Investigating the mechanical response of paediatric bone under bending and torsion using finite element analysis

Fractures of bone account 25% of all paediatric injuries (Cooper et al. in J Bone Miner Res 19:1976-1981, 2004. https://doi.org/10.1359/JBMR.040902 ). These can be broadly categorised into accidental or inflicted injuries. The current clinical approach to distinguish between these two is based on the clinician's judgment, which can be subjective. Furthermore, there is a lack of studies on paediatric bone to provide evidence-based information on bone strength, mainly due to the difficulties of obtaining paediatric bone samples. There is a need to investigate the behaviour of children's bones under external loading. Such data will critically enhance our understanding of injury tolerance of paediatric bones under various loading conditions, related to injuries, such as bending and torsional loads. The aim of this study is therefore to investigate the response of paediatric femora under two types of loading conditions, bending and torsion, using a CT-based finite element approach, and to determine a relationship between bone strength and age/body mass of the child. Thirty post-mortem CT scans of children aged between 0 and 3 years old were used in this study. Two different boundary conditions were defined to represent four-point bending and pure torsional loads. The principal strain criterion was used to estimate the failure moment for both loading conditions. The results showed that failure moment of the bone increases with the age and mass of the child. The predicted failure moment for bending, external and internal torsions were 0.8-27.9, 1.0-31.4 and 1.0-30.7 Nm, respectively. To the authors' knowledge, this is the first report on infant bone strength in relation to age/mass using models developed from modern medical images. This technology may in future help advance the design of child, car restrain system, and more accurate computer models of children

Investigating rolling as mechanism for humeral fractures in non-ambulant infants : a preliminary finite element study

AIM To use personalised computed tomography (CT)-based finite element models to quantitatively investigate the likelihood of self-inflicted humeral fracture in non-ambulant infants secondary to rolling. MATERIALS AND METHODS Three whole-body post-mortem CT examinations of children at the age of rolling (two 4-month-old and one 6-month-old) were used. The mechanical moment needed by each infant to perform a rolling manoeuvre was calculated and applied to the finite element model in order to simulate spontaneous rolling from the prone to the supine position. RESULTS The maximum predicted strains were found to be substantially lower (with a difference of >80%) than the elastic limit of the bone. CONCLUSION Results of this study challenge the plausibility of self-inflicted humeral fracture caused by rolling in non-ambulant infants and indicate that it is unlikely for a humeral fracture to result from this mechanism without the assistance of an external force

Femoral neck strain prediction during level walking using a combined musculoskeletal and finite element model approach

Recently, coupled musculoskeletal-finite element modelling approaches have emerged as a way to investigate femoral neck loading during various daily activities. Combining personalised gait data with finite element models will not only allow us to study changes in motion/movement, but also their effects on critical internal structures, such as the femur. However, previous studies have been hampered by the small sample size and the lack of fully personalised data in order to construct the coupled model. Therefore, the aim of this study was to build a pipeline for a fully personalised multiscale (body-organ level) model to investigate the strain levels at the femoral neck during a normal gait cycle. Five postmenopausal women were included in this study. The CT and MRI scans of the lower limb, and gait data were collected for all participants. Muscle forces derived from the body level musculoskeletal models were used as boundary constraints on the finite element femur models. Principal strains were estimated at the femoral neck region during a full gait cycle. Considerable variation was found in the predicted peak strain among individuals with mean peak first principal strain of 0.24% ± 0.11% and mean third principal strain of -0.29% ± 0.24%. For four individuals, two overall peaks of the maximum strains were found to occur when both feet were in contact with the floor, while one individual had one peak at the toe-off phase. Both the joint contact forces and the muscular forces were found to substantially influence the loading at the femoral neck. A higher correlation was found between the predicted peak strains and the gluteus medius (R2 ranged between 0.95 and 0.99) than the hip joint contact forces (R2 ranged between 0.63 and 0.96). Therefore, the current findings suggest that personal variations are substantial, and hence it is important to consider multiple subjects before deriving general conclusions for a target population

A multiscale model to predict current absolute risk of femoral fracture in a postmenopausal population

Osteoporotic hip fractures are a major healthcare problem. Fall severity and bone strength are important risk factors of hip fracture. This study aims to obtain a mechanistic explanation for fracture risk in dependence of these risk factors. A novel modelling approach is developed that combines models at different scales to overcome the challenge of a large space–time domain of interest and considers the variability of impact forces between potential falls in a subject. The multiscale model and its component models are verified with respect to numerical approximations made therein, the propagation of measurement uncertainties of model inputs is quantified, and model predictions are validated against experimental and clinical data. The main results are model predicted absolute risk of current fracture (ARF0) that ranged from 1.93 to 81.6% (median 36.1%) for subjects in a retrospective cohort of 98 postmenopausal British women (49 fracture cases and 49 controls); ARF0 was computed up to a precision of 1.92 percentage points (pp) due to numerical approximations made in the model; ARF0 possessed an uncertainty of 4.00 pp due to uncertainties in measuring model inputs; ARF0 classified observed fracture status in the above cohort with AUC = 0.852 (95% CI 0.753–0.918), 77.6% specificity (95% CI 63.4–86.5%) and 81.6% sensitivity (95% CI 68.3–91.1%). These results demonstrate that ARF0 can be computed using the model with sufficient precision to distinguish between subjects and that the novel mechanism of fracture risk determination based on fall dynamics, hip impact and bone strength can be considered validated

Toward simple, generalizable neural networks with universal training for low-SWaP hybrid vision

Speed, generalizability, and robustness are fundamental issues for building lightweight computational cameras. Here we demonstrate generalizable image reconstruction with the simplest of hybrid machine vision systems: linear optical preprocessors combined with no-hidden-layer, “small-brain” neural networks. Surprisingly, such simple neural networks are capable of learning the image reconstruction from a range of coded diffraction patterns using two masks. We investigate the possibility of generalized or “universal training” with these small brains. Neural networks trained with sinusoidal or random patterns uniformly distribute errors around a reconstructed image, whereas models trained with a combination of sharp and curved shapes (the phase pattern of optical vortices) reconstruct edges more boldly. We illustrate variable convergence of these simple neural networks and relate learnability of an image to its singular value decomposition entropy of the image. We also provide heuristic experimental results. With thresholding, we achieve robust reconstruction of various disjoint datasets. Our work is favorable for future real-time low size, weight, and power hybrid vision: we reconstruct images on a 15 W laptop CPU with 15,000 frames per second: faster by a factor of 3 than previously reported results and 3 orders of magnitude faster than convolutional neural networks

In vivo and in vitro studies on renal uptake of radiolabeled affibody molecules for imaging of HER2 expression in tumors

Contains fulltext : 118633.pdf (publisher's version ) (Closed access)Affibody molecules (6-7 kDa) are a new class of small robust three-helical scaffold proteins. Radiolabeled subnanomolar anti-HER2 affibody ZHER2:342 was developed for imaging of HER2 expression in tumors, and a clinical study has demonstrated that the (111)In- and (68)Ga-labeled affibody molecules can efficiently detect HER2 expressing metastases in breast cancer patients. However, a significant renal accumulation of radioactivity after systemic injection of a radiolabeled anti-HER2 affibody conjugate is observed. The aim of this study was to investigate the mechanism of renal reabsorption of anti-HER2 affibody at the molecular level. Renal accumulation of radiolabeled anti-HER2 affibody molecules was studied in a murine model and in vitro using opossum-derived proximal tubule (OK) cells. It was found that kidney reabsorption of affibody molecule was not driven by megalin/cubilin. Amino acids in the target-binding side of affibody molecule were involved in binding to OK cells. On OK cells, two types of receptors for anti-HER2 affibody molecule were found: KD1=0.8 nM, Bmax1=71,500 and KD2=9.2 nM, Bmax2=367,000. The results of the present study indicate that affibody molecule and other scaffold-based targeting proteins with a relatively low kidney uptake can be selected using in vitro studies with tubular kidney cells.Altai, Mohamed Varasteh, Zohreh Andersson, Karl Eek, Annemarie Boerman, Otto Orlova, Anna Research Support, Non-U.S. Gov't United States Cancer Biother Radiopharm. 2013 Apr;28(3):187-95. doi: 10.1089/cbr.2012.1304. Epub 2013 Mar 5