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  • Open Access


    Numerical Treatments for Crossover Cancer Model of Hybrid Variable-Order Fractional Derivatives

    Nasser Sweilam1, Seham Al-Mekhlafi2,*, Aya Ahmed3, Ahoud Alsheri4, Emad Abo-Eldahab3

    CMES-Computer Modeling in Engineering & Sciences, Vol.140, No.2, pp. 1619-1645, 2024, DOI:10.32604/cmes.2024.047896

    Abstract In this paper, two crossover hybrid variable-order derivatives of the cancer model are developed. Grünwald-Letnikov approximation is used to approximate the hybrid fractional and variable-order fractional operators. The existence, uniqueness, and stability of the proposed model are discussed. Adams Bashfourth’s fifth-step method with a hybrid variable-order fractional operator is developed to study the proposed models. Comparative studies with generalized fifth-order Runge-Kutta method are given. Numerical examples and comparative studies to verify the applicability of the used methods and to demonstrate the simplicity of these approximations are presented. We have showcased the efficiency of the proposed method and garnered robust empirical… More >

  • Open Access


    DNA Methylation Variation Is Identified in Monozygotic Twins Discordant for Congenital Heart Diseases

    Shuliang Xia1,2,3,#, Huikang Tao2,#, Shixin Su4, Xinxin Chen2, Li Ma2, Jianru Li5, Bei Gao6, Xumei Liu5, Lei Pi7, Jinqing Feng4, Fengxiang Li2, Jia Li4,*, Zhiwei Zhang1,3,*

    Congenital Heart Disease, Vol.19, No.2, pp. 247-256, 2024, DOI:10.32604/chd.2024.052583

    Abstract Aims: Multiple genes and environmental factors are known to be involved in congenital heart disease (CHD), but epigenetic variation has received little attention. Monozygotic (MZ) twins with CHD provide a unique model for exploring this phenomenon. In order to investigate the potential role of Deoxyribonucleic Acid (DNA) methylation in CHD pathogenesis, the present study examined DNA methylation variation in MZ twins discordant for CHD, especially ventricular septal defect (VSD). Methods and Results: Using genome-wide DNA methylation profiles, we identified 4004 differentially methylated regions (DMRs) in 18 MZ twin pairs discordant for CHD, and 2826 genes were identified. Gene Ontology (GO)… More > Graphic Abstract

    DNA Methylation Variation Is Identified in Monozygotic Twins Discordant for Congenital Heart Diseases

  • Open Access


    Model Agnostic Meta-Learning (MAML)-Based Ensemble Model for Accurate Detection of Wheat Diseases Using Vision Transformer and Graph Neural Networks

    Yasir Maqsood1, Syed Muhammad Usman1,*, Musaed Alhussein2, Khursheed Aurangzeb2,*, Shehzad Khalid3, Muhammad Zubair4

    CMC-Computers, Materials & Continua, Vol.79, No.2, pp. 2795-2811, 2024, DOI:10.32604/cmc.2024.049410

    Abstract Wheat is a critical crop, extensively consumed worldwide, and its production enhancement is essential to meet escalating demand. The presence of diseases like stem rust, leaf rust, yellow rust, and tan spot significantly diminishes wheat yield, making the early and precise identification of these diseases vital for effective disease management. With advancements in deep learning algorithms, researchers have proposed many methods for the automated detection of disease pathogens; however, accurately detecting multiple disease pathogens simultaneously remains a challenge. This challenge arises due to the scarcity of RGB images for multiple diseases, class imbalance in existing public datasets, and the difficulty… More >

  • Open Access


    Aggravation of Cancer, Heart Diseases and Diabetes Subsequent to COVID-19 Lockdown via Mathematical Modeling

    Fatma Nese Efil1, Sania Qureshi1,2,3, Nezihal Gokbulut1,4, Kamyar Hosseini1,3, Evren Hincal1,4,*, Amanullah Soomro2

    CMES-Computer Modeling in Engineering & Sciences, Vol.140, No.1, pp. 485-512, 2024, DOI:10.32604/cmes.2024.047907

    Abstract The global population has been and will continue to be severely impacted by the COVID-19 epidemic. The primary objective of this research is to demonstrate the future impact of COVID-19 on those who suffer from other fatal conditions such as cancer, heart disease, and diabetes. Here, using ordinary differential equations (ODEs), two mathematical models are developed to explain the association between COVID-19 and cancer and between COVID-19 and diabetes and heart disease. After that, we highlight the stability assessments that can be applied to these models. Sensitivity analysis is used to examine how changes in certain factors impact different aspects… More >

  • Open Access


    Therapeutic and regenerative potential of different sources of mesenchymal stem cells for cardiovascular diseases


    BIOCELL, Vol.48, No.4, pp. 559-569, 2024, DOI:10.32604/biocell.2024.048056

    Abstract Mesenchymal stem cells (MSCs) are ideal candidates for treating many cardiovascular diseases. MSCs can modify the internal cardiac microenvironment to facilitate their immunomodulatory and differentiation abilities, which are essential to restore heart function. MSCs can be easily isolated from different sources, including bone marrow, adipose tissues, umbilical cord, and dental pulp. MSCs from various sources differ in their regenerative and therapeutic abilities for cardiovascular disorders. In this review, we will summarize the therapeutic potential of each MSC source for heart diseases and highlight the possible molecular mechanisms of each source to restore cardiac function. More >

  • Open Access


    Bronchoalveolar lavage fluid metagenomic next-generation sequencing assay for identifying pathogens in lung cancer patients


    BIOCELL, Vol.48, No.4, pp. 623-637, 2024, DOI:10.32604/biocell.2024.030420

    Abstract Background: For patients with lung cancer, timely identification of new lung lesions as infectious or non-infectious, and accurate identification of pathogens is very important in improving OS of patients. As a new auxiliary examination, metagenomic next-generation sequencing (mNGS) is believed to be more accurate in diagnosing infectious diseases in patients without underlying diseases, compared with conventional microbial tests (CMTs). We designed this study to find out whether mNGS has better performance in distinguishing infectious and non-infectious diseases in lung cancer patients using bronchoalveolar lavage fluid (BALF). Materials and Methods: This study was a real-world retrospective review based on electronic medical… More >

  • Open Access


    Olive Leaf Disease Detection via Wavelet Transform and Feature Fusion of Pre-Trained Deep Learning Models

    Mahmood A. Mahmood1,2,*, Khalaf Alsalem1

    CMC-Computers, Materials & Continua, Vol.78, No.3, pp. 3431-3448, 2024, DOI:10.32604/cmc.2024.047604

    Abstract Olive trees are susceptible to a variety of diseases that can cause significant crop damage and economic losses. Early detection of these diseases is essential for effective management. We propose a novel transformed wavelet, feature-fused, pre-trained deep learning model for detecting olive leaf diseases. The proposed model combines wavelet transforms with pre-trained deep-learning models to extract discriminative features from olive leaf images. The model has four main phases: preprocessing using data augmentation, three-level wavelet transformation, learning using pre-trained deep learning models, and a fused deep learning model. In the preprocessing phase, the image dataset is augmented using techniques such as… More >

  • Open Access


    Multimodality Medical Image Fusion Based on Pixel Significance with Edge-Preserving Processing for Clinical Applications

    Bhawna Goyal1, Ayush Dogra2, Dawa Chyophel Lepcha1, Rajesh Singh3, Hemant Sharma4, Ahmed Alkhayyat5, Manob Jyoti Saikia6,*

    CMC-Computers, Materials & Continua, Vol.78, No.3, pp. 4317-4342, 2024, DOI:10.32604/cmc.2024.047256

    Abstract Multimodal medical image fusion has attained immense popularity in recent years due to its robust technology for clinical diagnosis. It fuses multiple images into a single image to improve the quality of images by retaining significant information and aiding diagnostic practitioners in diagnosing and treating many diseases. However, recent image fusion techniques have encountered several challenges, including fusion artifacts, algorithm complexity, and high computing costs. To solve these problems, this study presents a novel medical image fusion strategy by combining the benefits of pixel significance with edge-preserving processing to achieve the best fusion performance. First, the method employs a cross-bilateral… More >

  • Open Access


    Femoral Access with Ultrasound-Guided Puncture and Z-Stitch Hemostasis for Adults with Congenital Heart Diseases Undergoing Electrophysiological Procedures

    Fu Guan1,*, Matthias Gass2, Florian Berger2, Heiko Schneider1, Firat Duru1,3, Thomas Wolber1,3,*

    Congenital Heart Disease, Vol.19, No.1, pp. 85-92, 2024, DOI:10.32604/chd.2024.047266

    Abstract Aims: Although the application of ultrasound-guided vascular puncture and Z-stitch hemostasis to manage femoral access has been widely utilized, there is limited data on this combined application in adult congenital heart disease (ACHD) patients undergoing electrophysiological (EP) procedures. We sought to evaluate the safety and efficacy of ultrasound-guided puncture and postprocedural Z-stitch hemostasis for ACHD patients undergoing EP procedures. Methods and Results: The population of ACHD patients undergoing transfemoral EP procedures at the University of Zurich Heart Center between January 2019 and December 2022 was observed and analyzed. During the study period, femoral access (left/right, arterial/venous) was performed under real-time… More >

  • Open Access


    Privacy-Preserving Federated Deep Learning Diagnostic Method for Multi-Stage Diseases

    Jinbo Yang1, Hai Huang1, Lailai Yin2, Jiaxing Qu3, Wanjuan Xie4,*

    CMES-Computer Modeling in Engineering & Sciences, Vol.139, No.3, pp. 3085-3099, 2024, DOI:10.32604/cmes.2023.045417

    Abstract Diagnosing multi-stage diseases typically requires doctors to consider multiple data sources, including clinical symptoms, physical signs, biochemical test results, imaging findings, pathological examination data, and even genetic data. When applying machine learning modeling to predict and diagnose multi-stage diseases, several challenges need to be addressed. Firstly, the model needs to handle multimodal data, as the data used by doctors for diagnosis includes image data, natural language data, and structured data. Secondly, privacy of patients’ data needs to be protected, as these data contain the most sensitive and private information. Lastly, considering the practicality of the model, the computational requirements should… More >

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