The binding mode of side chain- and C3-modified epothilones to tubulin

The tubulin-binding mode of C3- and C15-modified analogues of epothilone A (Epo A) was determined by NMR spectroscopy and computational methods and compared with the existing structural models of tubulin-bound natural Epo A. Only minor differences were observed in the conformation of the macrocycle between Epo A and the C3-modified analogues investigated. In particular, 3-deoxy- (compound 2) and 3-deoxy-2,3-didehydro-Epo A (3) were found to adopt similar conformations in the tubulin-binding cleft as Epo A, thus indicating that the 3-OH group is not essential for epothilones to assume their bioactive conformation. None of the available models of the tubulin-epothilone complex is able to fully recapitulate the differences in tubulin-polymerizing activity and microtubule-binding affinity between C20-modified epothilones 6 (C20-propyl), 7 (C20-butyl), and 8 (C20-hydroxypropyl). Based on the results of transferred NOE experiments in the presence of tubulin, the isomeric C15 quinoline-based Epo B analogues 4 and 5 show very similar orientations of the side chain, irrespective of the position of the nitrogen atom in the quinoline ring. The quinoline side chain stacks on the imidazole moiety of beta-His227 with equal efficiency in both cases, thus suggesting that the aromatic side chain moiety in epothilones contributes to tubulin binding through strong van der Waals interactions with the protein rather than hydrogen bonding involving the heteroaromatic nitrogen atom. These conclusions are in line with existing tubulin polymerization and microtubule-binding data for 4, 5, and Epo B.

Influence of Smoothing on Voxel-Based Mesh Accuracy in Micro-Finite Element

The interest in automatic volume meshing for finite element analysis (FEA) has grown more since the appearance of microfocus CT (μCT), due to its high resolution, which allows for the assessment of mechanical behaviour at a high precision. Nevertheless, the basic meshing approach of generating one hexahedron per voxel produces jagged edges. To prevent this effect, smoothing algorithms have been introduced to enhance the topology of the mesh. However, whether smoothing also improves the accuracy of voxel-based meshes in clinical applications is still under question. There is a trade-off between smoothing and quality of elements in the mesh. Distorted elements may be produced by excessive smoothing and reduce accuracy of the mesh. In the present work, influence of smoothing on the accuracy of voxel-based meshes in micro-FE was assessed. An accurate 3D model of a trabecular structure with known apparent mechanical properties was used as a reference model. Virtual CT scans of this reference model (with resolutions of 16, 32 and 64 μm) were then created and used to build voxel-based meshes of the microarchitecture. Effects of smoothing on the apparent mechanical properties of the voxel-based meshes as compared to the reference model were evaluated. Apparent Young’s moduli of the smooth voxel-based mesh were significantly closer to those of the reference model for the 16 and 32 μm resolutions. Improvements were not significant for the 64 μm, due to loss of trabecular connectivity in the model. This study shows that smoothing offers a real benefit to voxel-based meshes used in micro-FE. It might also broaden voxel-based meshing to other biomechanical domains where it was not used previously due to lack of accuracy. As an example, this work will be used in the framework of the European project ContraCancrum, which aims at providing a patient-specific simulation of tumour development in brain and lungs for oncologists. For this type of clinical application, such a fast, automatic, and accurate generation of the mesh is of great benefit.

Mesh-based vs. Image-based Statistical Appearance Model of the Human Femur: a Preliminary Comparison Study for the Creation of Finite Element Meshes

Statistical models have been recently introduced in computational orthopaedics to investigate the bone mechanical properties across several populations. A fundamental aspect for the construction of statistical models concerns the establishment of accurate anatomical correspondences among the objects of the training dataset. Various methods have been proposed to solve this problem such as mesh morphing or image registration algorithms. The objective of this study is to compare a mesh-based and an image-based statistical appearance model approaches for the creation of nite element(FE) meshes. A computer tomography (CT) dataset of 157 human left femurs was used for the comparison. For each approach, 30 finite element meshes were generated with the models. The quality of the obtained FE meshes was evaluated in terms of volume, size and shape of the elements. Results showed that the quality of the meshes obtained with the image-based approach was higher than the quality of the mesh-based approach. Future studies are required to evaluate the impact of this finding on the final mechanical simulations.

Soft-Tissue Simulation for Cranio-Maxillofacial Surgery: Clinical Needs and Technical Aspects

Computerized soft-tissue simulation can provide unprecedented means for predicting facial outlook pre-operatively. Surgeons can virtually perform several surgical plans to have the best surgical results for their patients while considering corresponding soft-tissue outcome. It could be used as an interactive communication tool with their patients as well. There has been comprehensive amount of works for simulating soft-tissue for cranio-maxillofacial surgery. Although some of them have been realized as commercial products, none of them has been fully integrated into clinical practice due to the lack of accuracy and excessive amount of processing time. In this chapter, state-of-the-art and general workflow in facial soft-tissue simulation will be presented, along with an example of patient-specific facial soft-tissue simulation method.

Femur Specific Polyaffine Model to Regularize the Log-Domain Demons Registration

Osteoarticular allograft transplantation is a popular treatment method in wide surgical resections with large defects. For this reason hospitals are building bone data banks. Performing the optimal allograft selection on bone banks is crucial to the surgical outcome and patient recovery. However, current approaches are very time consuming hindering an efficient selection. We present an automatic method based on registration of femur bones to overcome this limitation. We introduce a new regularization term for the log-domain demons algorithm. This term replaces the standard Gaussian smoothing with a femur specific polyaffine model. The polyaffine femur model is constructed with two affine (femoral head and condyles) and one rigid (shaft) transformation. Our main contribution in this paper is to show that the demons algorithm can be improved in specific cases with an appropriate model. We are not trying to find the most optimal polyaffine model of the femur, but the simplest model with a minimal number of parameters. There is no need to optimize for different number of regions, boundaries and choice of weights, since this fine tuning will be done automatically by a final demons relaxation step with Gaussian smoothing. The newly developed synthesis approach provides a clear anatomically motivated modeling contribution through the specific three component transformation model, and clearly shows a performance improvement (in terms of anatomical meaningful correspondences) on 146 CT images of femurs compared to a standard multiresolution demons. In addition, this simple model improves the robustness of the demons while preserving its accuracy. The ground truth are manual measurements performed by medical experts.

Fully Automatic Segmentation of Brain Tumor Images using Support Vector Machine Classiffication in Combination with Hierarchical Conditional Random Field Regularization

Delineating brain tumor boundaries from magnetic resonance images is an essential task for the analysis of brain cancer. We propose a fully automatic method for brain tissue segmentation, which combines Support Vector Machine classification using multispectral intensities and textures with subsequent hierarchical regularization based on Conditional Random Fields. The CRF regularization introduces spatial constraints to the powerful SVM classification, which assumes voxels to be independent from their neighbors. The approach first separates healthy and tumor tissue before both regions are subclassified into cerebrospinal fluid, white matter, gray matter and necrotic, active, edema region respectively in a novel hierarchical way. The hierarchical approach adds robustness and speed by allowing to apply different levels of regularization at different stages. The method is fast and tailored to standard clinical acquisition protocols. It was assessed on 10 multispectral patient datasets with results outperforming previous methods in terms of segmentation detail and computation times.

Minimization of Intra-Operative Shaping of Orthopaedic Fixation Plates: A Population-Based Design

In this paper we present a new population-based method for the design of bone fixation plates. Standard pre-contoured plates are designed based on the mean shape of a certain population. We propose a computational process to design implants while reducing the amount of required intra-operative shaping, thus reducing the mechanical stresses applied to the plate. A bending and torsion model was used to measure and minimize the necessary intra-operative deformation. The method was applied and validated on a population of 200 femurs that was further augmented with a statistical shape model. The obtained results showed substantial reduction in the bending and torsion needed to shape the new design into any bone in the population when compared to the standard mean-based plates.

Geometry-Aware Multiscale Image Registration via OBBTree-Based Polyaffine Log-Demons

Non-linear image registration is an important tool in many areas of image analysis. For instance, in morphometric studies of a population of brains, free-form deformations between images are analyzed to describe the structural anatomical variability. Such a simple deformation model is justified by the absence of an easy expressible prior about the shape changes. Applying the same algorithms used in brain imaging to orthopedic images might not be optimal due to the difference in the underlying prior on the inter-subject deformations. In particular, using an un-informed deformation prior often leads to local minima far from the expected solution. To improve robustness and promote anatomically meaningful deformations, we propose a locally affine and geometry-aware registration algorithm that automatically adapts to the data. We build upon the log-domain demons algorithm and introduce a new type of OBBTree-based regularization in the registration with a natural multiscale structure. The regularization model is composed of a hierarchy of locally affine transformations via their logarithms. Experiments on mandibles show improved accuracy and robustness when used to initialize the demons, and even similar performance by direct comparison to the demons, with a significantly lower degree of freedom. This closes the gap between polyaffine and non-rigid registration and opens new ways to statistically analyze the registration results.

Segmentation of brain tumor images based on atlas-registration combined with a Markov-Random-Field lesion growth model

We present an automatic method to segment brain tissues from volumetric MRI brain tumor images. The method is based on non-rigid registration of an average atlas in combination with a biomechanically justified tumor growth model to simulate soft-tissue deformations caused by the tumor mass-effect. The tumor growth model, which is formulated as a mesh-free Markov Random Field energy minimization problem, ensures correspondence between the atlas and the patient image, prior to the registration step. The method is non-parametric, simple and fast compared to other approaches while maintaining similar accuracy. It has been evaluated qualitatively and quantitatively with promising results on eight datasets comprising simulated images and real patient data.

The effects of dynamic loading on the intervertebral disc

Loading is important to maintain the balance of matrix turnover in the intervertebral disc (IVD). Daily cyclic diurnal assists in the transport of large soluble factors across the IVD and its surrounding circulation and applies direct and indirect stimulus to disc cells. Acute mechanical injury and accumulated overloading, however, could induce disc degeneration. Recently, there is more information available on how cyclic loading, especially axial compression and hydrostatic pressure, affects IVD cell biology. This review summarises recent studies on the response of the IVD and stem cells to applied cyclic compression and hydrostatic pressure. These studies investigate the possible role of loading in the initiation and progression of disc degeneration as well as quantifying a physiological loading condition for the study of disc degeneration biological therapy. Subsequently, a possible physiological/beneficial loading range is proposed. This physiological/beneficial loading could provide insight into how to design loading regimes in specific system for the testing of various biological therapies such as cell therapy, chemical therapy or tissue engineering constructs to achieve a better final outcome. In addition, the parameter space of 'physiological' loading may also be an important factor for the differentiation of stem cells towards most ideally 'discogenic' cells for tissue engineering purpose.

Erratum to: The effects of dynamic loading on the intervertebral disc

Erratum to: Eur Spine J DOI 10.1007/s00586-011-1827-1 In the original article ‘‘Acknowledgments’’ was missing. The Acknowledgment is given below: Acknowledgments This project was supported by the Swiss National Science Foundation (SNF # 310030-127586/1) and the Department for Orthopedic Research, Insel University Hospital, Bern, Switzerland.