Artificial Neural Network Methods in Quantum Mechanics

In a previous article we have shown how one can employ Artificial Neural Networks (ANNs) in order to solve non-homogeneous ordinary and partial differential equations. In the present work we consider the solution of eigenvalue problems for differential and integrodifferential operators, using ANNs. We start by considering the Schr\"odinger equation for the Morse potential that has an analytically known solution, to test the accuracy of the method. We then proceed with the Schr\"odinger and the Dirac equations for a muonic atom, as well as with a non-local Schr\"odinger integrodifferential equation that models the $n+\alpha$ system in the framework of the resonating group method. In two dimensions we consider the well studied Henon-Heiles Hamiltonian and in three dimensions the model problem of three coupled anharmonic oscillators. The method in all of the treated cases proved to be highly accurate, robust and efficient. Hence it is a promising tool for tackling problems of higher complexity and dimensionality.Comment: Latex file, 29pages, 11 psfigs, submitted in CP

Multiscale - Patient-Specific Artery and Atherogenesis Models

In this work, we present a platform for the development of multiscale patient-specific artery and atherogenesis models. The platform, called ARTool, integrates technologies of 3-D image reconstruction from various image modalities, blood flow and biological models of mass transfer, plaque characterization, and plaque growth. Patient images are acquired for the development of the 3-D model of the patient specific arteries. Then, blood flow ismodeled within the arterial models for the calculation of the wall shear stress distribution (WSS). WSS is combined with other patient-specific parameters for the development of the plaque progression models. Real-time simulation can be performed for same cases in grid environment. The platform is evaluated using both animal and human data

Enabling heterogeneous data integration and biomedical event prediction through ICT: The test case of cancer reoccurrence

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Three-dimensional reconstruction of coronary arteries and plaque morphology using CT angiography - comparison and registration with IVUS

Background: The aim of this study is to present a new methodology for three-dimensional (3D) reconstruction of coronary arteries and plaque morphology using Computed Tomography Angiography (CTA). Methods: The methodology is summarized in six stages: 1) pre-processing of the initial raw images, 2) rough estimation of the lumen and outer vessel wall borders and approximation of the vessel's centerline, 3) manual adaptation of plaque parameters, 4) accurate extraction of the luminal centerline, 5) detection of the lumen - outer vessel wall borders and calcium plaque region, and 6) finally 3D surface construction. Results: The methodology was compared to the estimations of a recently presented Intravascular Ultrasound (IVUS) plaque characterization method. The correlation coefficients for calcium volume, surface area, length and angle vessel were 0.79, 0.86, 0.95 and 0.88, respectively. Additionally, when comparing the inner and outer vessel wall volumes of the reconstructed arteries produced by IVUS and CTA the observed correlation was 0.87 and 0.83, respectively. Conclusions: The results indicated that the proposed methodology is fast and accurate and thus it is likely in the future to have applications in research and clinical arena

Non-invasive prediction of site-specific coronary atherosclerotic plaque progression using lipidomics, blood flow, and LDL transport modeling

Background: coronary computed tomography angiography (CCTA) is a first line non-invasive imaging modality for detection of coronary atherosclerosis. Computational modeling with lipidomics analysis can be used for prediction of coronary atherosclerotic plaque progression. Methods: 187 patients (480 vessels) with stable coronary artery disease (CAD) undergoing CCTA scan at baseline and after 6.2 +/- 1.4 years were selected from the SMARTool clinical study cohort (Clinicaltrial.gov Identifiers NCT04448691) according to a computed tomography (CT) scan image quality suitable for three-dimensional (3D) reconstruction of coronary arteries and the absence of implanted coronary stents. Clinical and biohumoral data were collected, and plasma lipidomics analysis was performed. Blood flow and low-density lipoprotein (LDL) transport were modeled using patient-specific data to estimate endothelial shear stress (ESS) and LDL accumulation based on a previously developed methodology. Additionally, non-invasive Fractional Flow Reserve (FFR) was calculated (SmartFFR). Plaque progression was defined as significant change of at least two of the morphological metrics: lumen area, plaque area, plaque burden. Results: a multi-parametric predictive model, including traditional risk factors, plasma lipids, 3D imaging parameters, and computational data demonstrated 88% accuracy to predict site-specific plaque progression, outperforming current computational models. Conclusions: Low ESS and LDL accumulation, estimated by computational modeling of CCTA imaging, can be used to predict site-specific progression of coronary atherosclerotic plaques.Cardiolog

Relationship of endothelial shear stress with plaque features with coronary CT angiography and vasodilating capability with PET

Background: Advances in three-dimensional reconstruction techniques and computational fluid dynamics of coronary CT angiography (CCTA) data sets make feasible evaluation of endothelial shear stress (ESS) in the vessel wall.Purpose: To investigate the relationship between CCTA-derived computational fluid dynamics metrics, anatomic and morphologic characteristics of coronary lesions, and their comparative performance in predicting impaired coronary vasodilating capability assessed by using PET myocardial perfusion imaging (MPI).Materials and Methods: In this retrospective study, conducted between October 2019 and September 2020, coronary vessels in patients with stable chest pain and with intermediate probability of coronary artery disease who underwent both CCTA and PET MPI with oxygen 15-labeled water or nitrogen 13 ammonia and quantification of myocardial blood flow were analyzed. CCTA images were used in assessing stenosis severity, lesion-specific total plaque volume (PV), noncalcified PV, calcified PV, and plaque phenotype. PET MPI was used in assessing significant coronary stenosis. The predictive performance of the CCTA-derived parameters was evaluated by using area under the receiver operating characteristic curve (AUC) analysis.Results: There were 92 coronary vessels evaluated in 53 patients (mean age, 65 years +/- 7; 31 men). ESS was higher in lesions with greater than 50% stenosis versus those without significant stenosis (mean, 15.1 Pa +/- 30 vs 4.6 Pa +/- 4 vs 3.3 Pa +/- 3; P = .004). ESS was higher in functionally significant versus nonsignificant lesions (median, 7 Pa [interquartile range, 5-23 Pa] vs 2.6 Pa [interquartile range, 1.8-5 Pa], respectively; P <= .001). Adding ESS to stenosis severity improved prediction (change in AUC, 0.10; 95% CI: 0.04, 0.17; P =.002) for functionally significant lesions.Conclusion: The combination of endothelial shear stress with coronary CT angiography (CCTA) stenosis severity improved prediction of an abnormal PET myocardial perfusion imaging result versus CCTA stenosis severity alone. (C) RSNA, 2021Cardiolog

Patient-specific simulation of coronary artery pressure measurements: An in vivo three-dimensional validation study in humans

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Whole-genome sequencing reveals host factors underlying critical COVID-19

Critical COVID-19 is caused by immune-mediated inflammatory lung injury. Host genetic variation influences the development of illness requiring critical care1 or hospitalization2,3,4 after infection with SARS-CoV-2. The GenOMICC (Genetics of Mortality in Critical Care) study enables the comparison of genomes from individuals who are critically ill with those of population controls to find underlying disease mechanisms. Here we use whole-genome sequencing in 7,491 critically ill individuals compared with 48,400 controls to discover and replicate 23 independent variants that significantly predispose to critical COVID-19. We identify 16 new independent associations, including variants within genes that are involved in interferon signalling (IL10RB and PLSCR1), leucocyte differentiation (BCL11A) and blood-type antigen secretor status (FUT2). Using transcriptome-wide association and colocalization to infer the effect of gene expression on disease severity, we find evidence that implicates multiple genes—including reduced expression of a membrane flippase (ATP11A), and increased expression of a mucin (MUC1)—in critical disease. Mendelian randomization provides evidence in support of causal roles for myeloid cell adhesion molecules (SELE, ICAM5 and CD209) and the coagulation factor F8, all of which are potentially druggable targets. Our results are broadly consistent with a multi-component model of COVID-19 pathophysiology, in which at least two distinct mechanisms can predispose to life-threatening disease: failure to control viral replication; or an enhanced tendency towards pulmonary inflammation and intravascular coagulation. We show that comparison between cases of critical illness and population controls is highly efficient for the detection of therapeutically relevant mechanisms of disease

An Automatic Microcalcification Detection System Based on Hybrid Neural Network Classifier

A hybrid intelligent system is presented for the identification of microcalcification clusters in digital mammograms. The proposed method is based on a three-step procedure: (a) preprocessing and segmentation, (b) regions of interest (ROI) specification, and (c) feature extraction and classification. The reduction of false positive cases is performed using an intelligent system containing two subsystems: a rule-based and a neural network sub-system. In the first step of the classification schema 22 features are automatically computed which refer either to individual microcalcifications or to groups of them. Further reduction in the number of features is achieved through principal component analysis (PCA). The proposed methodology is tested using the Nijmegen and the Mammographic Image Analysis Society (MIAS) mammographic databases. Results are presented as the receiver operating characteristic (ROC) performance and are quantified by the area under the ROC curve (A z ). In particular, the A z value for the Nijmegen dataset is 0.91 and for the MIAS is 0.92. The detection specificity of the two sets is 1.80 and 1.15 false positive clusters per image, at the sensitivity level higher than 0.90, respectively. # 2002 Elsevier Science B.V. All rights reserved