logistic回归分析明确DVT的危险因素,建立预测模型,并进行模型的验证,绘制受试者工作特征曲线（ROC）进行效能评估。结果：创伤性骨折患者术后DVT发生率为28.70%。DVT组骨折至入院时间＞24 h患者占比多于非DVT组,凝血酶原时间（PT）、活化部分凝血活酶时间（APTT）、D-D、VEGF、PDGF-B、Caprini血栓风险评分均高于非DVT组（P＜0.05）。多因素Logistic回归分析显示,骨折至入院时间>24 h、血清D-D、VEGF、PDGF-B高水平及Caprini血栓风险评分升高是创伤性骨折患者术后发生DVT的独立危险因素（P＜0.05）。以Logistic回归分析筛选的创伤性骨折患者术后发生DVT的5个独立危险因素,建立预测模型方程：Logit（P）=-0.171 +1.170×骨折至入院时间 +1.041×血清D-D +0.046×血清VEGF +0.100×血清PDGF-B +0.080×Caprini血栓风险评分,内部验证结果显示C-index指数为0.825（95%CI：0.740~0.892）,Calibration曲线显示校正曲线与理想曲线趋近重合（P＞0.05）。使用本研究样本进行外部验证：曲线下面积AUC为0.901、灵敏度为0.887、特异度为0.870,显示该模型具有很高的预测效能,明显高于D-D、VEGF、PDGF-B、Caprini血栓风险评分单独应用的预测效能。结论：骨折至入院时间、血清D-D、VEGF、PDG及Caprini血栓风险评分均是创伤性骨折患者术后发生DVT的影响因素,构建的预测模型对DVT的发生具有较好的预测效能。     

Study and formation of vesicle systems with low polydispersity index by ultrasound method

The formation of liposomes with low polydispersity index by application of ultrasounds was investigated considering methodology specifications such as sonication time and sonication power. Phosphatidylcholine (PC) liposomes were formed by the evaporation–hydration method. The vesicles were sonicated using several sonication conditions. The liposomes were then characterized by dynamic light scattering (DLS) and freeze-fracture electron microscopy (FFEM). Correlation functions from DLS were treated by cumulants method and GENDIST to obtain the mean radius and polydispersity index. These calculations allowed to fix an optimal sonication time (3000 s) and a useful interval of ultrasound power between 39 and 91 W. DLS and FFEM results confirmed that vesicle size, lamellarity and the polydispersity index decreased with the increase of sonication power. Thus, we propose a systematic method to form liposomes in which the physical characteristics of the vesicles may be controlled as a function of sonication time and power.     

SPSRG: a prediction approach for correlated failures in distributed computing systems

Failure instances in distributed computing systems (DCSs) have exhibited temporal and spatial correlations, where a single failure instance can trigger a set of failure instances simultaneously or successively within a short time interval. In this work, we propose a correlated failure prediction approach (CFPA) to predict correlated failures of computing elements in DCSs. The approach models correlated-failure patterns using the concept of probabilistic shared risk groups and makes a prediction for correlated failures by exploiting an association rule mining approach in a parallel way. We conduct extensive experiments to evaluate the feasibility and effectiveness of CFPA using both failure traces from Los Alamos National Lab and simulated datasets. The experimental results show that the proposed approach outperforms other approaches in both the failure prediction performance and the execution time, and can potentially provide better prediction performance in a larger system.     

Current Developments in Dementia Risk Prediction Modelling: An Updated Systematic Review

Background

Accurate identification of individuals at high risk of dementia influences clinical care, inclusion criteria for clinical trials and development of preventative strategies. Numerous models have been developed for predicting dementia. To evaluate these models we undertook a systematic review in 2010 and updated this in 2014 due to the increase in research published in this area. Here we include a critique of the variables selected for inclusion and an assessment of model prognostic performance.

Methods

Our previous systematic review was updated with a search from January 2009 to March 2014 in electronic databases (MEDLINE, Embase, Scopus, Web of Science). Articles examining risk of dementia in non-demented individuals and including measures of sensitivity, specificity or the area under the curve (AUC) or c-statistic were included.

Findings

In total, 1,234 articles were identified from the search; 21 articles met inclusion criteria. New developments in dementia risk prediction include the testing of non-APOE genes, use of non-traditional dementia risk factors, incorporation of diet, physical function and ethnicity, and model development in specific subgroups of the population including individuals with diabetes and those with different educational levels. Four models have been externally validated. Three studies considered time or cost implications of computing the model.

Interpretation

There is no one model that is recommended for dementia risk prediction in population-based settings. Further, it is unlikely that one model will fit all. Consideration of the optimal features of new models should focus on methodology (setting/sample, model development and testing in a replication cohort) and the acceptability and cost of attaining the risk variables included in the prediction score. Further work is required to validate existing models or develop new ones in different populations as well as determine the ethical implications of dementia risk prediction, before applying the particular models in population or clinical settings.     

An improved murine femur fracture device for bone healing studies

Murine models are commonly used to investigate bone healing and test new treatments before human trials. Our objective was to design an improved murine femur fracture device and determine optimal mass and velocity settings for maximal likelihood of transverse fracture. Fracture reproducibility was maximized using an adjustable kinetic energy level, a novel mouse positioning system and an electromagnet striker release assembly. Sixty wild-type mice of 8-12-week-old male and female with a weight of 26.4+/-6.1g were subjected to an experimental postmortem fracture in the left and right femur (n=120) using variable kinetic energy inputs. A best-fit prediction equation for transverse fracture was developed using multivariate linear regression. Transverse fracture was shown to correlate most highly with kinetic energy with a maximum likelihood at mv2=292 where m is mass (g) and v is velocity (m/s). Model validation with a group of 134 anesthetized C57BL/6 mice resulted in a favorable transverse fracture rate of 85.8%. Simple modifications to existing fracture devices can improve accuracy and reproducibility. The results may assist researchers studying the effects of genetic modifications and novel treatments on boney healing in murine femur fracture models. Maintaining kinetic energy parameters within suggested ranges may also aid in ensuring accuracy and reproducibility.     

Prediction of femoral strength using 3D finite element models reconstructed from DXA images: validation against experiments

Computed tomography (CT)-based finite element (FE) models may improve the current osteoporosis diagnostics and prediction of fracture risk by providing an estimate for femoral strength. However, the need for a CT scan, as opposed to the conventional use of dual-energy X-ray absorptiometry (DXA) for osteoporosis diagnostics, is considered a major obstacle. The 3D shape and bone mineral density (BMD) distribution of a femur can be reconstructed using a statistical shape and appearance model (SSAM) and the DXA image of the femur. Then, the reconstructed shape and BMD could be used to build FE models to predict bone strength. Since high accuracy is needed in all steps of the analysis, this study aimed at evaluating the ability of a 3D FE model built from one 2D DXA image to predict the strains and fracture load of human femora. Three cadaver femora were retrieved, for which experimental measurements from ex vivo mechanical tests were available. FE models were built using the SSAM-based reconstructions: using only the SSAM-reconstructed shape, only the SSAM-reconstructed BMD distribution, and the full SSAM-based reconstruction (including both shape and BMD distribution). When compared with experimental data, the SSAM-based models predicted accurately principal strains (coefficient of determination >0.83, normalized root-mean-square error <16%) and femoral strength (standard error of the estimate 1215 N). These results were only slightly inferior to those obtained with CT-based FE models, but with the considerable advantage of the models being built from DXA images. In summary, the results support the feasibility of SSAM-based models as a practical tool to introduce FE-based bone strength estimation in the current fracture risk diagnostics.     

MUFOLD: A new solution for protein 3D structure prediction

There have been steady improvements in protein structure prediction during the past 2 decades. However, current methods are still far from consistently predicting structural models accurately with computing power accessible to common users. Toward achieving more accurate and efficient structure prediction, we developed a number of novel methods and integrated them into a software package, MUFOLD. First, a systematic protocol was developed to identify useful templates and fragments from Protein Data Bank for a given target protein. Then, an efficient process was applied for iterative coarse‐grain model generation and evaluation at the Cα or backbone level. In this process, we construct models using interresidue spatial restraints derived from alignments by multidimensional scaling, evaluate and select models through clustering and static scoring functions, and iteratively improve the selected models by integrating spatial restraints and previous models. Finally, the full‐atom models were evaluated using molecular dynamics simulations based on structural changes under simulated heating. We have continuously improved the performance of MUFOLD by using a benchmark of 200 proteins from the Astral database, where no template with >25% sequence identity to any target protein is included. The average root‐mean‐square deviation of the best models from the native structures is 4.28 Å, which shows significant and systematic improvement over our previous methods. The computing time of MUFOLD is much shorter than many other tools, such as Rosetta. MUFOLD demonstrated some success in the 2008 community‐wide experiment for protein structure prediction CASP8. Proteins 2010. © 2009 Wiley‐Liss, Inc.     

Quantitative computed tomography-based finite element models of the human lumbar vertebral body: effect of element size on stiffness,damage, and fracture strength predictions

This study investigated the numerical convergence characteristics of specimen-specific "voxel-based" finite element models of 14 excised human cadaveric lumbar vertebral bodies (age: 37-87; M = 6, F = 8) that were generated automatically from clinical-type CT scans. With eventual clinical applications in mind, the ability of the model stiffness to predict the experimentally measured compressive fracture strength of the vertebral bodies was also assessed. The stiffness of "low"-resolution models (3 x 3 x 3 mm element size) was on average only 4% greater (p = 0.03) than for "high"-resolution models (1 x 1 x 1.5 mm) despite interspecimen variations that varied over four-fold. Damage predictions using low- vs high-resolution models were significantly different (p = 0.01) at loads corresponding to an overall strain of 0.5%. Both the high (r2 = 0.94) and low (r2 = 0.92) resolution model stiffness values were highly correlated with the experimentally measured ultimate strength values. Because vertebral stiffness variations in the population are much greater than those that arise from differences in voxel size, these results indicate that imaging resolution is not critical in cross-sectional studies of this parameter. However, longitudinal studies that seek to track more subtle changes in stiffness over time should account for the small but highly significant effects of voxel size. These results also demonstrate that an automated voxel-based finite element modeling technique may provide an excellent noninvasive assessment of vertebral strength.     

Prediction of the structural response of the femoral shaft under dynamic loading using subject-specific finite element models

The goal of this study was to predict the structural response of the femoral shaft under dynamic loading conditions using subject-specific finite element (SS-FE) models and to evaluate the prediction accuracy of the models in relation to the model complexity. In total, SS-FE models of 31 femur specimens were developed. Using those models, dynamic three-point bending and combined loading tests (bending with four different levels of axial compression) of bare femurs were simulated, and the prediction capabilities of five different levels of model complexity were evaluated based on the impact force time histories: baseline, mass-based scaled, structure-based scaled, geometric SS-FE, and heterogenized SS-FE models. Among the five levels of model complexity, the geometric SS-FE and the heterogenized SS-FE models showed statistically significant improvement on response prediction capability compared to the other model formulations whereas the difference between two SS-FE models was negligible. This result indicated the geometric SS-FE models, containing detailed geometric information from CT images with homogeneous linear isotropic elastic material properties, would be an optimal model complexity for prediction of structural response of the femoral shafts under the dynamic loading conditions. The average and the standard deviation of the RMS errors of the geometric SS-FE models for all the 31 cases was 0.46 kN and 0.66 kN, respectively. This study highlights the contribution of geometric variability on the structural response variation of the femoral shafts subjected to dynamic loading condition and the potential of geometric SS-FE models to capture the structural response variation of the femoral shafts.     

Bone Mineral Density of the 1/3 Radius Refines Osteoporosis Diagnosis,Correlates With Prevalent Fractures,and Enhances Fracture Risk Estimates

ObjectiveTo investigate the added value of 1/3 radius (1/3R) for the diagnosis of osteoporosis by spine and hip sites and its correlation with prevalent fractures and predicted fracture risk.MethodsFracture Risk Assessment Tool (FRAX) scores for hip and major osteoporotic fractures (MOF) with/without trabecular bone score were considered proxy for fracture risk. The contribution of 1/3R to risk prediction was depicted via linear regression models with FRAX score as the dependent variable—first only with central and then with radius T-score as an additional covariate. Significance of change in the explained variance was compared by F-test.ResultsThe study included 1453 patients, 86% women, aged 66 ± 10 years. A total of 32% (n = 471) were osteoporotic by spine/hip and 8% (n = 115) by radius only, constituting a 24.4% increase in the number of subjects defined as osteoporotic (n = 586, 40%). Prior fracture prevalence was similar among patients with osteoporosis by spine/hip (17.4%) and radius only (19.1%) (P = .77).FRAX prediction by a regression model using spine/hip T-score yielded explained variance of 51.8% and 49.9% for MOF and 39.8% and 36.4% for hip (with/without trabecular bone score adjustment, respectively). The contribution of 1/3R was statistically significant (P < .001) and slightly increased the explained variance to 52.3% and 50.4% for MOF and 40.9% and 37.4% for hip, respectively.ConclusionReclassification of BMD results according to radius measurements results in higher diagnostic output. Prior fractures were equally prevalent among patients with radius-only and classic-site osteoporosis. FRAX tool performance slightly improved by incorporating radius BMD. Whether this approach may lead to a better fracture prediction warrants further prospective evaluation.     

Toxic gas dispersion prediction for point source emission using deep learning method

Abstract

Accurate and rapid toxic gas concentration prediction model plays an important role in emergency aid of sudden gas leak. However, it is difficult for existing dispersion model to achieve accuracy and efficiency requirements at the same time. Although some researchers have considered developing new forecasting models with traditional machine learning, such as back propagation (BP) neural network, support vector machine (SVM), the prediction results obtained from such models need to be improved still in terms of accuracy. Then new prediction models based on deep learning are proposed in this paper. Deep learning has obvious advantages over traditional machine learning in prediction and classification. Deep belief networks (DBNs) as well as convolution neural networks (CNNs) are used to build new dispersion models here. Both models are compared with Gaussian plume model, computation fluid dynamics (CFD) model and models based on traditional machine learning in terms of accuracy, prediction time, and computation time. The experimental results turn out that CNNs model performs better considering all evaluation indexes.     

Estimation of the dominance merit in noninbred populations without recourse to its inverted relationship matrix

This study presents a new approach to obtain dominance estimates without using the full Henderson's mixed model equations (MMEs) related to an additive plus dominance animal model. This reduction could decrease substantially the computing time and hence its cost. In contrast to a procedure that we proposed before, the method developed in this paper does not require D(-1) and provides best linear unbiased prediction (BLUP) of genetic values that is close to that given by processing the full MMEs. In the previous study, we also elaborated an algorithm (denoted xi-REML) in order to approximate restricted maximum likelihood estimation of variance components via the expectation maximization (EM) algorithm. The xi-REML algorithm has been modified to be adapted to our new resolution approach. Through a numerical example, we show that there is a good agreement between REML-(EM), xi-REML and modified xi-REML estimates and that the latter algorithm is more efficient than our first proposition in terms of computing time and memory conservation.     

Effect of boundary conditions,impact loading and hydraulic stiffening on femoral fracture strength

Patient specific quantitative CT (QCT) imaging data together with the finite element (FE) method may provide an accurate prediction of a patient's femoral strength and fracture risk. Although numerous FE models investigating femoral fracture strength have been published, there is little consent on the effect of boundary conditions, dynamic loading and hydraulic strengthening due to intra-medullary pressure on the predicted fracture strength. We developed a QCT-derived FE model of a proximal femur that included node-specific modulus assigned based on the local bone density. The effect of three commonly used boundary conditions published in literature were investigated by comparing the resulting strain field due to an applied fracture load. The models were also augmented with viscoelastic material properties and subject to a realistic impact load profile to determine the effect of dynamic loads on the strain field. Finally, the effect of hydraulic strengthening was investigated by including node specific permeability and performing a coupled pore diffusion and stress analysis of the FE model. Results showed that all boundary conditions yield the same strain field patterns, but peak strains were 22% lower and fracture load was 18% higher when loaded at the greater trochanter than when loaded at the femoral head. Comparison of the dynamic models showed that material viscoelasticity was important, but inertial effects (vibration and shock) were not. Finally, pore pressure changes did not cause significant hydraulic strengthening of bone under fall impact loading.     

Prediction of femoral fracture load using finite element models: an examination of stress- and strain-based failure theories

Finite element (FE) models are often used to model bone failure. However, no failure theory for bone has been validated at this time. In this study, we examined the performance of nine stress- and strain-based failure theories, six of which could account for differences in tensile and compressive material strengths. The distortion energy, Hoffman and a strain-based Hoffman analog, maximum normal stress, maximum normal strain, maximum shear strain, maximum shear stress (tau(max)), Coulomb-Mohr, and modified Mohr failure theories were evaluated using automatically generated, computed tomographic scan-based FE models of the femur. Eighteen matched pairs of proximal femora were examined in two load configurations, one approximating joint loading during single-limb stance and one simulating impact from a fall. Mechanical testing was performed to assess model and failure theory performance in the context of predicting femoral fracture load. Measured and FE-computed fracture load were significantly correlated for both loading conditions and all failure criteria (p < or = 0.001). The distortion energy and tau(max) failure theories were the most robust of those examined, providing the most consistently strong FE model performance for two very different loading conditions. The more complex failure theories and the strain-based theories examined did not improve performance over the simpler distortion energy and tau(max) theories, and often degraded performance, even when differences between tensile and compressive failure properties were represented. The relatively strong performance of the distortion energy and tau(max) theories supports the hypothesis that shear/distortion is an important failure mode during femoral fracture.     

Uric Acid and the Prediction Models of Tumor Lysis Syndrome in AML

We investigated the ability of serum uric acid (SUA) to predict laboratory tumor lysis syndrome (LTLS) and compared it to common laboratory variables, cytogenetic profiles, tumor markers and prediction models in acute myeloid leukemia patients. In this retrospective study patients were risk-stratified for LTLS based on SUA cut-off values and the discrimination ability was compared to current prediction models. The incidences of LTLS were 17.8%, 21% and 62.5% in the low, intermediate and high-risk groups, respectively. SUA was an independent predictor of LTLS (adjusted OR 1.12, CI95% 1.0–1.3, p = 0.048). The discriminatory ability of SUA, per ROC curves, to predict LTLS was superior to LDH, cytogenetic profile, tumor markers and the combined model but not to WBC (AUCWBC 0.679). However, in comparisons between high-risk SUA and high-risk WBC, SUA had superior discriminatory capability than WBC (AUCSUA 0.664 vs. AUCWBC 0.520; p <0.001). SUA also demonstrated better performance than the prediction models (high-risk SUAAUC 0.695, p<0.001). In direct comparison of high-risk groups, SUA again demonstrated superior performance than the prediction models (high-risk SUAAUC 0.668, p = 0.001) in predicting LTLS, approaching that of the combined model (AUC 0.685, p<0.001). In conclusion, SUA alone is comparable and highly predictive for LTLS than other prediction models.