Mechanical properties of calvarial bones in a mouse model for craniosynostosis

The mammalian cranial vault largely consists of five flat bones that are joined together along their edges by soft fibrous tissues called sutures. Premature closure of the cranial sutures, craniosynostosis, can lead to serious clinical pathology unless there is surgical intervention. Research into the genetic basis of the disease has led to the development of various animal models that display this condition, e.g. mutant type Fgfr2C342Y/+ mice which display early fusion of the coronal suture (joining the parietal and frontal bones). However, whether the biomechanical properties of the mutant and wild type bones are affected has not been investigated before. Therefore, nanoindentation was used to compare the elastic modulus of cranial bone and sutures in wild type (WT) and Fgfr2C342Y/+mutant type (MT) mice during their postnatal development. Further, the variations in properties with indentation position and plane were assessed. No difference was observed in the elastic modulus of parietal bone between the WT and MT mice at postnatal (P) day 10 and 20. However, the modulus of frontal bone in the MT group was lower than the WT group at both P10 (1.39±0.30 vs. 5.32±0.68 GPa; p<0.05) and P20 (5.57±0.33 vs. 7.14±0.79 GPa; p<0.05). A wide range of values was measured along the coronal sutures for both the WT and MT samples, with no significant difference between the two groups. Findings of this study suggest that the inherent mechanical properties of the frontal bone in the mutant mice were different to the wild type mice from the same genetic background. These differences may reflect variations in the degree of biomechanical adaptation during skull growth, which could have implications for the surgical management of craniosynostosis patients

Computer simulation of glioma growth and morphology

Despite major advances in the study of glioma, the quantitative links between intra-tumor molecular/cellular properties, clinically observable properties such as morphology, and critical tumor behaviors such as growth and invasiveness remain unclear, hampering more effective coupling of tumor physical characteristics with implications for prognosis and therapy. Although molecular biology, histopathology, and radiological imaging are employed in this endeavor, studies are severely challenged by the multitude of different physical scales involved in tumor growth, i.e., from molecular nanoscale to cell microscale and finally to tissue centimeter scale. Consequently, it is often difficult to determine the underlying dynamics across dimensions. New techniques are needed to tackle these issues. Here, we address this multi-scalar problem by employing a novel predictive three-dimensional mathematical and computational model based on first-principle equations (conservation laws of physics) that describe mathematically the diffusion of cell substrates and other processes determining tumor mass growth and invasion. The model uses conserved variables to represent known determinants of glioma behavior, e.g., cell density and oxygen concentration, as well as biological functional relationships and parameters linking phenomena at different scales whose specific forms and values are hypothesized and calculated based on in vitro and in vivo experiments and from histopathology of tissue specimens from human gliomas. This model enables correlation of glioma morphology to tumor growth by quantifying interdependence of tumor mass on the microenvironment (e.g., hypoxia, tissue disruption) and on the cellular phenotypes (e.g., mitosis and apoptosis rates, cell adhesion strength). Once functional relationships between variables and associated parameter values have been informed, e.g., from histopathology or intra-operative analysis, this model can be used for disease diagnosis/prognosis, hypothesis testing, and to guide surgery and therapy. In particular, this tool identifies and quantifies the effects of vascularization and other cell-scale glioma morphological characteristics as predictors of tumor-scale growth and invasion

Evaluation System for Craniosynostosis Surgeries with Computer Simulation and Statistical Modelling

Craniosynostosis is a pathology in infants when one or more sutures prematurely closed, leading to abnormal skull shape. It has been classified according to the specific suture that has been closed, each of which has a typical skull shape. Surgery is the common treatment to correct the deformed skull shape and to reduce the excessive intracranial pressure. Since every case is unique, the cranial facial teams have difficulties to select an optimum solution for a specific patient from multiple options. In addition, there is not an appropriate quantified measurement existed currently to help cranial facial team to quantitatively evaluate their surgeries. We aimed to develop a head model of a craniosynostosis patient, which allows neurosurgeons to perform any potential surgeries on it so as to simulate the postoperative head development. Therefore, neurosurgeons could foresee the surgical results and is able to select the optimal one. In this thesis, we have developed a normal head model, and built mathematical models for possible dynamic behaviors. We also modified this model by closing one or two sutures to simulate common types of craniosynostosis. The abnormal simulation results showed a qualitative match with real cases and the normal simulation indicated a higher growth rate of cranial index than clinical data. We believed that this discrepancy caused by the rigidity of our skull plates, which will be adapted to deformable object in the future. In order to help neurosurgeons to better evaluate a surgery, we hope to develop an algorithm to quantify the level of deformity of a skull. We have designed a set of work flow and targeted curvatures as the key role. A training data was carefully selected to search for an optimal system to characterize different shapes. A set of test data was used to validate our algorithm to assess the performance of the optimal system. With a stable evaluating system, we can evaluate a surgery by comparing the preoperative and postoperative skulls from the patient. An effective surgery can be considered if the postoperative skull shifted toward normal shape from preoperative shape

Hybrid foetus with an FE head for a pregnant occupant model for vehicle safety investigations

‘Expecting’, a computational pregnant occupant model, developed to simulate the dynamic response to crash impacts, possesses anthropometric properties of a fifth percentile female at around the 38th week of pregnancy. The model is complete with a finite element uterus and a multi-body foetus which is a novel feature in models of this kind. In this paper, the effect of incorporating a foetus with a finite element head into ‘Expecting’ is investigated. The finite element head was developed using detailed anatomic geometry and projected material properties. Then it was integrated with the ‘Expecting’ model and validated using the lap belt loading and the rigid bar impact tests. The model is then used to simulate frontal impacts at a range of crash severities with seatbelt and airbag, seatbelt only, airbag only as well as no restraint cases to investigate the risk of placental abruption and compare it with the model featuring the original multi-body foetus. The maximum strains developed in the utero-placental interface are used as the main criteria for foetus safety. The results show comparable strain levels to those from the multi-body foetus. It is, therefore, recommended to use the multi-body foetus in simulations as the computation time is more favourable

Controlling a mobile robot with a biological brain

The intelligent controlling mechanism of a typical mobile robot is usually a computer system. Some recent research is ongoing in which biological neurons are being cultured and trained to act as the brain of an interactive real world robot�thereby either completely replacing, or operating in a cooperative fashion with, a computer system. Studying such hybrid systems can provide distinct insights into the operation of biological neural structures, and therefore, such research has immediate medical implications as well as enormous potential in robotics. The main aim of the research is to assess the computational and learning capacity of dissociated cultured neuronal networks. A hybrid system incorporating closed-loop control of a mobile robot by a dissociated culture of neurons has been created. The system is flexible and allows for closed-loop operation, either with hardware robot or its software simulation. The paper provides an overview of the problem area, gives an idea of the breadth of present ongoing research, establises a new system architecture and, as an example, reports on the results of conducted experiments with real-life robots

Incorporation of biomechanical child cadaver neck behaviour in a child model and injury prediction in vehicle frontal crash

This research was completed in an effort to improve the biofidelity of a finite element child model and the accuracy of injury predictions in forward facing child restraint seats during numerical simulations of frontal crashes. After material alterations to the child model, neck tensile force was found to be within the range of cadaver tests and the rotation-moment curves were in good agreement with the corridor of the pediatric cadaver head/neck complex tests. The altered child model has illustrated more accurate biomechanical responses and kinematics; its biofidelity has been improved. The upper and lower neck tensile forces of the child model were reduced by approximately 35% and 41%, respectively. Tensile deformation of the child neck was increased by 2.75 times while rotational deformation increased by 37%. The percentage error of the maximum displacements of the child head was reduced from approximately 16% to 13.5%