A mechanistic model of a PDGFRα(+) cell.

A novel platelet-derived growth factor receptor alpha-positive cell (PDGFRα(+)) has recently been identified as part of the purinergic inhibitory neural control mechanism in the gastrointestinal (GI) tract. The mechanism through which PDGFRα(+) cells mediate GI muscle relaxation has been found to be associated with the purine receptors P2Y1 and apamin-sensitive SK3 channels that are highly expressed in these cells. This study aims to develop a mechanistic model elucidating a proposed mechanism through which PDGFRα(+) cells contribute to purinergic inhibitory neuromuscular transmission. In accordance with recent experimental findings, the model describes how the binding of neurotransmitters, released from enteric neurons, triggers the release of Ca(2+) from the endoplasmic reticulum in the PDGFRα(+) cells, and how this subsequently leads to large amplitude transient outward currents, which in turn hyperpolarize the cell. The model has been validated against experimental recordings and good agreement was found under normal and pharmacologically-altered conditions. This model demonstrates the feasibility of the proposed mechanism and provides a basis for understanding the mechanism underlying purinergic control of colonic motility.

The origin of intraluminal pressure waves in gastrointestinal tract.

The gastrointestinal (GI) peristalsis is an involuntary wave-like contraction of the GI wall that helps to propagate food along the tract. Many GI diseases, e.g., gastroparesis, are known to cause motility disorders in which the physiological contractile patterns of the wall get disrupted. Therefore, to understand the pathophysiology of these diseases, it is necessary to understand the mechanism of GI motility. We present a coupled electromechanical model to describe the mechanism of GI motility and the transduction pathway of cellular electrical activities into mechanical deformation and the generation of intraluminal pressure (IP) waves in the GI tract. The proposed model consolidates a smooth muscle cell (SMC) model, an actin-myosin interaction model, a hyperelastic constitutive model, and a Windkessel model to construct a coupled model that can describe the origin of peristaltic contractions in the intestine. The key input to the model is external electrical stimuli, which are converted into mechanical contractile waves in the wall. The model recreated experimental observations efficiently and was able to establish a relationship between change in luminal volume and pressure with the compliance of the GI wall and the peripheral resistance to bolus flow. The proposed model will help us understand the GI tract's function in physiological and pathophysiological conditions.

Biventricular finite element modeling of the fetal heart in health and during critical aortic stenosis.

Finite Element simulations are a robust way of investigating cardiac biomechanics. To date, it has only been performed with the left ventricle (LV) alone for fetal hearts, even though results are likely different with biventricular (BiV) simulations. In this research, we conduct BiV simulations of the fetal heart based on 4D echocardiography images to show that it can capture the biomechanics of the normal healthy fetal heart, as well as those of fetal aortic stenosis better than the LV alone simulations. We found that performing LV alone simulations resulted in overestimation of LV stresses and pressures, compared to BiV simulations. Interestingly, inserting a compliance between the LV and right ventricle (RV) in the lumped parameter model of the LV only simulation effectively resolved these overestimations, demonstrating that the septum could be considered to play a LV-RV pressure communication role. However, stresses and strains spatial patterns remained altered from BiV simulations after the addition of the compliance. The BiV simulations corroborated previous studies in showing disease effects on the LV, where fetal aortic stenosis (AS) drastically elevated LV pressures and reduced strains and stroke volumes, which were moderated down with the addition of mitral regurgitation (MR). However, BiV simulations enabled an evaluation of the RV as well, where we observed that effects of the AS and MR on pressures and stroke volumes were generally much smaller and less consistent. The BiV simulations also enabled investigations of septal dynamics, which showed a rightward shift with AS, and partial restoration with MR. Interestingly, AS tended to enhance RV stroke volume, but MR moderated that down.

Are Macula or Optic Nerve Head Structures Better at Diagnosing Glaucoma? An Answer Using Artificial Intelligence and Wide-Field Optical Coherence Tomography.

Purpose: We wanted to develop a deep-learning algorithm to automatically segment optic nerve head (ONH) and macula structures in three-dimensional (3D) wide-field optical coherence tomography (OCT) scans and to assess whether 3D ONH or macula structures (or a combination of both) provide the best diagnostic power for glaucoma. Methods: A cross-sectional comparative study was performed using 319 OCT scans of glaucoma eyes and 298 scans of nonglaucoma eyes. Scans were compensated to improve deep-tissue visibility. We developed a deep-learning algorithm to automatically label major tissue structures, trained with 270 manually annotated B-scans. The performance was assessed using the Dice coefficient (DC). A glaucoma classification algorithm (3D-CNN) was then designed using 500 OCT volumes and corresponding automatically segmented labels. This algorithm was trained and tested on three datasets: cropped scans of macular tissues, those of ONH tissues, and wide-field scans. The classification performance for each dataset was reported using the area under the curve (AUC). Results: Our segmentation algorithm achieved a DC of 0.94 ± 0.003. The classification algorithm was best able to diagnose glaucoma using wide-field scans, followed by ONH scans, and finally macula scans, with AUCs of 0.99 ± 0.01, 0.93 ± 0.06 and 0.91 ± 0.11, respectively. Conclusions: This study showed that wide-field OCT may allow for significantly improved glaucoma diagnosis over typical OCTs of the ONH or macula. Translational Relevance: This could lead to mainstream clinical adoption of 3D wide-field OCT scan technology.

Contribution of Ventricular Motion and Sampling Location to Discrepancies in Two-Dimensional Versus Three-Dimensional Fetal Ventricular Strain Measures.

BACKGROUND: Echocardiographic quantification of fetal cardiac strain is important to evaluate function and the need for intervention, with both two-dimensional (2D) and three-dimensional (3D) strain measurements currently feasible. However, discrepancies between 2D and 3D measurements have been reported, the etiologies of which are unclear. This study sought to determine the etiologies of the differences between 2D and 3D strain measurements. METHODS: A validated cardiac motion-tracking algorithm was used on 3D cine ultrasound images acquired in 26 healthy fetuses. Both 2D and 3D myocardial strain quantifications were performed on each image set for controlled comparisons. Finite element modeling of 2 left ventricle (LV) models with minor geometrical differences were performed with various helix angle configurations for validating image processing results. RESULTS: Three-dimensional longitudinal strain (LS) was significantly lower than 2D LS for the LV free wall and septum but not for the right ventricular (RV) free wall, while 3D circumferential strain (CS) was significantly higher than 2D CS for the LV, RV, and septum. The LS discrepancy was due to 2D long-axis imaging not capturing the out-of-plane motions associated with LV twist, while the CS discrepancy was due to the systolic motion of the heart toward the apex that caused out-of-plane motions in 2D short-axis imaging. A timing mismatch between the occurrences of peak longitudinal and circumferential dimensions caused a deviation in zero-strain referencing between 2D and 3D strain measurements, contributing to further discrepancies between the 2. CONCLUSIONS: Mechanisms for discrepancies between 2D and 3D strain measurements in fetal echocardiography were identified, and inaccuracies associated with 2D strains were highlighted. Understanding of this mechanism is useful and important for future standardization of fetal cardiac strain measurements, which we propose to be important in view of large discrepancies in measured values in the literature.

Quantification of gastrointestinal sodium channelopathy.

Na(v)1.5 sodium channels, encoded by SCN5A, have been identified in human gastrointestinal interstitial cells of Cajal (ICC) and smooth muscle cells (SMC). A recent study found a novel, rare missense R76C mutation of the sodium channel interacting protein telethonin in a patient with primary intestinal pseudo-obstruction. The presence of a mutation in a patient with a motility disorder, however, does not automatically imply a cause-effect relationship between the two. Patch clamp experiments on HEK-293 cells previously established that the R76C mutation altered Na(v)1.5 channel function. Here the process through which these data were quantified to create stationary Markov state models of wild-type and R76C channel function is described. The resulting channel descriptions were included in whole cell ICC and SMC computational models and simulations were performed to assess the cellular effects of the R76C mutation. The simulated ICC slow wave was decreased in duration and the resting membrane potential in the SMC was depolarized. Thus, the R76C mutation was sufficient to alter ICC and SMC cell electrophysiology. However, the cause-effect relationship between R76C and intestinal pseudo-obstruction remains an open question.

Biventricular biaxial mechanical testing and constitutive modelling of fetal porcine myocardium passive stiffness.

The evaluation of fetal heart mechanical function is becoming increasingly important for determining the prognosis and making subsequent decisions on the treatment and management of congenital heart diseases. Finite Element (FE) modelling can potentially provide detailed information on fetal hearts, and help perform virtual interventions to assist in predicting outcomes and supporting clinical decisions. Previous FE studies have enabled an improved understanding of healthy and diseased fetal heart biomechanics. However, to date, the mechanical properties of the fetal myocardium have not been well characterized which limits the reliability of such modelling. Here, we characterize the passive mechanical properties of late fetal and neonatal porcine hearts via biaxial mechanical testing as a surrogate for human fetal heart mechanical properties. We used samples from both the right and left ventricles over the late gestational period from 85 days of gestation to birth. Constitutive modelling was subsequently performed with a transversely isotropic Fung-type model and a Humphrey-type model, using fiber orientations identified with histology. We found no significant difference in mechanical stiffness across all age groups and between the right and left ventricular samples. This was likely due to the similarity in LV and RV pressures in the fetal heart, and similar gestational maturity across these late gestational ages. We thus recommend using the constitutive model for the average stress-stress behaviour of the tissues in future modelling work. Furthermore, we characterized the variability of the stiffness to inform such work.

The three-dimensional structural configuration of the central retinal vessel trunk and branches as a glaucoma biomarker.

PURPOSE: To assess whether the 3-dimensional (3D) structural configuration of the central retinal vessel trunk and its branches (CRVT&B) could be used as a diagnostic marker for glaucoma. DESIGN: Retrospective, deep-learning approach diagnosis study. METHODS: We trained a deep learning network to automatically segment the CRVT&B from the B-scans of the optical coherence tomography (OCT) volume of the optic nerve head. Subsequently, 2 different approaches were used for glaucoma diagnosis using the structural configuration of the CRVT&B as extracted from the OCT volumes. In the first approach, we aimed to provide a diagnosis using only 3D convolutional neural networks and the 3D structure of the CRVT&B. For the second approach, we projected the 3D structure of the CRVT&B orthographically onto sagittal, frontal, and transverse planes to obtain 3 two-dimensional (2D) images, and then a 2D convolutional neural network was used for diagnosis. The segmentation accuracy was evaluated using the Dice coefficient, whereas the diagnostic accuracy was assessed using the area under the receiver operating characteristic curves (AUCs). The diagnostic performance of the CRVT&B was also compared with that of retinal nerve fiber layer (RNFL) thickness (calculated in the same cohorts). RESULTS: Our segmentation network was able to efficiently segment retinal blood vessels from OCT scans. On a test set, we achieved a Dice coefficient of 0.81 ± 0.07. The 3D and 2D diagnostic networks were able to differentiate glaucoma from nonglaucoma subjects with accuracies of 82.7% and 83.3%, respectively. The corresponding AUCs for the CRVT&B were 0.89 and 0.90, higher than those obtained with RNFL thickness alone (AUCs ranging from 0.74 to 0.80). CONCLUSIONS: Our work demonstrated that the diagnostic power of the CRVT&B is superior to that of a gold-standard glaucoma parameter, that is, RNFL thickness. Our work also suggested that the major retinal blood vessels form a "skeleton"-the configuration of which may be representative of major optic nerve head structural changes as typically observed with the development and progression of glaucoma.

Describing the Structural Phenotype of the Glaucomatous Optic Nerve Head Using Artificial Intelligence.

PURPOSE: To develop a novel deep-learning approach that can describe the structural phenotype of the glaucomatous optic nerve head (ONH) and can be used as a robust glaucoma diagnosis tool. DESIGN: Retrospective, deep-learning approach diagnosis study. METHOD: We trained a deep-learning network to segment 3 neural-tissue and 4 connective-tissue layers of the ONH. The segmented optical coherence tomography images were then processed by a customized autoencoder network with an additional parallel branch for binary classification. The encoder part of the autoencoder reduced the segmented optical coherence tomography images into a low-dimensional latent space (LS), whereas the decoder and the classification branches reconstructed the images and classified them as glaucoma or nonglaucoma, respectively. We performed principal component analysis on the latent parameters and identified the principal components (PCs). Subsequently, the magnitude of each PC was altered in steps and reported how it impacted the morphology of the ONH. RESULTS: The image reconstruction quality and diagnostic accuracy increased with the size of the LS. With 54 parameters in the LS, the diagnostic accuracy was 92.0 ± 2.3% with a sensitivity of 90.0 ± 2.4% (at 95% specificity), and the corresponding Dice coefficient for the reconstructed images was 0.86 ± 0.04. By changing the magnitudes of PC in steps, we were able to reveal how the morphology of the ONH changes as one transitions from a "nonglaucoma" to a "glaucoma" condition. CONCLUSIONS: Our network was able to identify novel biomarkers of the ONH for glaucoma diagnosis. Specifically, the structural features identified by our algorithm were found to be related to clinical observations of glaucoma.

Changes in Iris Stiffness and Permeability in Primary Angle Closure Glaucoma.

Purpose: To evaluate the biomechanical properties of the iris by evaluating iris movement during pupil constriction and to compare such properties between healthy and primary angle-closure glaucoma (PACG) subjects. Methods: A total of 140 subjects were recruited for this study. In a dark room, the anterior segments of one eye per subject were scanned using anterior segment optical coherence tomography imaging during induced pupil constriction with an external white light source of 1700 lux. Using a custom segmentation code, we automatically isolated the iris segments from the AS-OCT images, which were then discretized and transformed into a three-dimensional point cloud. For each iris, a finite element (FE) mesh was constructed from the point cloud, and an inverse FE simulation was performed to match the clinically observed iris constriction in the AS-OCT images. Through this optimization process, we were able to identify the elastic modulus and permeability of each iris. Results: For all 140 subjects (95 healthy and 45 PACG of Indian/Chinese ethnicity; age 60.2 ± 8.7 for PACG subjects and 57.7 ± 10.1 for healthy subjects), the simulated deformation pattern of the iris during pupil constriction matched well with OCT images. We found that the iris stiffness was higher in PACG than in healthy controls (24.5 ± 8.4 kPa vs. 17.1 ± 6.6 kPa with 40 kPa of active stress specified in the sphincter region; P < 0.001), whereas iris permeability was lower (0.41 ± 0.2 mm2/kPa s vs. 0.55 ± 0.2 mm2/kPa s; p = 0.142). Conclusions: This study suggests that the biomechanical properties of the iris in PACG are different from those in healthy controls. An improved understanding of the biomechanical behavior of the iris may have implications for the understanding and management of angle-closure glaucoma.

OCT-GAN: single step shadow and noise removal from optical coherence tomography images of the human optic nerve head.

Speckle noise and retinal shadows within OCT B-scans occlude important edges, fine textures and deep tissues, preventing accurate and robust diagnosis by algorithms and clinicians. We developed a single process that successfully removed both noise and retinal shadows from unseen single-frame B-scans within 10.4ms. Mean average gradient magnitude (AGM) for the proposed algorithm was 57.2% higher than current state-of-the-art, while mean peak signal to noise ratio (PSNR), contrast to noise ratio (CNR), and structural similarity index metric (SSIM) increased by 11.1%, 154% and 187% respectively compared to single-frame B-scans. Mean intralayer contrast (ILC) improvement for the retinal nerve fiber layer (RNFL), photoreceptor layer (PR) and retinal pigment epithelium (RPE) layers decreased from 0.362 ± 0.133 to 0.142 ± 0.102, 0.449 ± 0.116 to 0.0904 ± 0.0769, 0.381 ± 0.100 to 0.0590 ± 0.0451 respectively. The proposed algorithm reduces the necessity for long image acquisition times, minimizes expensive hardware requirements and reduces motion artifacts in OCT images.

An extended bidomain framework incorporating multiple cell types.

The muscular layers within the walls of the gastrointestinal tract contain two distinct cell types, the interstitial cells of Cajal and smooth muscle cells, which together produce rhythmic depolarizations known as slow waves. The bidomain model of tissue-level electrical activity consists of single intracellular and extracellular domains separated by an intervening membrane at all points in space and is therefore unable to adequately describe the presence of two distinct cell types in its conventional form. Here, an extension to the bidomain framework is presented whereby multiple interconnected cell types can be incorporated. Although the derivation is focused on the interactions of the interstitial cells of Cajal and smooth muscle cells, the conceptual framework can be more generally applied. Simulations demonstrating the feasibility of the proposed model are also presented.

Reference descriptions of cellular electrophysiology models.

UNLABELLED: In recent years there has been much development of the fundamental ideas underlying mathematical model curation in regard to models of biology. While much has been achieved in the realms of systems biology and bioinformatics, little progress has been made in relation to cellular electrophysiology modeling. The primary reason for slow progress in this field is the lack of a consistent and machine-readable reference description for a given model. CellML has been widely used to describe mathematical models of cellular electrophysiology in an unambiguous, machine-readable format. Through the use of well-annotated CellML models we propose a standard by which reference descriptions of cellular electrophysiology models can be similarly defined in an unambiguous, software independent, and machine-readable format. Adoption of this standard will provide a consistent technology by which cellular electrophysiology models can be curated. AVAILABILITY: http://www.bioeng.nus.edu.sg/compbiolab/p2/

A finite element approach for gastrointestinal tissue mechanics.

The biomechanical properties of gastrointestinal (GI) tissue play a significant role in the normal functioning of the organ. GI soft tissues exhibit a highly nonlinear rate- and time-dependent stress-strain behaviour. In recent years, many constitutive relations have been proposed to characterize these properties. However, a constitutive relation is not sufficient to analyse the biomechanics at the organ level with complex loading and boundary conditions. Hence, for a refined mechanical analysis, a finite element (FE) implementation of the constitutive relation is needed. Here, we propose an FE implementation of a finite nonlinear hyperviscoelastic model suitable for soft biological tissues. The FE model has been validated at first by comparing its results with the analytical solutions of a standard linear solid, and then it has been used to recreate experimental observations performed on tissue strips obtained from different animals. We have also proposed a method, in this work, to construct a residually stressed FE model so that the consequences of residual stresses on GI mechanics can be examined. Our FE formulation was able to capture the nonlinear soft tissue properties and also demonstrated that the addition of residual stresses reduces stress concentrations and the stress gradient in the GI wall.

Anatomically realistic multiscale models of normal and abnormal gastrointestinal electrical activity.

One of the major aims of the International Union of Physiological Sciences (IUPS) Physiome Project is to develop multiscale mathematical and computer models that can be used to help understand human health. We present here a small facet of this broad plan that applies to the gastrointestinal system. Specifically, we present an anatomically and physiologically based modelling framework that is capable of simulating normal and pathological electrical activity within the stomach and small intestine. The continuum models used within this framework have been created using anatomical information derived from common medical imaging modalities and data from the Visible Human Project. These models explicitly incorporate the various smooth muscle layers and networks of interstitial cells of Cajal (ICC) that are known to exist within the walls of the stomach and small bowel. Electrical activity within individual ICCs and smooth muscle cells is simulated using a previously published simplified representation of the cell level electrical activity. This simulated cell level activity is incorporated into a bidomain representation of the tissue, allowing electrical activity of the entire stomach or intestine to be simulated in the anatomically derived models. This electrical modelling framework successfully replicates many of the qualitative features of the slow wave activity within the stomach and intestine and has also been used to investigate activity associated with functional uncoupling of the stomach.

Modelling Human Colonic Smooth Muscle Cell Electrophysiology.

The colon is a digestive organ that is subject to a wide range of motility disorders. However, our understanding of the etiology of these disorders is far from complete. In this study, a quantitative single cell model has been developed to describe the electrical behaviour of a human colonic smooth muscle cell (hCSMC). This model includes the pertinent ionic channels and intracellular calcium homoeostasis. These components are believed to contribute significantly to the electrical response of the hCSMC during a slow wave. The major ion channels were constructed based on published data recorded from isolated human colonic myocytes. The whole cell model is able to reproduce experimentally recorded slow waves from human colonic muscles. This represents the first biophysically-detailed model of a hCSMC and provides a means to better understand colonic disorders.

An integrative software package for gastrointestinal biomagnetic data acquisition and analysis using SQUID magnetometers.

The study of bioelectric and biomagnetic activity in the human gastrointestinal (GI) tract is of great interest in clinical research due to the proven possibility to detect pathological conditions thereof from electric and magnetic field recordings. The magnetogastrogram (MGG) and magnetoenterogram (MENG) can be recorded using superconducting quantum interference device (SQUID) magnetometers, which are the most sensitive magnetic flux-to-voltage converters currently available. To address the urgent need for powerful acquisition and analysis software tools faced by many researchers and clinicians in this important area of investigation, an integrative and modular computer program was developed for the acquisition, processing and analysis of GI SQUID signals. In addition to a robust hardware implementation for efficient data acquisition, a number of signal processing and analysis modules were developed to serve in a variety of both clinical procedures and scientific investigations. Implemented software features include data processing and visualization, waterfall plots of signal frequency spectra as well as spatial maps of GI signal frequencies. Moreover, a software tool providing powerful 3D visualizations of GI signals was created using realistic models of the human torso and internal organs.

Mechanical testing and non-linear viscoelastic modelling of the human placenta in normal and growth restricted pregnancies.

BACKGROUND: Intrauterine Growth Restriction (IUGR) is a disease where the placenta is unable to transfer enough nutrients to the fetus, limiting its growth, and resulting in high mortality and life-long morbidities. Current detection rates of IUGR are poor, resulting in limited disease management. Elastography is a promising non-invasive tool for the detection of IUGR, and works by detecting changes in the mechanical properties of the placenta. To date, however, it is not known whether IUGR placentas have different mechanical properties from normal ones, and thus investigating this is the first focus of the current study. The second focus is to evaluate and model the viscoelastic properties of the normal and IUGR placenta, so that it may be possible to improve elastography in the future by incorporating viscoelasticity. METHODS: Cyclic uniaxial mechanical compression testing was conducted on post-delivery human placenta samples. 18 samples from 5 normal placentae and 12 samples from 3 IUGR placentae were tested. Viscoelastic models were fitted to the resulting experimental data. RESULTS: Mechanical testing showed that IUGR placentae have reduced stiffness and viscosity compared to normal placentae. Linear viscoelastic models were unable to provide a good fit to the data, but non-linear viscoelastic solid (NVS) models could do so. The best performing model was a five parameters bi-exponential NVS model. Two of the five parameters appear to capture the differences between normal and diseased samples. DISCUSSION: Our results demonstrate that IUGR placentae have different mechanical properties from normal placentae, and a five parameter bi-exponential NVS model can effectively describe the mechanical properties of the placenta in health and disease.

The effect of torso impedance on epicardial and body surface potentials: a modeling study.

Experimental results have been published that report marked changes in measured epicardial potentials when the conductivity of the material surrounding the heart is altered. These reports raise a question as to the validity of the traditional two step, equivalent cardiac source approach to modeling the forward problem of electrocardiology as the equivalent source calculation occurs in what is effectively an isolated cardiac region. In the physical situation the heart is surrounded by a torso that contains many different tissue types with different conductivities and is certainly not isolated. Here, a fully coupled model of the problem is employed where the electrical pathways are continuous from a cellular level through to the body surface. This model is used to investigate the effects that torso inhomogeneities have on epicardial and body surface potentials, including comparisons with a traditional two step approach. In particular, it is shown that adding lungs changes the epicardial potentials by 17%, which is consistent with the reported experimental results. In none of the tested situations did the equivalent source approach completely reproduce the fully coupled results, supporting the notion that a fully coupled approach is required to properly solve the forward problem of electrocardiology.

Modelling tissue electrophysiology with multiple cell types: applications of the extended bidomain framework.

The bidomain framework has been extensively used to model tissue electrophysiology in a variety of applications. One limitation of the bidomain model is that it describes the activity of only one cell type interacting with the extracellular space. If more than one cell type contributes to the tissue electrophysiology, then the bidomain model is not sufficient. Recently, evidence has suggested that this is the case for at least two important applications: cardiac and gastrointestinal tissue electrophysiology. In the heart, fibroblasts ubiquitously interact with myocytes and are believed to play an important role in the organ electrophysiology. Along the GI tract, interstitial cells of Cajal (ICC) generate electrical waves that are passed on to surrounding smooth muscle cells (SMC), which are interconnected with the ICC and with each other. Because of the contribution of more than one cell type to the overall organ electrophysiology, investigators in different fields have independently proposed similar extensions of the bidomain model to incorporate multiple cell types and tested it on simplified geometries. In this paper, we provide a general derivation of such an extended bidomain framework applicable to any tissue and provide a generic and efficient implementation applicable to any geometry. Proof-of-concept results of tissue electrophysiology on realistic 3D organ geometries using the extended bidomain framework are presented for the heart and the stomach.