scholarly journals Individualised computational modelling of immune mediated disease onset, flare and clearance in psoriasis

2021 ◽  
Author(s):  
Fedor Shmarov ◽  
Graham R Smith ◽  
Sophie C Weatherhead ◽  
Nick J Reynolds ◽  
Paolo Zuliani
Keyword(s):  
Clinical Response ◽  
Computational Models ◽  
Disease Onset ◽  
Computational Modelling ◽  
Patient Specific ◽  
Prospective Clinical Study ◽  
Healthy Skin ◽  
Early Assessment ◽  
Psoriasis Area Severity Index ◽  
Sensitivity Parameter

Despite increased understanding about psoriasis pathophysiology, currently there is a lack of predictive computational models. We developed a personalisable ordinary differential equations model of human epidermis that features two stable steady states: healthy skin and psoriasis. In line with experimental data, an immune stimulus initiated transition from healthy skin to psoriasis and apoptosis induced by UVB phototherapy returned the epidermis back to the healthy state. The flexibility of our model permitted the development of a patient-specific "UVB sensitivity" parameter that enabled accurate simulation of individual patients' clinical response trajectory. In a prospective clinical study of 94 patients, serial individual UVB doses and clinical response (Psoriasis Area Severity Index) values collected over the first three weeks of UVB therapy informed estimation of the "UVB sensitivity" parameter and the prediction of patient outcome at the end of phototherapy. Notably, our model was able to distinguish disease flares and offers the potential for clinical application in early assessment of response to UVB therapy outcome, and for individualised optimisation of phototherapy regimes to improve clinical outcome.

Calculation of Carotid Artery Flow Rates Using Doppler Ultrasound: Implications of Velocity Profile Skewing

2012 ◽  
Author(s):  
Jonathan P. Mynard ◽  
David A. Steinman
Keyword(s):  
Carotid Artery ◽  
Velocity Profile ◽  
Doppler Ultrasound ◽  
Computational Models ◽  
Peak Velocity ◽  
Computational Modelling ◽  
Carotid Bifurcation ◽  
Sample Volume ◽  
Patient Specific ◽  
Specific Flow

Doppler ultrasound (DUS) is a non-invasive means of obtaining patient-specific flow boundary conditions in computational modelling studies [1] or estimating volumetric flow in clinical studies [2, 3]. To convert velocity information to a flow waveform, three related assumptions are often applied, 1) that the peak velocity lies in the centre of a cylindrical vessel, 2) that a centrally-located sample volume will thus detect the peak velocity, and 3) that the velocity profile is fully-developed and axisymmetric, being well-approximated by a parabolic (Poiseuille) or Womersley profile. These assumptions may not always be valid, however, even for nominally straight vessels like the common carotid artery (CCA) [4, 5]. While one might expect that flow estimated from DUS would become increasingly inaccurate as the profile becomes less axisymmetric, the scale of such errors and their relation to the true profile shape have not been quantified for the CCA. Moreover, for a heavily skewed velocity profile, the peak velocity may not lie within the DUS sample volume, and hence the choice of sample volume or beam-vessel orientation may also affect the accuracy of flow calculations. In this study, we investigate these issues by performing an idealized virtual DUS on data from image-based computational models of the carotid bifurcation.

scholarly journals Evaluation of a modified Cheatham-Platinum stent for the treatment of aortic coarctation by finite element modelling

2018 ◽  
Vol 7 ◽  
pp. 204800401877395 ◽  
Author(s):  
Barbara EU Burkhardt ◽  
Nicholas Byrne ◽  
Marí Nieves Velasco Forte ◽  
Francesco Iannaccone ◽  
Matthieu De Beule ◽  
...  
Keyword(s):  
Finite Element ◽  
Aortic Coarctation ◽  
Computational Models ◽  
Three Dimensional ◽  
Computational Modelling ◽  
Patient Specific ◽  
Stent Design ◽  
Initial Length ◽  
Element Analysis

Objectives Stent implantation for the treatment of aortic coarctation has become a standard approach for the management of older children and adults. Criteria for optimal stent design and construction remain undefined. This study used computational modelling to compare the performance of two generations of the Cheatham-Platinum stent (NuMED, Hopkinton, NY, USA) deployed in aortic coarctation using finite element analysis. Design Three-dimensional models of both stents, reverse engineered from microCT scans, were implanted in the aortic model of one representative patient. They were virtually expanded in the vessel with a 16 mm balloon and a pressure of 2 atm. Results The conventional stent foreshortened to 96.5% of its initial length, whereas the new stent to 99.2% of its initial length. Diameters in 15 slices across the conventional stent were 11.6–15 mm (median 14.2 mm) and slightly higher across the new stent: 10.7–15.3 mm (median 14.5 mm) (p= 0.021). Apposition to the vessel wall was similar: conventional stent 31.1% and new stent 28.6% of total stent area. Conclusions The new design Cheatham-Platinum stent showed similar deployment results compared to the conventional design. The new stent design showed slightly higher expansion, using the same delivery balloon. Patient-specific computational models can be used for virtual implantation of new aortic stents and promise to inform subsequent in vivo trials.

scholarly journals NIMG-65. STUDY OF LOCAL PERTURBATION IN COMPUTATIONAL MODELLING ON TUMOR TREATING FIELDS (TTFIELDS) THERAPY

2020 ◽  
Vol 22 (Supplement_2) ◽  
pp. ii162-ii162
Author(s):  
Oshrit Zeevi ◽  
Zeev Bomzon ◽  
Tal Marciano
Keyword(s):  
Electric Field ◽  
Treatment Planning ◽  
Computational Models ◽  
Computational Modelling ◽  
Clinical Trial Data ◽  
Patient Specific ◽  
Local Perturbation ◽  
Tumor Bed ◽  
Tumor Treating Fields ◽  
Local Perturbations

Abstract INTRODUCTION Tumor Treating Fields (TTFields) are an approved therapy for glioblastoma (GBM). A recent study combining post-hoc analysis of clinical trial data and extensive computational modelling demonstrated that TTFields dose at the tumor has a direct impact on patient survival (Ballo MT, et al. Int J Radiat Oncol Biol Phys, 2019). Hence, there is rationale for developing TTFields treatment planning tools that rely on numerical simulations and patient-specific computational models. To assist in the development of such tools is it important to understand how inaccuracies in the computational models influence the estimation of the TTFields dose delivered to the tumor bed. Here we analyze the effect of local perturbations in patient-specific head models on TTFields dose at the tumor bed. METHODS Finite element models of human heads with tumor were created. To create defects in the models, conductive spheres with varying conductivities and radii were placed into the model’s brains at different distances from the tumor. Virtual transducer arrays were placed on the models, and delivery of TTFields numerically simulated. The error in the electric field induced by the defects as a function of defect conductivity, radius, and distance to tumor was investigated. RESULTS Simulations showed that when a defect of radius R is placed at a distance, d >7R, the error is below 1% regardless of the defect conductivity. Further the defects induced errors in the electric field that were below 1% when σrR/d < 0.16, where σrR/d < 0.16, where σr = (σsphere – σsurrounding)/(σsphere + σsurrounding).σsurroundings is the average conductivity around the sphere and σsphere is the conductivity of the sphere. CONCLUSIONS This study demonstrates the limited impact of local perturbations in the model on the calculated field distribution. These results could be used as guidelines on required model accuracy for TTFields treatment planning.

scholarly journals Supervised Domain Adaptation for Automated Semantic Segmentation of the Atrial Cavity

Entropy ◽  
2021 ◽  
Vol 23 (7) ◽  
pp. 898
Author(s):  
Marta Saiz-Vivó ◽  
Adrián Colomer ◽  
Carles Fonfría ◽  
Luis Martí-Bonmatí ◽  
Valery Naranjo
Keyword(s):  
High Performance ◽  
Computational Models ◽  
Domain Adaptation ◽  
Semantic Segmentation ◽  
Patient Specific ◽  
Mr Images ◽  
Training Samples ◽  
Volumetric Images ◽  
Acquisition Costs ◽  
Left And Right

Atrial fibrillation (AF) is the most common cardiac arrhythmia. At present, cardiac ablation is the main treatment procedure for AF. To guide and plan this procedure, it is essential for clinicians to obtain patient-specific 3D geometrical models of the atria. For this, there is an interest in automatic image segmentation algorithms, such as deep learning (DL) methods, as opposed to manual segmentation, an error-prone and time-consuming method. However, to optimize DL algorithms, many annotated examples are required, increasing acquisition costs. The aim of this work is to develop automatic and high-performance computational models for left and right atrium (LA and RA) segmentation from a few labelled MRI volumetric images with a 3D Dual U-Net algorithm. For this, a supervised domain adaptation (SDA) method is introduced to infer knowledge from late gadolinium enhanced (LGE) MRI volumetric training samples (80 LA annotated samples) to a network trained with balanced steady-state free precession (bSSFP) MR images of limited number of annotations (19 RA and LA annotated samples). The resulting knowledge-transferred model SDA outperformed the same network trained from scratch in both RA (Dice equals 0.9160) and LA (Dice equals 0.8813) segmentation tasks.

scholarly journals Towards Patient-Specific Computational Modelling of Articular Cartilage on the Basis of Advanced Multiparametric MRI Techniques

2019 ◽  
Vol 9 (1) ◽  
Author(s):  
Kevin Linka ◽  
Amelie Schäfer ◽  
Markus Hillgärtner ◽  
Mikhail Itskov ◽  
Matthias Knobe ◽  
...  
Keyword(s):  
Articular Cartilage ◽  
Computational Modelling ◽  
Multiparametric Mri ◽  
Patient Specific

scholarly journals Error Propagation in the Simulation of Atherosclerotic Plaque Growth and the Prediction of Atherosclerotic Disease Progression

Diagnostics ◽  
2021 ◽  
Vol 11 (12) ◽  
pp. 2306
Author(s):  
Antonis I. Sakellarios ◽  
Panagiotis Siogkas ◽  
Vassiliki Kigka ◽  
Panagiota Tsompou ◽  
Dimitrios Pleouras ◽  
...  
Keyword(s):  
Error Propagation ◽  
Computational Models ◽  
Computational Modelling ◽  
Predictive Modelling ◽  
Prognostic Models ◽  
Current Analysis ◽  
Non Invasive ◽  
Plaque Growth ◽  
Artery Disease ◽  
Diagnostic Index

Assessments of coronary artery disease can be achieved using non-invasive computed tomography coronary angiography (CTCA). CTCA can be further used for the 3D reconstruction of the coronary arteries and the development of computational models. However, image acquisition and arterial reconstruction introduce an error which can be propagated, affecting the computational results and the accuracy of diagnostic and prognostic models. In this work, we investigate the effect of an imaging error, propagated to a diagnostic index calculated using computational modelling of blood flow and then to prognostic models based on plaque growth modelling or binary logistic predictive modelling. The analysis was performed utilizing data from 20 patients collected at two time points with interscan period of six years. The collected data includes clinical and risk factors, biological and biohumoral data, and CTCA imaging. The results demonstrated that the error propagated and may have significantly affected some of the final outcomes. The calculated propagated error seemed to be minor for shear stress, but was major for some variables of the plaque growth model. In parallel, in the current analysis SmartFFR was not considerably affected, with the limitation of only one case located into the gray zone.

scholarly journals Neuronal and Astrocytic Regulations in Schizophrenia: A Computational Modelling Study

2021 ◽  
Vol 15 ◽  
Author(s):  
Lea Fritschi ◽  
Johanna Hedlund Lindmar ◽  
Florian Scheidl ◽  
Kerstin Lenk
Keyword(s):  
Computational Models ◽  
Glutamate Release ◽  
Signal Transmission ◽  
Computational Modelling ◽  
Atp Release ◽  
Network Activity ◽  
Inhibitory Neurons ◽  
Neuronal Network Activity ◽  
Cell Densities ◽  
Synapse Model

According to the tripartite synapse model, astrocytes have a modulatory effect on neuronal signal transmission. More recently, astrocyte malfunction has been associated with psychiatric diseases such as schizophrenia. Several hypotheses have been proposed on the pathological mechanisms of astrocytes in schizophrenia. For example, post-mortem examinations have revealed a reduced astrocytic density in patients with schizophrenia. Another hypothesis suggests that disease symptoms are linked to an abnormality of glutamate transmission, which is also regulated by astrocytes (glutamate hypothesis of schizophrenia). Electrophysiological findings indicate a dispute over whether the disorder causes an increase or a decrease in neuronal and astrocytic activity. Moreover, there is no consensus as to which molecular pathways and network mechanisms are altered in schizophrenia. Computational models can aid the process in finding the underlying pathological malfunctions. The effect of astrocytes on the activity of neuron-astrocyte networks has been analysed with computational models. These can reproduce experimentally observed phenomena, such as astrocytic modulation of spike and burst signalling in neuron-astrocyte networks. Using an established computational neuron-astrocyte network model, we simulate experimental data of healthy and pathological networks by using different neuronal and astrocytic parameter configurations. In our simulations, the reduction of neuronal or astrocytic cell densities yields decreased glutamate levels and a statistically significant reduction in the network activity. Amplifications of the astrocytic ATP release toward postsynaptic terminals also reduced the network activity and resulted in temporarily increased glutamate levels. In contrast, reducing either the glutamate release or re-uptake in astrocytes resulted in higher network activities. Similarly, an increase in synaptic weights of excitatory or inhibitory neurons raises the excitability of individual cells and elevates the activation level of the network. To conclude, our simulations suggest that the impairment of both neurons and astrocytes disturbs the neuronal network activity in schizophrenia.

scholarly journals Patient-specific iPSCs carrying an SFTPC mutation reveal the intrinsic alveolar epithelial dysfunction at the inception of interstitial lung disease

2020 ◽  
Author(s):  
Konstantinos-Dionysios Alysandratos ◽  
Scott J. Russo ◽  
Anton Petcherski ◽  
Evan P. Taddeo ◽  
Rebeca Acín-Pérez ◽  
...  
Keyword(s):  
Interstitial Lung Disease ◽  
Pulmonary Fibrosis ◽  
Lung Disease ◽  
Disease Onset ◽  
Metabolic Reprogramming ◽  
Patient Specific ◽  
Autophagic Flux ◽  
Alveolar Epithelial ◽  
Epithelial Dysfunction

SummaryThe incompletely understood pathogenesis of pulmonary fibrosis (PF) and lack of reliable preclinical disease models have limited development of effective therapies. An emerging literature now implicates alveolar epithelial type 2 cell (AEC2) dysfunction as an initiating pathogenic event in the onset of a variety of PF syndromes, including adult idiopathic pulmonary fibrosis (IPF) and childhood interstitial lung disease (chILD). However, inability to access primary AEC2s from patients, particularly at early disease stages, has impeded identification of disease-initiating mechanisms. Here we present an in vitro reductionist model system that permits investigation of epithelial-intrinsic events that lead to AEC2 dysfunction over time using patient-derived cells that carry a disease-associated variant, SFTPCI73T, known to be expressed solely in AEC2s. After generating patient-specific induced pluripotent stem cells (iPSCs) and engineering their gene-edited (corrected) counterparts, we employ directed differentiation to produce pure populations of syngeneic corrected and mutant AEC2s, which we expand >1015 fold in vitro, providing a renewable source of cells for modeling disease onset. We find that mutant iPSC-derived AEC2s (iAEC2s) accumulate large amounts of misprocessed pro-SFTPC protein which mistrafficks to the plasma membrane, similar to changes observed in vivo in the donor patient’s AEC2s. These changes result in marked reduction in AEC2 progenitor capacity and several downstream perturbations in AEC2 proteostatic and bioenergetic programs, including a late block in autophagic flux, accumulation of dysfunctional mitochondria with consequent time-dependent metabolic reprograming from oxidative phosphorylation to glycolysis, and activation of an NF-κB dependent inflammatory response. Treatment of SFTPCI73T expressing iAEC2s with hydroxychloroquine, a medication commonly prescribed to these patients, results in aggravation of autophagy perturbations and metabolic reprogramming. Thus, iAEC2s provide a patientspecific preclinical platform for modeling the intrinsic epithelial dysfunction associated with the inception of interstitial lung disease.

scholarly journals What is the role of computational models in Cognitive Science? A quantitative and qualitative analysis of the history of the TRACE model of speech segmentation

2019 ◽  
Author(s):  
Manisha Chawla ◽  
Richard Shillcock
Keyword(s):  
Qualitative Analysis ◽  
Cognitive Science ◽  
Computational Models ◽  
Computational Modelling ◽  
Speech Segmentation ◽  
Cognitive Modelling ◽  
Trace Model ◽  
History Of ◽  
Central Paradigm

Implemented computational models are a central paradigm of Cognitive Science. How do cognitive scientists really use such models? We take the example of one of the most successful and influential cognitive models, TRACE (McClelland & Elman, 1986), and we map its impact on the field in terms of published, electronically available documents that cite the original TRACE paper over a period of 25 years since its publication. We draw conclusions about the general status of computational cognitive modelling and make critical suggestions regarding the nature of abstraction in computational modelling.

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