Soo-Kng Teo

A three-dimensional random network model of the cytoskeleton and its role in mechanotransduction and nucleus deformation

We have developed a three-dimensional random network model of the intracellular actin cytoskeleton and have used it to study the role of the cytoskeleton in mechanotransduction and nucleus deformation. We use the model to predict the deformation of the nucleus when mechanical stresses applied on the plasma membrane are propagated through the random cytoskeletal network to the nucleus membrane. We found that our results agree with previous experiments utilizing micropipette pulling. Therefore, we propose that stress propagation through the random cytoskeletal network can be a mechanism to effect nucleus deformation, without invoking any biochemical signaling activity. Using our model, we also predict how nucleus strain and its relative displacement within the cytosol vary with varying concentrations of actin filaments and actin-binding proteins. We find that nucleus strain varies in a sigmoidal manner with actin filament concentration, while there exists an optimal concentration of actin-binding proteins that maximize nucleus displacement. We provide a theoretical analysis for these nonlinearities in terms of the connectivity of the random cytoskeletal network. Finally, we discuss laser ablation experiments that can be performed to validate these results in order to advance our understanding of the role of the cytoskeleton in mechanotransduction.

Design and Construction of an Equibiaxial Cell Stretching System That Is Improved for Biochemical Analysis

We describe the design and validation of an equibiaxial stretching device in which cells are confined to regions of homogeneous strain. Using this device, we seek to overcome a significant limitation of existing equibiaxial stretching devices, in which strains are not homogeneous over the entire region of cell culture. We cast PDMS in a mold to produce a membrane with a cylindrical wall incorporated in the center, which was used to confine cell monolayers to the central membrane region subjected to homogeneous equibiaxial strain. We demonstrated that the presence of the wall to hold the culture medium did not affect strain homogeneity over the majority of the culture surface and also showed that cells adhered well onto the PDMS membranes. We used our device in cyclic strain experiments and demonstrated strain-dependent changes in extracellular signal-regulated kinase (ERK) and tyrosine phosphorylation upon cell stretching. Furthermore, we examined cell responses to very small magnitudes of strain ranging from 1% to 6% and were able to observe a graduated increase in ERK phosphorylation in response to these strains. Collectively, we were able to study cellular biochemical response with a high degree of accuracy and sensitivity to fine changes in substrate strain. Because we have designed our device along the lines of existing equibiaxial stretching technologies, we believe that our innovations can be incorporated into existing systems. This device would provide a useful addition to the set of tools applied for in vitro studies of cell mechanobiology.

Regional ejection fraction and regional area strain for left ventricular function assessment in male patients after first-time myocardial infarction

[ J. R. Soc. Interface 12 , 20150006 (2015; Published online 18 February 2015) ([doi:10.1098/rsif.2015.0006][2])][2] The x -axis tick labels (for REF) in the original [figure 3][2] are wrong. In [figure 3][2], there were a total of 11 ticks on the x -axis, and their respective labels are [0, 20, 40

Advanced analyses of computed tomography coronary angiography can help discriminate ischemic lesions

BACKGROUND Computed tomography coronary angiography (CTCA) image analysis enables plaque characterization and non-invasive fractional flow reserve (FFR) calculation. We analyzed various parameters derived from CTCA images and evaluated their associations with ischemia. METHODS 49 (61 lesions) patients underwent CTCA and invasive FFR. Lesions with diameter stenosis (DS) ≥ 50% were considered obstructive. CTCA image processing incorporating analytical and numerical methods were used to quantify anatomical parameters of lesion length (LL) and minimum lumen area (MLA); plaque characteristic parameters of plaque volume, low attenuation plaque (LAP) volume, dense calcium volume (DCV), normalized plaque volume (NP Vol), plaque burden, eccentricity index and napkin-ring (NR) sign; and hemodynamic parameters of resistance index, stenosis flow reserve (SFR) and FFRB. Ischemia was defined as FFR ≤ 0.8. RESULTS Plaque burden and plaque volume were inversely related to FFR. Multivariable logistic regression analysis identified the best anatomical, plaque and hemodynamic predictors, respectively, as DS (≥50% vs <50%; OR: 8.0; 95% CI: 1.6-39.4), normalized plaque volume (NP Vol) (≥4.3 vs <4.3; OR: 3.9; 95% CI: 1.1-14.0) and NR Sign (0 vs 1; OR: 13.6; 95% CI: 1.3-146.1), and FFRB (≤0.8 vs >0.8; OR: 44.4; 95% CI: 8.8-224.8). AUC increased from 0.70 with DS as the sole predictor to 0.81 after adding NP Vol and NR Sign; further addition of FFRB increased AUC to 0.93. CONCLUSION Normalized plaque volume, napkin-ring derived from plaque analysis, and FFRB from numerical simulations on CTCA images substantially improved discrimination of ischemic lesions, compared to assessment by DS alone.

Left Ventricular Wall Stress Is Sensitive Marker of Hypertrophic Cardiomyopathy With Preserved Ejection Fraction

Hypertrophic cardiomyopathy (HCM) patients present altered myocardial mechanics due to the hypertrophied ventricular wall and are typically diagnosed by the increase in myocardium wall thickness. This study aimed to quantify regional left ventricular (LV) shape, wall stress and deformation from cardiac magnetic resonance (MR) images in HCM patients and controls, in order to establish superior measures to differentiate HCM from controls. A total of 19 HCM patients and 19 controls underwent cardiac MR scans. The acquired MR images were used to reconstruct 3D LV geometrical models and compute the regional parameters (i.e., wall thickness, curvedness, wall stress, area strain and ejection fraction) based on the standard 16 segment model using our in-house software. HCM patients were further classified into four quartiles based on wall thickness at end diastole (ED) to assess the impact of wall thickness on these regional parameters. There was a significant difference between the HCM patients and controls for all regional parameters (P < 0.001). Wall thickness was greater in HCM patients at the end-diastolic and end-systolic phases, and thickness was most pronounced in segments at the septal regions. A multivariate stepwise selection algorithm identified wall stress index at ED (σi,ED) as the single best independent predictor of HCM (AUC = 0.947). At the cutoff value σi,ED < 1.64, both sensitivity and specificity were 94.7%. This suggests that the end-diastolic wall stress index incorporating regional wall curvature—an index based on mechanical principle—is a sensitive biomarker for HCM diagnosis with potential utility in diagnostic and therapeutic assessment.

ANALYSIS OF RIGHT VENTRICULAR REMODELING USING CURVATURE HISTOGRAM COMPARISON

Understanding and quantifying right ventricular (RV) remodeling in repaired Tetralogy of Fallot (TOF) is crucial for patient management and therapy planning, i.e., in determining the optimal time for pulmonary valve replacement. However, quantification of RV remodeling is usually hampered by its complex geometry. This paper presents a computer-aided cardiac assessment methodology to quantitate RV remodeling in terms of a histogram similarity index, based on the surface curvature distribution of three-dimensional (3D) RV geometries at both the end-diastole and end-systole phases. These 3D RV geometries are reconstructed from border delineated cardiac MRI images, whereby a surface fitting algorithm is then used to calculate the curvature distribution of the 3D models. The curvature histograms at ED and ES are computed and their similarities are measured using the Bhattacharya Similarity Metric, which is denoted as hdist. Based on an initial study involving 5 TOF patients and 5 normal subjects, we observed that the mean hdist for the normal controls is significantly higher (p = 0.0015 < 0.05 and p = 0.004 < 0.05; student t-test and Mann-Whitney- Wilcoxon test, respectively) as compared to that of the TOF patients. This suggests that hdist can be used as a discriminant between TOF patients and normal control.

A virtual surgical training system that simulates cutting of soft tissue using a modified pre-computed elastic model

This work presents a surgical training system that incorporates cutting operation of soft tissue simulated based on a modified pre-computed linear elastic model in the Simulation Open Framework Architecture (SOFA) environment. A precomputed linear elastic model used for the simulation of soft tissue deformation involves computing the compliance matrix a priori based on the topological information of the mesh. While this process may require a few minutes to several hours, based on the number of vertices in the mesh, it needs only to be computed once and allows real-time computation of the subsequent soft tissue deformation. However, as the compliance matrix is based on the initial topology of the mesh, it does not allow any topological changes during simulation, such as cutting or tearing of the mesh. This work proposes a way to modify the pre-computed data by correcting the topological connectivity in the compliance matrix, without re-computing the compliance matrix which is computationally expensive.

Automatic generation of surface meshes for right ventricle with 1-to-1 correspondence from cine-MR images

We develop an automatic method to generate a set of 4D 1-to-1 corresponding surface meshes of the right ventricle (RV) which are motion registered over the whole cardiac cycle. The inputs are a set of 3D RV surface meshes at different phases of the cardiac cycle, each reconstructed independently from border-delineated MR images. To generate point correspondence, a template mesh is matched to the actual shape of the RV meshes in each of the time phases. This is done via a coarse matching phase and a fine matching phase. In the former, an initial rough matching between the template and the target is achieved using a radial basis function (RBF) morphing process. The feature points on the template and target meshes are automatically identified using a parameterization method. In the latter, a progressive mesh projection process is used to conform the rough estimate to fit the exact shape of the target. In addition, an optimization-based smoothing process is used to achieve superior mesh quality and continuous point motion. Eight healthy volunteers were recruited for MRI scanning and the algorithm was tested on the acquired data. It was observed that the algorithm took an average of approximately 70 seconds to complete. The maximum absolute deviation of the matched model from the original geometry was 0.187mm.

Tracking of tumor motion in lung cancer using patient specific finite element modeling and 4D-MRI image data

This paper presents a study that demonstrates the potential of using finite element (FE) lung model constructed using 4D-MRI (3D + time) for tracking tumor motion during a respiratory cycle. A series of volumetric images of one lung cancer patient was acquired over time under free breathing and sorted into respiratory phases. A FE model of the lung with the tumor was constructed using the volume which is at full exhale phase. Displacement field from this initial volume to the subsequent 3D volumes in the respiratory phases were derived using a deformable image registration technique. This displacement field which provides displacement information of the lung surface is then used to predict the tumor motion in the lung interior using the FE model. Our results showed that the tumor motion (as represented by the trajectory of the tumor centroid) follows a highly non-linear path during the respiratory cycle from the full exhale phase to the full inhale phase. We also showed that the predicted tumor motion from our FE model is in reasonable agreement with that computed from 4D-MRI.