A computational study of VEGF production by patterned retinal epithelial cell colonies as a model for neovascular macular degeneration

Background: The configuration of necrotic areas within the retinal pigmented epithelium is an important element in the progression of age-related macular degeneration (AMD). In the exudative (wet) and non-exudative (dry) forms of the disease, retinal pigment epithelial (RPE) cells respond to adjacent atrophied regions by secreting vascular endothelial growth factor (VEGF) that in turn recruits new blood vessels which lead to a further reduction in retinal function and vision. In vitro models exist for studying VEGF expression in wet AMD (Vargis et al., Biomaterials 35(13):3999–4004, 2014), but are limited in the patterns of necrotic and intact RPE epithelium they can produce and in their ability to finely resolve VEGF expression dynamics. Results: In this work, an in silico hybrid agent-based model was developed and validated using the results of this cell culture model of VEGF expression in AMD. The computational model was used to extend the cell culture investigation to explore the dynamics of VEGF expression in different sized patches of RPE cells and the role of negative feedback in VEGF expression. Results of the simulation and the cell culture studies were in excellent qualitative agreement, and close quantitative agreement. Conclusions: The model indicated that the configuration of necrotic and RPE cell-containing regions have a major impact on VEGF expression dynamics and made precise predictions of VEGF expression dynamics by groups of RPE cells of various sizes and configurations. Coupled with biological studies, this model may give insights into key molecular mechanisms of AMD progression and open routes to more effective treatments

A transfer learning with deep neural network approach for diabetic retinopathy classification

Diabetic retinopathy is an eye disease caused by high blood sugar and pressure which damages the blood vessels in the eye. Diabetic retinopathy is the root cause of more than 1% of the blindness worldwide. Early detection of this disease is crucial as it prevents it from progressing to a more severe level. However, the current machine learning-based approaches for detecting the severity level of diabetic retinopathy are either, i) rely on manually extracting features which makes an approach unpractical, or ii) trained on small dataset thus cannot be generalized. In this study, we propose a transfer learning-based approach for detecting the severity level of the diabetic retinopathy with high accuracy. Our model is a deep learning model based on global average pooling (GAP) technique with various pre-trained convolutional neural net- work (CNN) models. The experimental results of our approach, in which our best model achieved 82.4% quadratic weighted kappa (QWK), corroborate the ability of our model to detect the severity level of diabetic retinopathy efficiently

Using deep learning models for learning semantic text similarity of Arabic questions

Question-answering platforms serve millions of users seeking knowledge and solutions for their daily life problems. However, many knowledge seekers are facing the challenge to find the right answer among similar answered questions and writer’s responding to asked questions feel like they need to repeat answers many times for similar questions. This research aims at tackling the problem of learning the semantic text similarity among different asked questions by using deep learning. Three models are implemented to address the aforementioned problem: i) a supervised-machine learning model using XGBoost trained with pre-defined features, ii) an adapted Siamese-based deep learning recurrent architecture trained with pre-defined features, and iii) a Pre-trained deep bidirectional transformer based on BERT model. Proposed models were evaluated using a reference Arabic dataset from the mawdoo3.com company. Evaluation results show that the BERT-based model outperforms the other two models with an F1=92.99%, whereas the Siamese-based model comes in the second place with F1=89.048%, and finally, the XGBoost as a baseline model achieved the lowest result of F1=86.086%

Lumbar disk 3D modeling from limited number of MRI axial slices

This paper studies the problem of clinical MRI analysis in the field of lumbar intervertebral disk herniation diagnosis. It discusses the possibility of assisting radiologists in reading the patients MRI images by constructing a 3D model for the region of interest using simple computer vision methods. We use axial MRI slices of the lumbar area. The proposed framework works with a very small number of MRI slices and goes through three main stages. Namely, the region of interest extraction and enhancement, inter-slice interpolation, and 3D model construction. We use the Marching Cubes algorithm to construct the 3D model of the the region of interest. The validation of our 3D models is based on a radiologist’s analysis of the models. We tested the proposed 3D model construction on 83 cases and We have a 95% accuracy according to the radiologist evaluation. This study shows that 3D model construction can greatly ease the task of the radiologist which enhances the working experience. This leads eventually to more accurate and easy diagnosis process

Improving radiologists’ and orthopedists’ QoE in diagnosing lumbar disk herniation using 3D modeling

This article studies and analyzes the use of 3D models, built from magnetic resonance imaging (MRI) axial scans of the lumbar intervertebral disk, that are needed for the diagnosis of disk herniation. We study the possibility of assisting radiologists and orthopedists and increasing their quality of experience (QoE) during the diagnosis process. The main aim is to build a 3D model for the desired area of interest and ask the specialists to consider the 3D models in the diagnosis process instead of considering multiple axial MRI scans. We further propose an automated framework to diagnose the lumber disk herniation using the constructed 3D models. We evaluate the effectiveness of increasing the specialists QoE by conducting a questionnaire on 14 specialists with different experiences ranging from residents to consultants. We then evaluate the effectiveness of the automated diagnosis framework by training it with a set of 83 cases and then testing it on an unseen test set. The results show that the the use of 3D models increases doctors QoE and the automated framework gets 90% of diagnosis accuracy

Transfer deep learning approach for detecting coronavirus disease in X-ray images

Currently, the whole world is fighting a very dangerous and infectious disease caused by the novel coronavirus, called COVID-19. The COVID-19 is rapidly spreading around the world due to its high infection rate. Therefore, early discovery of COVID-19 is crucial to better treat the infected person as well as to slow down the spread of this virus. However, the current solution for detecting COVID-19 cases including the PCR test, CT images, epidemiologically history, and clinical symptoms suffer from high false positive. To overcome this problem, we have developed a novel transfer deep learning approach for detecting COVID-19 based on x-ray images. Our approach helps medical staff in determining if a patient is normal, has COVID-19, or other pneumonia. Our approach relies on pre-trained models including Inception-V3, Xception, and MobileNet to perform two tasks: i) binary classification to determine if a person infected with COVID-19 or not and ii) a multi-task classification problem to distinguish normal, COVID-19, and pneumonia cases. Our experimental results on a large dataset show that the F1-score is 100% in the first task and 97.66 in the second task

Computational Modeling to Study Disease Development: Applications to Breast Cancer and an in vitro Model of Macular Degeneration

There have been several techniques developed in recent years to develop computer models of a variety of disease behaviors. Agent-based modeling is a discrete-based modeling approach used agents to represent individual cells that mechanically interact and secrete, consume or react to soluble products. It has become a powerful modeling approach, widely used by computational researchers. In this research, we utilized agent-based modeling to study and explore disease development, particularly in two applications, breast cancer and bioengineering experiments. We further proposed an error-minimization search approach and used it to estimate cellular parameters from multicellular in vitro data. In this dissertation, in the first study, we developed a 2D agent-based model that attempted to emulate the in vivo structure of breast cancer. The model was applied to describe the progression from DCIS into DCI. This model confirms that the interaction between tumor cells and the surrounding stroma in the duct plays a critical role in tumor growth and metastasis. This interaction depends on many mechanical and chemical factors that work with each other to produce tumor invasion of the surrounding tissue. In the second study, an in silico model was developed and applied to understanding the underlying mechanism of vascular-endothelial growth factor (VEGF) auto-regulation in REP and emulate the in vitro experiments as part of bioengineering research. This model may provide a system with robust predictive modeling and visualization that could enable discovery of the molecular mechanisms involved in age-related macular degeneration (AMD) progression and provide routers to the development of effective treatments. In the third and final study, a searching approach was applied to estimate cellular parameters from spatiotemporal data produced from bioengineered multicellular in vitro experiments. We applied a search method to an integrated cellular and multicellular model of retinal pigment epithelial cells to estimate the auto-regulation parameters of VEGF

Study of Stroma and Tumor Growth Interaction in Ductal Carcinoma in Situ Progress: A 3D Agent-Based Modeling Approach

The transition in breast cancer from ductal carcinoma in situ to invasive ductal carcinoma marks a significant drop in patient survival and is one of the leading causes of death in women. Tumor initiation, growth, and metastasis are primarily driven by multiple biochemical and biomechanical interactions among the epithelia, tumor cells, diffusible signals, and stromal components such as fibroblasts, myofibroblasts and extracellular matrix in the ductal microenvironments. Understanding how the interplay among these components drives the dynamics of metastasis and invasion may lead to new therapeutic approaches to breast cancer. We introduced a 3D multicellular agent-based model of DCIS growth and invasion that includes ductal, stromal and tumor cell types acting along with microenvironmental components such as matrix metalloproteinases (MMP), Lysyl oxidase(LOX), nutrients, TGF\u27 and extracellular matrix (ECM) protein assemblies. We are investigating a wide range of parameters and assumptions in our model that lead to change in the model outcomes. The model explicitly determines mechanical tensional and compressive forces within the developing tissue

Bioinformatics in Jordan: Status, challenges, and future directions.

Bioinformatics plays a key role in supporting the life sciences. In this work, we examine bioinformatics in Jordan, beginning with the current status of bioinformatics education and research, then exploring the challenges of advancing bioinformatics, and finally looking to the future for how Jordanian bioinformatics research may develop