A Feature-Based Morphing Methodology for Biological Structures Applied to the Spatial Organization of Cardiomyocytes in the Left Atrium

2011 ◽  
Author(s):  
Alessandro Satriano ◽  
Edward J. Vigmond ◽  
Elena S. Di Martino
Keyword(s):  
Spatial Organization ◽  
Experimental Tests ◽  
Smooth Transition ◽  
Patient Specific ◽  
Clinical Environment ◽  
Individual Values ◽  
Tissue Properties ◽  
Biological Structures ◽  
Critical Issues ◽  
Parameter Values

When complex biological structures are modeled, one of the most critical issues is the assignment of geometrical, mechanical and electrical properties to the meshed surfaces. Properties of interest are commonly obtained from diagnostic imaging, experimental tests or anatomical observation. These parameters are usually lumped into individual values assigned to a specific region after subdividing the structure in sub-regions. This practice simplifies the problem avoiding the cumbersome assignment of parameter values to each element. However, sub-regions may not adequately represent the smooth transition between regions thus resulting in artificial discontinuities. In addition, some parameters, such as for example the organization of cardiomyocytes, which is the objective of our research, may be obtained through destructive tests or through sophisticated methods that can only be performed on a limited number of samples. Or else, data structure obtained for one animal species could be applied on a different species. Furthermore, in a clinical environment the need for fast turnout of patient-specific models would benefit from the assignment of tissue properties in a semi-automatic manner.

A Feature-Based Morphing Methodology for Computationally Modeled Biological Structures Applied to Left Atrial Fiber Directions

2013 ◽  
Vol 135 (3) ◽  
Author(s):  
Alessandro Satriano ◽  
Chiara Bellini ◽  
Edward J. Vigmond ◽  
Elena S. Di Martino
Keyword(s):  
Finite Element ◽  
Left Atrium ◽  
3D Model ◽  
Material Model ◽  
Element Model ◽  
Dynamic Simulations ◽  
Tissue Properties ◽  
Biological Structures ◽  
Feature Based ◽  
Parameter Values

To properly simulate the behavior of biological structures through computer modeling, there exists a need to describe parameters that vary locally. These parameters can be obtained either from literature or from experimental data and they are often assigned to regions in the model as lumped values. Furthermore, parameter values may be obtained on a representative case and may not be available for each specific modeled organ. We describe a semiautomated technique to assign detailed maps of local tissue properties to a computational model of a biological structure. We applied the method to the left atrium of the heart. The orientation of myocytes in the tissue as obtained from histologic analysis was transferred to the 3D model of a porcine left atrium. Finite element method (FEM) dynamic simulations were performed by using an isotropic, neo-Hookean, constitutive model first, then adding an anisotropic, cardiomyocyte oriented, Fung-type component. Results showed higher stresses for the anisotropic material model corresponding to lower stretches in the cardiomyocyte directions. The same methodology can be applied to transfer any map of parameters onto a discretized finite element model.

scholarly journals Systems biology informed neural networks (SBINN) predict response and novel combinations for PD-1 checkpoint blockade

2021 ◽  
Vol 4 (1) ◽  
Author(s):  
Michelle Przedborski ◽  
Munisha Smalley ◽  
Saravanan Thiyagarajan ◽  
Aaron Goldman ◽  
Mohammad Kohandel
Keyword(s):  
Neural Networks ◽  
Systems Biology ◽  
Clinical Data ◽  
Treatment Protocol ◽  
Response Prediction ◽  
Clinical Findings ◽  
Patient Specific ◽  
Patient Response ◽  
Triple Combination Therapy ◽  
Parameter Values

AbstractAnti-PD-1 immunotherapy has recently shown tremendous success for the treatment of several aggressive cancers. However, variability and unpredictability in treatment outcome have been observed, and are thought to be driven by patient-specific biology and interactions of the patient’s immune system with the tumor. Here we develop an integrative systems biology and machine learning approach, built around clinical data, to predict patient response to anti-PD-1 immunotherapy and to improve the response rate. Using this approach, we determine biomarkers of patient response and identify potential mechanisms of drug resistance. We develop systems biology informed neural networks (SBINN) to calculate patient-specific kinetic parameter values and to predict clinical outcome. We show how transfer learning can be leveraged with simulated clinical data to significantly improve the response prediction accuracy of the SBINN. Further, we identify novel drug combinations and optimize the treatment protocol for triple combination therapy consisting of IL-6 inhibition, recombinant IL-12, and anti-PD-1 immunotherapy in order to maximize patient response. We also find unexpected differences in protein expression levels between response phenotypes which complement recent clinical findings. Our approach has the potential to aid in the development of targeted experiments for patient drug screening as well as identify novel therapeutic targets.

scholarly journals Three-dimensional technologies used for patient specific applications in orthopedics

2021 ◽  
Vol 67 (2) ◽  
pp. 77-85
Author(s):  
Flaviu Moldovan ◽  
Tiberiu Bataga
Keyword(s):  
Orthopedic Surgery ◽  
Three Dimensional ◽  
Medical Application ◽  
Surgical Instruments ◽  
Patient Specific ◽  
Innovative Technology ◽  
Clinical Environment ◽  
Clinical Practices ◽  
Content Description ◽  
Almost All

Abstract Background: Three-dimensional (3D) technologies have numerous medical applications and have gained a lot of interest in medical world. After the advent of three-dimensional printing technology, and especially in last decade, orthopedic surgeons began to apply this innovative technology in almost all areas of orthopedic traumatic surgery. Objective: The aim of this paper is to give an overview of 3D technologies current usage in orthopedic surgery for patient specific applications. Methods: Two major databases PubMed and Web of Science were explored for content description and applications of 3D technologies in orthopedic surgery. It was considered papers presenting controlled studies and series of cases that include descriptions of 3D technologies compatible with applications to human medical purposes. Results: First it is presented the available three-dimensional technologies that can be used in orthopedic surgery as well as methods of integration in order to achieve the desired medical application for patient specific orthopedics. Technology starts with medical images acquisition, followed by design, numerical simulation, and printing. Then it is described the state of the art clinical applications of 3D technologies in orthopedics, by selecting the latest reported articles in medical literature. It is focused on preoperative visualization and planning, trauma, injuries, elective orthopedic surgery, guides and customized surgical instrumentation, implants, orthopedic fixators, orthoses and prostheses. Conclusion: The new 3D digital technologies are revolutionizing orthopedic clinical practices. The vast potential of 3D technologies is increasingly used in clinical practice. These technologies provide useful tools for clinical environment: accurate preoperative planning for cases of complex trauma and elective cases, personalized surgical instruments and personalized implants. There is a need to further explore the vast potential of 3D technologies in many other areas of orthopedics and to accommodate healthcare professionals with these technologies, as well as to study their effectiveness compared to conventional methods.

scholarly journals A gene regulatory motif that generates oscillatory or multiway switch outputs

2013 ◽  
Vol 10 (79) ◽  
pp. 20120826 ◽  
Author(s):  
Jasmina Panovska-Griffiths ◽  
Karen M. Page ◽  
James Briscoe
Keyword(s):  
Gene Expression ◽  
Transcription Factors ◽  
Spatial Organization ◽  
Formation Mechanisms ◽  
Regulatory Interactions ◽  
Qualitative Dynamics ◽  
Gene Regulatory ◽  
Oscillatory Mechanisms ◽  
Parameter Values ◽  
Natural Way

The pattern of gene expression in a developing tissue determines the spatial organization of cell type generation. We previously defined regulatory interactions between a set of transcription factors that specify the pattern of gene expression in progenitors of different neuronal subtypes of the vertebrate neural tube. These transcription factors form a circuit that acts as a multistate switch, patterning the tissue in response to a gradient of Sonic Hedgehog. Here, by simplifying aspects of the regulatory interactions, we found that the topology of the circuit allows either switch-like or oscillatory behaviour depending on parameter values. The qualitative dynamics appear to be controlled by a simpler sub-circuit, which we term the AC–DC motif. We argue that its topology provides a natural way to implement a multistate gene expression switch and we show that the circuit is readily extendable to produce more distinct stripes of gene expression. Our analysis also suggests that AC–DC motifs could be deployed in tissues patterned by oscillatory mechanisms, thus blurring the distinction between pattern-formation mechanisms relying on temporal oscillations or graded signals. Furthermore, during evolution, mechanisms of gradient interpretation might have arisen from oscillatory circuits, or vice versa.

scholarly journals Cardiovascular Modeling With Adapted Parametric Inference

2018 ◽  
Vol 62 ◽  
pp. 91-107 ◽  
Author(s):  
Didier Lucor ◽  
Olivier P. Le Maître
Keyword(s):  
Approximation Error ◽  
Surrogate Models ◽  
Patient Specific ◽  
System Response ◽  
Cardiovascular Parameter ◽  
Monte Carlo Algorithms ◽  
Statistical Framework ◽  
Posterior Sampling ◽  
Powerful Approach ◽  
Parameter Values

Computational modeling of the cardiovascular system, promoted by the advance of fluid-structure interaction numerical methods, has made great progress towards the development of patient-specific numerical aids to diagnosis, risk prediction, intervention and clinical treatment. Nevertheless, the reliability of these models is inevitably impacted by rough modeling assumptions. A strong in-tegration of patient-specific data into numerical modeling is therefore needed in order to improve the accuracy of the predictions through the calibration of important physiological parameters. The Bayesian statistical framework to inverse problems is a powerful approach that relies on posterior sampling techniques, such as Markov chain Monte Carlo algorithms. The generation of samples re-quires many evaluations of the cardiovascular parameter-to-observable model. In practice, the use of a full cardiovascular numerical model is prohibitively expensive and a computational strategy based on approximations of the system response, or surrogate models, is needed to perform the data as-similation. As the support of the parameters distribution typically concentrates on a small fraction of the initial prior distribution, a worthy improvement consists in gradually adapting the surrogate model to minimize the approximation error for parameter values corresponding to high posterior den-sity. We introduce a novel numerical pathway to construct a series of polynomial surrogate models, by regression, using samples drawn from a sequence of distributions likely to converge to the posterior distribution. The approach yields substantial gains in efficiency and accuracy over direct prior-based surrogate models, as demonstrated via application to pulse wave velocities identification in a human lower limb arterial network.

scholarly journals Parameter subset reduction for patient-specific modelling of arrhythmogenic cardiomyopathy-related mutation carriers in the CircAdapt model

2020 ◽  
Vol 378 (2173) ◽  
pp. 20190347 ◽  
Author(s):  
Nick van Osta ◽  
Aurore Lyon ◽  
Feddo Kirkels ◽  
Tijmen Koopsen ◽  
Tim van Loon ◽  
...  
Keyword(s):  
Right Ventricular ◽  
Computational Models ◽  
Clinical Decision Making ◽  
Early Stage ◽  
Patient Specific ◽  
Model Parameters ◽  
Arrhythmogenic Cardiomyopathy ◽  
Tissue Properties ◽  
Myocardial Disease ◽  
Deformation Patterns

Arrhythmogenic cardiomyopathy (AC) is an inherited cardiac disease, clinically characterized by life-threatening ventricular arrhythmias and progressive cardiac dysfunction. Patient-specific computational models could help understand the disease progression and may help in clinical decision-making. We propose an inverse modelling approach using the CircAdapt model to estimate patient-specific regional abnormalities in tissue properties in AC subjects. However, the number of parameters ( n  = 110) and their complex interactions make personalized parameter estimation challenging. The goal of this study is to develop a framework for parameter reduction and estimation combining Morris screening, quasi-Monte Carlo (qMC) simulations and particle swarm optimization (PSO). This framework identifies the best subset of tissue properties based on clinical measurements allowing patient-specific identification of right ventricular tissue abnormalities. We applied this framework on 15 AC genotype-positive subjects with varying degrees of myocardial disease. Cohort studies have shown that atypical regional right ventricular (RV) deformation patterns reveal an early-stage AC disease. The CircAdapt model of cardiovascular mechanics and haemodynamics has already demonstrated its ability to capture typical deformation patterns of AC subjects. We, therefore, use clinically measured cardiac deformation patterns to estimate model parameters describing myocardial disease substrates underlying these AC-related RV deformation abnormalities. Morris screening reduced the subset to 48 parameters. qMC and PSO further reduced the subset to a final selection of 16 parameters, including regional tissue contractility, passive stiffness, activation delay and wall reference area. This article is part of the theme issue ‘Uncertainty quantification in cardiac and cardiovascular modelling and simulation’.

NUMERICAL AND EXPERIMENTAL APPROACH FOR THE OPTIMAL DESIGN OF A DUAL PLATE UNDER BALLISTIC IMPACT

2008 ◽  
Vol 22 (09n11) ◽  
pp. 1538-1543
Author(s):  
JEONGHOON YOO ◽  
DONG-TEAK CHUNG ◽  
MYUNG SOO PARK
Keyword(s):  
Numerical Simulation ◽  
Optimization Technique ◽  
Mesh Size ◽  
Experimental Tests ◽  
Ballistic Impact ◽  
Plastic Energy ◽  
Dual Plate ◽  
Element Erosion ◽  
Parameter Values ◽  
The Impact

To predict the behavior of a dual plate composed of 5052-aluminum and 1002-cold rolled steel under ballistic impact, numerical and experimental approaches are attempted. For the accurate numerical simulation of the impact phenomena, the appropriate selection of the key parameter values based on numerical or experimental tests are critical. This study is focused on not only the optimization technique using the numerical simulation but also numerical and experimental procedures to obtain the required parameter values in the simulation. The Johnson-Cook model is used to simulate the mechanical behaviors, and the simplified experimental and the numerical approaches are performed to obtain the material properties of the model. The element erosion scheme for the robust simulation of the ballistic impact problem is applied by adjusting the element erosion criteria of each material based on numerical and experimental results. The adequate mesh size and the aspect ratio are chosen based on parametric studies. Plastic energy is suggested as a response representing the strength of the plate for the optimization under dynamic loading. Optimized thickness of the dual plate is obtained to resist the ballistic impact without penetration as well as to minimize the total weight.

scholarly journals Personalized Urination Activity Management Based on an Intelligent System Using a Wearable Device

2021 ◽  
Vol 25 (3) ◽  
pp. 229-235
Author(s):  
Sung-Jong Eun ◽  
Jun Young Lee ◽  
Han Jung ◽  
Khae-Hawn Kim
Keyword(s):  
Management System ◽  
Short Term Memory ◽  
Intelligent System ◽  
Interval Data ◽  
Memory Systems ◽  
Patient Specific ◽  
Sequential Data ◽  
Clinical Environment ◽  
Motor Patterns ◽  
Activity Management

Purpose: In this study, a urinary management system was established to collect and analyze urinary time and interval data detected through patient-worn smart bands, and the results of the analysis were shown through a web-based visualization to enable monitoring and appropriate feedback for urological patients.Methods: We designed a device that can recognize urination time and spacing based on patient-specific posture and consistent posture changes, and we built a urination patient management system based on this device. The order of body movements during urination was consistent in terms of time characteristics; therefore, sequential data were analyzed and urinary activity was recognized using repeated neural networks and long-term short-term memory systems. The results were implemented as a web (HTML5) service program, enabling visual support for clinical diagnostic assistance.Results: Experiments were conducted to evaluate the performance of the proposed recognition techniques. The effectiveness of smart band monitoring urination was evaluated in 30 men (average age, 28.73 years; range, 26–34 years) without urination problems. The entire experiment lasted a total of 3 days. The final accuracy of the algorithm was calculated based on urological clinical guidelines. This experiment showed a high average accuracy of 95.8%, demonstrating the soundness of the proposed algorithm.Conclusions: This urinary activity management system showed high accuracy and was applied in a clinical environment to characterize patients’ urinary patterns. As wearable devices are developed and generalized, algorithms capable of detecting certain sequential body motor patterns that reflect certain physiological behaviors can be a new methodology for studying human physiological behaviors. It is also thought that these systems will have a significant impact on diagnostic assistance for clinicians.

scholarly journals 3D spatial organization and network-guided comparison of mutation profiles in Glioblastoma reveals similarities across patients

2019 ◽  
Author(s):  
Cansu Dincer ◽  
Tugba Kaya ◽  
Ozlem Keskin ◽  
Attila Gursoy ◽  
Nurcan Tuncbag
Keyword(s):  
Protein Interaction ◽  
Spatial Organization ◽  
Structural Information ◽  
Protein Structures ◽  
Protein Interaction Networks ◽  
Interaction Networks ◽  
Patient Specific ◽  
Tumor Type ◽  
Main Challenge ◽  
The Impact

AbstractMutation profiles of Glioblastoma (GBM) tumors are very heterogeneous which is the main challenge in the interpretation of the effects of mutations in disease. Additionally, the impact of the mutations is not uniform across the proteins and protein-protein interactions. The pathway level representation of the mutations is very limited. In this work, we approach these challenges through a systems level perspective in which we analyze how the mutations in GBM tumors are distributed in protein structures/interfaces and how they are organized at the network level. Our results show that out of 14644 mutations, 4392 have structural information and ~13% of them form spatial patches. Despite a small portion of all mutations, 3D patches partially decrease the heterogeneity across the patients. Hub proteins adapt multiple patches of mutations usually with a very large one and connects mutations in multiple binding sites through the core of the protein. We reconstructed patient specific networks for 290 GBM tumors. Network-guided analysis of mutations completes the interaction components that mutated proteins potentially affect, and groups the patients according to the reconstructed networks. As a result, we found 4 tumor clusters that overcome the heterogeneity in mutation profiles, and reveal predominant pathways in each group. Additionally, the network-based similarity analysis shows that each group of patients carries a set of signature 3D mutation patches. We believe that this study provides another perspective to the analysis of mutation effects and a good training towards the network-guided precision medicine.Author SummaryGlioblastoma (GBM) is the most aggressive brain tumor type with a 15 months of survival on average. The mutation distribution of the GBM patients is very heterogeneous and standard treatments fail to consider the inter-tumor heterogeneity. In our study, we follow a systems level approach that integrates mutation profiles with protein-protein interaction networks. We hypothesized that although the mutations are heterogeneous, the mutations that are close in 3D of the same protein may affect the protein function similarly and this information can be used to get meaningful relation between disease state and mutations. Therefore, we spatially grouped these mutations as “patches” and reconstructed patient specific protein interaction networks. When we cluster these networks based on their pathway similarities, we found four patient groups in GBM. Then, the comparison of groups revealed overrepresentation of 3D patches. This finding can be used for patient specific therapy.

scholarly journals Neurosurgical simulator for training aneurysm microsurgery—a user suitability study involving neurosurgeons and residents

2020 ◽  
Vol 162 (10) ◽  
pp. 2313-2321 ◽  
Author(s):  
Fredrick Johnson Joseph ◽  
Stefan Weber ◽  
Andreas Raabe ◽  
David Bervini
Keyword(s):  
Intracranial Aneurysm ◽  
Surgical Simulation ◽  
Postgraduate Education ◽  
Patient Specific ◽  
Training Methods ◽  
Tissue Properties ◽  
Video Training ◽  
Current State ◽  
Training Standards ◽  
Study Participants

Abstract Background Due to its complexity and to existing treatment alternatives, exposure to intracranial aneurysm microsurgery at the time of neurosurgical residency is limited. The current state of the art includes training methods like assisting in surgeries, operating under supervision, and video training. These approaches are labor-intensive and difficult to fit into a timetable limited by the new work regulations. Existing virtual reality (VR)–based training modules lack patient-specific exercises and haptic properties and are thus inferior to hands-on training sessions and exposure to real surgical procedures. Materials and methods We developed a physical simulator able to reproduce the experience of clipping an intracranial aneurysm based on a patient-specific 3D-printed model of the skull, brain, and arteries. The simulator is made of materials that not only imitate tissue properties including arterial wall patency, thickness, and elasticity but also able to recreate a pulsatile blood flow. A sample group of 25 neurosurgeons and residents (n = 16: early residency with less than 4 years of neurosurgical exposure; n = 9: late residency and board-certified neurosurgeons, 4–15 years of neurosurgical exposure) took part to the study. Participants evaluated the simulator and were asked to answer questions about surgical simulation anatomy, realism, haptics, tactility, and general usage, scored on a 5-point Likert scale. In order to evaluate the feasibility of a future validation study on the role of the simulator in neurosurgical postgraduate training, an expert neurosurgeon assessed participants’ clipping performance and a comparison between groups was done. Results The proposed simulator is reliable and potentially useful for training neurosurgical residents and board-certified neurosurgeons. A large majority of participants (84%) found it a better alternative than conventional neurosurgical training methods. Conclusion The integration of a new surgical simulator including blood circulation and pulsatility should be considered as part of the future armamentarium of postgraduate education aimed to ensure high training standards for current and future generations of neurosurgeons involved in intracranial aneurysm surgery.

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