Characterizing heterogeneous properties of cerebral aneurysms with unknown stress-free geometry: a precursor to in vivo identification

Knowledge of elastic properties of cerebral aneurysms is crucial for understanding the biomechanical behavior of the lesion. However, characterizing tissue properties using in vivo motion data presents a tremendous challenge. Aside from the limitation of data accuracy, a pressing issue is that the in vivo motion does not expose the stress-free geometry. This is compounded by the nonlinearity, anisotropy, and heterogeneity of the tissue behavior. This article introduces a method for identifying the heterogeneous properties of aneurysm wall tissue under unknown stress-free configuration. In the proposed approach, an accessible configuration is taken as the reference; the unknown stress-free configuration is represented locally by a metric tensor describing the prestrain from the stress-free configuration to the reference configuration. Material parameters are identified together with the metric tensor pointwisely. The paradigm is tested numerically using a forward-inverse analysis loop. An image-derived sac is considered. The aneurysm tissue is modeled as an eightply laminate whose constitutive behavior is described by an anisotropic hyperelastic strain-energy function containing four material parameters. The parameters are assumed to vary continuously in two assigned patterns to represent two types of material heterogeneity. Nine configurations between the diastolic and systolic pressures are generated by forward quasi-static finite element analyses. These configurations are fed to the inverse analysis to delineate the material parameters and the metric tensor. The recovered and the assigned distributions are in good agreement. A forward verification is conducted by comparing the displacement solutions obtained from the recovered and the assigned material parameters at a different pressure. The nodal displacements are found in excellent agreement.     

Nonlinear anisotropic stress analysis of anatomically realistic cerebral aneurysms

BACKGROUND: Static deformation analysis and estimation of wall stress distribution of patient-specific cerebral aneurysms can provide useful insights into the disease process and rupture. METHOD OF APPROACH: The three-dimensional geometry of saccular cerebral aneurysms from 27 patients (18 unruptured and nine ruptured) was reconstructed based on computer tomography angiography images. The aneurysm wall tissue was modeled using a nonlinear, anisotropic, hyperelastic material model (Fung-type) which was incorporated in a user subroutine in ABAQUS. Effective material fiber orientations were assumed to align with principal surface curvatures. Static deformation of the aneurysm models were simulated assuming uniform wall thickness and internal pressure load of 100 mm Hg. RESULTS: The numerical analysis technique was validated by quantitative comparisons to results in the literature. For the patient-specific models, in-plane stresses in the aneurysm wall along both the stiff and weak fiber directions showed significant regional variations with the former being higher. The spatial maximum of stress ranged from as low as 0.30 MPa in a small aneurysm to as high as 1.06 MPa in a giant aneurysm. The patterns of distribution of stress, strain, and surface curvature were found to be similar. Sensitivity analyses showed that the computed stress is mesh independent and not very sensitive to reasonable perturbations in model parameters, and the curvature-based criteria for fiber orientations tend to minimize the total elastic strain energy in the aneurysms wall. Within this small study population, there were no statistically significant differences in the spatial means and maximums of stress and strain values between the ruptured and unruptured groups. However, the ratios between the stress components in the stiff and weak fiber directions were significantly higher in the ruptured group than those in the unruptured group. CONCLUSIONS: A methodology for nonlinear, anisotropic static deformation analysis of geometrically realistic aneurysms was developed, which can be used for a more accurate estimation of the stresses and strains than previous methods and to facilitate prospective studies on the role of stress in aneurysm rupture.     

Identifying subgroup markers in heterogeneous populations

Traditional methods that aim to identify biomarkers that distinguish between two groups, like Significance Analysis of Microarrays or the t-test, perform optimally when such biomarkers show homogeneous behavior within each group and differential behavior between the groups. However, in many applications, this is not the case. Instead, a subgroup of samples in one group shows differential behavior with respect to all other samples. To successfully detect markers showing such imbalanced patterns of differential signal, a different approach is required. We propose a novel method, specifically designed for the Detection of Imbalanced Differential Signal (DIDS). We use an artificial dataset and a human breast cancer dataset to measure its performance and compare it with three traditional methods and four approaches that take imbalanced signal into account. Supported by extensive experimental results, we show that DIDS outperforms all other approaches in terms of power and positive predictive value. In a mouse breast cancer dataset, DIDS is the only approach that detects a functionally validated marker of chemotherapy resistance. DIDS can be applied to any continuous value data, including gene expression data, and in any context where imbalanced differential signal is manifested.     

Identifying anisotropic constraints in multiply labeled bacteriorhodopsin by 15N MAOSS NMR: a general approach to structural studies of membrane proteins

Structural models of membrane proteins can be refined with sets of multiple orientation constraints derived from structural NMR studies of specifically labeled amino acids. The magic angle oriented sample spinning (MAOSS) NMR approach was used to determine a set of orientational constraints in bacteriorhodopsin (bR) in the purple membrane (PM). This method combines the benefits of magic angle spinning (MAS), i.e., improved sensitivity and resolution, with the ability to measure the orientation of anisotropic interactions, which provide important structural information. The nine methionine residues in bacteriorhodopsin were isotopically (15)N labeled and spectra simplified by deuterium exchange before cross-polarization magic angle spinning (CPMAS) experiments. The orientation of the principal axes of the (15)N chemical shift anisotropy (CSA) tensors was determined with respect to the membrane normal for five of six residual resonances by analysis of relative spinning sideband intensities. The applicability of this approach to large proteins embedded in a membrane environment is discussed in light of these results.     

Identifying airways responsible for heterogeneous ventilation and mechanical dysfunction in asthma: an image functional modeling approach.

We present an image functional modeling approach, which synthesizes imaging and mechanical data with anatomically explicit computational models. This approach is utilized to identify the relative importance of small and large airways in the simultaneous deterioration of mechanical function and ventilation in asthma. Positron emission tomographic (PET) images provide the spatial distribution and relative extent of ventilation defects in asthmatic subjects postbronchoconstriction. We also measured lung resistance and elastance from 0.15 to 8 Hz. The first step in image functional modeling involves mapping ventilation three-dimensional images to the computational model and identifying the largest sized airways of the model that, if selectively constricted, could precisely match the size and anatomic location of ventilation defects imaged by PET. In data from six asthmatic subjects, these airways had diameters <2.39 mm and mostly <0.44 mm. After isolating and effectively closing airways in the model associated with these ventilation defects, we imposed constriction with various means and standard deviations to the remaining airways to match the measured lung resistance and elastance from the same subject. Our results show that matching both the degree of mechanical impairment and the size and location of the PET ventilation defects requires either constriction of airways <2.4 mm alone, or a simultaneous constriction of small and large airways, but not just large airways alone. Also, whereas larger airway constriction may contribute to mechanical dysfunction during asthma, degradation in ventilation function requires heterogeneous distribution of near closures confined to small airways.     

Determination of the heterogeneous anisotropic elastic properties of human femoral bone: From nanoscopic to organ scale

Cortical bone is a multiscale composite material. Its elastic properties are anisotropic and heterogeneous across its cross-section, due to endosteal bone resorption which might affect bone strength. The aim of this paper was to describe a homogenization method leading to the estimation of the variation of the elastic coefficients across the bone cross-section and along the bone longitudinal axis. The method uses the spatial variations of bone porosity and of the degree of mineralization of the bone matrix (DMB) obtained from the analysis of 3-D synchrotron micro-computed tomography images. For all three scales considered (the foam (100 nm), the ultrastructure (5 μm) and the mesoscale (500 μm)), the elastic coefficients were determined using the Eshelby’s inclusion problem. DMB values were used at the scale of the foam. Collagen was introduced at the scale of the ultrastructure and bone porosity was introduced at the mesoscale. The pores were considered as parallel cylinders oriented along the bone axis. Each elastic coefficient was computed for different regions of interest, allowing an estimation of its variations across the bone cross-section and along the bone longitudinal axis. The method was applied to a human femoral neck bone specimen, which is a site of osteoporotic fracture. The computed elastic coefficients for cortical bone were in good agreement with experimental results, but some discrepancies were obtained in the endosteal part (trabecular bone). These results highlight the importance of accounting for the heterogeneity of cortical bone properties across bone cross-section and along bone longitudinal axis.     

Identifying hosts of families of viruses: a machine learning approach

Identifying emerging viral pathogens and characterizing their transmission is essential to developing effective public health measures in response to an epidemic. Phylogenetics, though currently the most popular tool used to characterize the likely host of a virus, can be ambiguous when studying species very distant to known species and when there is very little reliable sequence information available in the early stages of the outbreak of disease. Motivated by an existing framework for representing biological sequence information, we learn sparse, tree-structured models, built from decision rules based on subsequences, to predict viral hosts from protein sequence data using popular discriminative machine learning tools. Furthermore, the predictive motifs robustly selected by the learning algorithm are found to show strong host-specificity and occur in highly conserved regions of the viral proteome.     

Identifying alert concentrations using a model-based bootstrap approach

The determination of alert concentrations, where a pre-specified threshold of the response variable is exceeded, is an important goal of concentration–response studies. The traditional approach is based on investigating the measured concentrations and attaining statistical significance of the alert concentration by using a multiple t-test procedure. In this paper, we propose a new model-based method to identify alert concentrations, based on fitting a concentration–response curve and constructing a simultaneous confidence band for the difference of the response of a concentration compared to the control. In order to obtain these confidence bands, we use a bootstrap approach which can be applied to any functional form of the concentration–response curve. This particularly offers the possibility to investigate also those situations where the concentration–response relationship is not monotone and, moreover, to detect alerts at concentrations which were not measured during the study, providing a highly flexible framework for the problem at hand.     

Identifying non-sperm particles during flow cytometric physiological assessment: a simple approach

Flow cytometry is now being used more frequently to determine sperm functional characteristics during semen assessment for artificial insemination. With this methodology, viable and potentially functional cells are detected as unstained events differentiated from non-sperm events through their light-scattering characteristics. However, it can be shown mathematically that identification of sperm on the basis of light scatter leads to significant overestimation of unstained viable cells and underestimation of responding cells in tests of sperm function (subpopulations expressing different fluorescence patterns). We have developed a simple and cost-efficient flow cytometric approach for identifying non-sperm particles that can be carried out in parallel with functional assessments. Our method is based on the sperm's osmotic intolerance. Diluted in water, lethal osmotic shock causes major damage to the cell membranes, and all sperm will stain with propidium iodide (PI). Particulate material which is not PI-positive can then be quantitatively evaluated by FACS analysis and the results substituted in mathematical equations to provide true values for sperm counts and subpopulations. In practical tests, the percentage of non-sperm particles determined by this technique was closely comparable to the figure obtained either by SYBR14^®/PI staining or by PI/CFDA staining. As well as being valuable with respect to tests of sperm function, the procedure is also suitable for obtaining accurate sperm counts during routine semen evaluation.     

Extracellular matrix composition and remodeling in human abdominal aortic aneurysms: a proteomics approach

Abdominal aortic aneurysms (AAA) are characterized by pathological remodeling of the aortic extracellular matrix (ECM). However, besides the well-characterized elastolysis and collagenolysis little is known about changes in other ECM proteins. Previous proteomics studies on AAA focused on cellular changes without emphasis on the ECM. In the present study, ECM proteins and their degradation products were selectively extracted from aneurysmal and control aortas using a solubility-based subfractionation methodology and analyzed by gel-liquid chromatography-tandem MS and label-free quantitation. The proteomics analysis revealed novel changes in the ECM of AAA, including increased expression as well as degradation of collagen XII, thrombospondin 2, aortic carboxypeptidase-like protein, periostin, fibronectin and tenascin. Proteomics also confirmed the accumulation of macrophage metalloelastase (MMP-12). Incubation of control aortic tissue with recombinant MMP-12 resulted in the extensive fragmentation of these glycoproteins, most of which are novel substrates of MMP-12. In conclusion, our proteomics methodology allowed the first detailed analysis of the ECM in AAA and identified markers of pathological ECM remodeling related to MMP-12 activity.     

Identifying natural substrates for chaperonins using a sequence-based approach

The Escherichia coli chaperonin machinery, GroEL, assists the folding of a number of proteins. We describe a sequence-based approach to identify the natural substrate proteins (SPs) for GroEL. Our method is based on the hypothesis that natural SPs are those that contain patterns of residues similar to those found in either GroES mobile loop and/or strongly binding peptide in complex with GroEL. The method is validated by comparing the predicted results with experimentally determined natural SPs for GroEL. We have searched for such patterns in five genomes. In the E. coli genome, we identify 1422 (about one-third) sequences that are putative natural SPs. In Saccharomyces cerevisiae, 2885 (32%) of sequences can be natural substrates for Hsp60, which is the analog of GroEL. The precise number of natural SPs is shown to be a function of the number of contacts an SP makes with the apical domain (N(C)) and the number of binding sites (N(B)) in the oligomer with which it interacts. For known SPs for GroEL, we find approximately 4 < N(C) < 5 and 2 相似文献   

Growth rate and rupture rate of unruptured intracranial aneurysms: a population approach

Background  

Understanding aneurysm growth rate allows us to predict not only the current rupture risk, but also accumulated rupture risk in the future. However, determining growth rate of unruptured intracranial aneurysms often requires follow-up of patients for a long period of time so that significant growth can be observed and measured. We investigate a relationship between growth rate and rupture rate and develop a theoretical model that can predict average behavior of unruptured intracranial aneurysms based on existing clinical data.     

Identifying vulnerable pathways in Mycobacterium tuberculosis by using a knockdown approach

We constructed recombinant strains of Mycobacterium tuberculosis in which expression of specific genes was downregulated to identify vulnerable drug targets. Growth phenotypes in macrophages and culture were used to rank targets: the dprE1, clpP1, and fadD32 operons were the best targets and glnA1, glnE, pknL, regX3, and senX3 were poor targets.     

The future of human cerebral cartography: a novel approach

Cerebral cartography can be understood in a limited, static, neuroanatomical sense. Temporal information from electrical recordings contributes information on regional interactions adding a functional dimension. Selective tagging and imaging of molecules adds biochemical contributions. Cartographic detail can also be correlated with normal or abnormal psychological or behavioural data. Modern cerebral cartography is assimilating all these elements. Cartographers continue to collect ever more precise data in the hope that general principles of organization will emerge. However, even detailed cartographic data cannot generate knowledge without a multi-scale framework making it possible to relate individual observations and discoveries. We propose that, in the next quarter century, advances in cartography will result in progressively more accurate drafts of a data-led, multi-scale model of human brain structure and function. These blueprints will result from analysis of large volumes of neuroscientific and clinical data, by a process of reconstruction, modelling and simulation. This strategy will capitalize on remarkable recent developments in informatics and computer science and on the existence of much existing, addressable data and prior, though fragmented, knowledge. The models will instantiate principles that govern how the brain is organized at different levels and how different spatio-temporal scales relate to each other in an organ-centred context.     

Computational methods for predicting drug transport in anisotropic and heterogeneous brain tissue

Effective drug delivery for many neurodegenerative diseases or tumors of the central nervous system is challenging. Targeted invasive delivery of large macromolecules such as trophic factors to desired locations inside the brain is difficult due to anisotropy and heterogeneity of the brain tissue. Despite much experimental research, prediction of bio-transport phenomena inside the brain remains unreliable. This article proposes a rigorous computational approach for accurately predicting the fate of infused therapeutic agents inside the brain. Geometric and physiological properties of anisotropic and heterogeneous brain tissue affecting drug transport are accounted for by in-vivo diffusion tensor magnetic resonance imaging data. The three-dimensional brain anatomy is reconstructed accurately from subject-specific medical images. Tissue anisotropy and heterogeneity are quantified with the help of diffusion tensor imaging (DTI). Rigorous first principles physical transport phenomena are applied to predict the fate of a high molecular weight trophic factor infused into the midbrain. Computer prediction of drug distribution in humans accounting for heterogeneous and anisotropic brain tissue properties have not been adequately researched in open literature before.