YARA ALZGHOUL, HALA J. BANI ISSA, AHMAD K. SANAJLEH, TAQWA ALABDUH, FATIMAH RABABAH, MAHA AL-SHDAIFAT, EJLAL ABU-EL-RUB^*, FATIMAH ALMAHASNEH, RAMADA R. KHASAWNEH, AYMAN ALZU’BI, HUTHAIFA MAGABLEH

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 >

Lingling Chen^1,2,*, Tong Liu¹, Zhuo Gong¹, Ding Wang¹

Computer Systems Science and Engineering, Vol.48, No.2, pp. 321-339, 2024, DOI:10.32604/csse.2023.037865

Abstract Human pose estimation is a basic and critical task in the field of computer vision that involves determining the position (or spatial coordinates) of the joints of the human body in a given image or video. It is widely used in motion analysis, medical evaluation, and behavior monitoring. In this paper, the authors propose a method for multi-view human pose estimation. Two image sensors were placed orthogonally with respect to each other to capture the pose of the subject as they moved, and this yielded accurate and comprehensive results of three-dimensional (3D) motion reconstruction that helped capture their multi-directional poses.… More >

Muhammad Shoaib Arif^1,2,*, Kamaleldin Abodayeh¹, Yasir Nawaz²

CMES-Computer Modeling in Engineering & Sciences, Vol.139, No.3, pp. 3417-3434, 2024, DOI:10.32604/cmes.2024.046944

Abstract This study focuses on the urgent requirement for improved accuracy in disease modeling by introducing a new computational framework called the Hybrid SIR-Fuzzy Model. By integrating the traditional Susceptible-Infectious-Recovered (SIR) model with fuzzy logic, our method effectively addresses the complex nature of epidemic dynamics by accurately accounting for uncertainties and imprecisions in both data and model parameters. The main aim of this research is to provide a model for disease transmission using fuzzy theory, which can successfully address uncertainty in mathematical modeling. Our main emphasis is on the imprecise transmission rate parameter, utilizing a three-part description of its membership level.… More >

Chein-Shan Liu¹, Chung-Lun Kuo¹, Chih-Wen Chang^2,*

CMES-Computer Modeling in Engineering & Sciences, Vol.139, No.3, pp. 3189-3208, 2024, DOI:10.32604/cmes.2023.046002

Abstract To solve the Laplacian problems, we adopt a meshless method with the multiquadric radial basis function (MQ-RBF) as a basis whose center is distributed inside a circle with a fictitious radius. A maximal projection technique is developed to identify the optimal shape factor and fictitious radius by minimizing a merit function. A sample function is interpolated by the MQ-RBF to provide a trial coefficient vector to compute the merit function. We can quickly determine the optimal values of the parameters within a preferred rage using the golden section search algorithm. The novel method provides the optimal values of parameters and,… More >

Sangeeta Yadav¹, Preeti Gulia^1,*, Nasib Singh Gill^1,*, Piyush Kumar Shukla², Arfat Ahmad Khan³, Sultan Alharby⁴, Ahmed Alhussen⁴, Mohd Anul Haq⁵

CMC-Computers, Materials & Continua, Vol.78, No.1, pp. 1253-1274, 2024, DOI:10.32604/cmc.2023.042869

Abstract Inpatient falls from beds in hospitals are a common problem. Such falls may result in severe injuries. This problem can be addressed by continuous monitoring of patients using cameras. Recent advancements in deep learning-based video analytics have made this task of fall detection more effective and efficient. Along with fall detection, monitoring of different activities of the patients is also of significant concern to assess the improvement in their health. High computation-intensive models are required to monitor every action of the patient precisely. This requirement limits the applicability of such networks. Hence, to keep the model lightweight, the already designed… More >

Chaoji Liu¹, Xingqiao Liu^1,*, Chong Chen², Kang Zhou¹

CMES-Computer Modeling in Engineering & Sciences, Vol.139, No.1, pp. 405-440, 2024, DOI:10.32604/cmes.2023.031040

Abstract Pose-invariant facial expression recognition (FER) is an active but challenging research topic in computer vision. Especially with the involvement of diverse observation angles, FER makes the training parameter models inconsistent from one view to another. This study develops a deep global multiple-scale and local patches attention (GMS-LPA) dual-branch network for pose-invariant FER to weaken the influence of pose variation and self-occlusion on recognition accuracy. In this research, the designed GMS-LPA network contains four main parts, i.e., the feature extraction module, the global multiple-scale (GMS) module, the local patches attention (LPA) module, and the model-level fusion model. The feature extraction module… More >

Zheng Shi, Wanru Song^*, Junhao Shan, Feng Liu

CMC-Computers, Materials & Continua, Vol.77, No.3, pp. 3467-3488, 2023, DOI:10.32604/cmc.2023.045849

Abstract Visible-infrared Cross-modality Person Re-identification (VI-ReID) is a critical technology in smart public facilities such as cities, campuses and libraries. It aims to match pedestrians in visible light and infrared images for video surveillance, which poses a challenge in exploring cross-modal shared information accurately and efficiently. Therefore, multi-granularity feature learning methods have been applied in VI-ReID to extract potential multi-granularity semantic information related to pedestrian body structure attributes. However, existing research mainly uses traditional dual-stream fusion networks and overlooks the core of cross-modal learning networks, the fusion module. This paper introduces a novel network called the Augmented Deep Multi-Granularity Pose-Aware Feature… More >

Pengxin Li¹, Rong Wang^1,2,*, Wenjing Zhang¹, Yinuo Liu¹, Chenyue Xu¹

CMES-Computer Modeling in Engineering & Sciences, Vol.138, No.3, pp. 2239-2255, 2024, DOI:10.32604/cmes.2023.030677

Abstract Human pose estimation aims to localize the body joints from image or video data. With the development of deep learning, pose estimation has become a hot research topic in the field of computer vision. In recent years, human pose estimation has achieved great success in multiple fields such as animation and sports. However, to obtain accurate positioning results, existing methods may suffer from large model sizes, a high number of parameters, and increased complexity, leading to high computing costs. In this paper, we propose a new lightweight feature encoder to construct a high-resolution network that reduces the number of parameters… More >

JING LI^1,2, WANWAN FAN⁴, LILI HAO¹, YONGSHENG LI⁵, GUOCHENG YU¹, WEI SUN⁶, XIANQIONG LUO^2,*, JINGXIANG ZHONG^1,3,*

BIOCELL, Vol.47, No.11, pp. 2485-2494, 2023, DOI:10.32604/biocell.2023.044177

Abstract Objective: This study aimed to investigate the potential of human umbilical cord mesenchymal stem cell (hucMSC)-derived exosomes (hucMSC-Exos) in inhibiting hypoxia-induced cell hyper proliferation and overexpression of vascular endothelial growth factor A (VEGF-A) in immature human fetal retinal microvascular endothelial cells (hfRMECs). Methods: Exosomes were isolated from hucMSCs using cryogenic ultracentrifugation and characterized through various techniques, including transmission electron microscopy, nanoparticle tracking analysis, bicinchoninic acid assays, and western blotting. The hfRMECs were identified using von Willebrand factor (vWF) co-staining and divided into four groups: a control group cultured under normoxic condition, a hypoxic model group, a hypoxic group treated with… More > Graphic Abstract

CHENYANG CHEN^1,*, JUAN WEN¹, WEI HUANG¹, JIANG LI^2,*

BIOCELL, Vol.47, No.11, pp. 2423-2433, 2023, DOI:10.32604/biocell.2023.044459

Abstract Background: Pulmonary arterial hypertension (PAH) is a chronic and progressive disease that is strongly associated with dysregulation of glucose metabolism. Alterations in nuclear receptor subfamily 4 group A member 1 (NR4A1) activity alter the outcome of PAH. This study aimed to investigate the effects of NR4A1 on glycolysis in PAH and its underlying mechanisms. Methods: This study included twenty healthy volunteers and twenty-three PAH patients, and plasma samples were collected from the participants. To mimic the conditions of PAH in vitro, a hypoxia-induced model of pulmonary artery smooth muscle cell (PASMC) model was established. The proliferation of PASMCs was assessed… More > Graphic Abstract