LING MA^1,#, ZIRUI JIANG^1,#, XIAO HOU¹, YUTING XU¹, ZIYUN CHEN¹, SIYI ZHANG¹, HANXUE LI¹, SHAOJIE MA¹, GENG ZHANG², XIUJUN WANG^1,*, JING JI^1,*

BIOCELL, Vol.48, No.5, pp. 847-860, 2024, DOI:10.32604/biocell.2024.050868

Abstract Background: Regulatory proteins involved in human cellular division and proliferation, cyclin-dependent kinases 4 and 6 (CDK4/6) are overexpressed in numerous cancers, including triple-negative breast cancer (TNBC). TNBC is a common pathological subtype of breast cancer that is prone to recurrence and metastasis, and has a single treatment method. As one of the CDK4/6 inhibitors, abemaciclib can effectively inhibit the growth of breast tumors. In this study, we synthesized LA-D-B1, a derivative of Abemaciclib, and investigated its anti-tumor effects in breast cancer. Methods: Cellular viability was assessed using the 3-(4,5-dimethylthiazol-2-yl)-2,5-diphenyltetrazolium bromide (MTT) assay. Cell cloning and migration abilities were determined by… More >

VIRAAJ VISHNU PRASAD, JENNIFER SALLY SAMSON, VENKATACHALAM DEEPA PARVATHI^*

BIOCELL, Vol.48, No.5, pp. 693-706, 2024, DOI:10.32604/biocell.2024.048776

Abstract Physical activity and exercise have several beneficial roles in enhancing both physiological and psychological well-being of an individual. In addition to aiding the regulation of aerobic and anaerobic metabolism, exercise can stimulate the synthesis of exerkine hormones in the circulatory system. Among several exerkines that have been investigated for their therapeutic potential, Brain-derived neurotrophic factor (BDNF) is considered the most promising candidate, especially in the management of neurodegenerative diseases. Owing to the ability of physical activity to enhance BDNF synthesis, several experimental studies conducted so far have validated this hypothesis and produced satisfactory results at the pre-clinical level. This review… More > Graphic Abstract

Jingcheng Zhang¹, Xingjian Zhou¹, Dong Shen¹, Qimeng Yu¹, Lin Yuan^2,*, Yingying Dong³

Phyton-International Journal of Experimental Botany, Vol.93, No.4, pp. 745-762, 2024, DOI:10.32604/phyton.2024.049734

Abstract As an important rice disease, rice bacterial leaf blight (RBLB, caused by the bacterium Xanthomonas oryzae pv. oryzae), has become widespread in east China in recent years. Significant losses in rice yield occurred as a result of the disease’s epidemic, making it imperative to monitor RBLB at a large scale. With the development of remote sensing technology, the broad-band sensors equipped with red-edge channels over multiple spatial resolutions offer numerous available data for large-scale monitoring of rice diseases. However, RBLB is characterized by rapid dispersal under suitable conditions, making it difficult to track the disease at a regional scale with… More >

Deming Lei, Surui Duan, Mingbo Li^*, Jing Wang

CMC-Computers, Materials & Continua, Vol.79, No.1, pp. 47-63, 2024, DOI:10.32604/cmc.2024.049481

Abstract Bottleneck stage and reentrance often exist in real-life manufacturing processes; however, the previous research rarely addresses these two processing conditions in a scheduling problem. In this study, a reentrant hybrid flow shop scheduling problem (RHFSP) with a bottleneck stage is considered, and an elite-class teaching-learning-based optimization (ETLBO) algorithm is proposed to minimize maximum completion time. To produce high-quality solutions, teachers are divided into formal ones and substitute ones, and multiple classes are formed. The teacher phase is composed of teacher competition and teacher teaching. The learner phase is replaced with a reinforcement search of the elite class. Adaptive adjustment on… More >

Yan Dong^1,2, Kang Zhao¹, Liang Gao¹, Hao Li^1,*

CMC-Computers, Materials & Continua, Vol.79, No.1, pp. 1-18, 2024, DOI:10.32604/cmc.2024.048870

Abstract With the continuous advancement in topology optimization and additive manufacturing (AM) technology, the capability to fabricate functionally graded materials and intricate cellular structures with spatially varying microstructures has grown significantly. However, a critical challenge is encountered in the design of these structures–the absence of robust interface connections between adjacent microstructures, potentially resulting in diminished efficiency or macroscopic failure. A Hybrid Level Set Method (HLSM) is proposed, specifically designed to enhance connectivity among non-uniform microstructures, contributing to the design of functionally graded cellular structures. The HLSM introduces a pioneering algorithm for effectively blending heterogeneous microstructure interfaces. Initially, an interpolation algorithm is… More >

Hala AlShamlan^*, Halah AlMazrua^*

CMC-Computers, Materials & Continua, Vol.79, No.1, pp. 675-694, 2024, DOI:10.32604/cmc.2024.048146

Abstract In this study, our aim is to address the problem of gene selection by proposing a hybrid bio-inspired evolutionary algorithm that combines Grey Wolf Optimization (GWO) with Harris Hawks Optimization (HHO) for feature selection. The motivation for utilizing GWO and HHO stems from their bio-inspired nature and their demonstrated success in optimization problems. We aim to leverage the strengths of these algorithms to enhance the effectiveness of feature selection in microarray-based cancer classification. We selected leave-one-out cross-validation (LOOCV) to evaluate the performance of both two widely used classifiers, k-nearest neighbors (KNN) and support vector machine (SVM), on high-dimensional cancer microarray… More >

Sapiah Sakri¹, Shakila Basheer¹, Zuhaira Muhammad Zain¹, Nurul Halimatul Asmak Ismail^2,*, Dua’ Abdellatef Nassar¹, Manal Abdullah Alohali¹, Mais Ayman Alharaki¹

CMC-Computers, Materials & Continua, Vol.79, No.1, pp. 1157-1185, 2024, DOI:10.32604/cmc.2024.048051

Abstract Background: Sepsis, a potentially fatal inflammatory disease triggered by infection, carries significant health implications worldwide. Timely detection is crucial as sepsis can rapidly escalate if left undetected. Recent advancements in deep learning (DL) offer powerful tools to address this challenge. Aim: Thus, this study proposed a hybrid CNNBDLSTM, a combination of a convolutional neural network (CNN) with a bi-directional long short-term memory (BDLSTM) model to predict sepsis onset. Implementing the proposed model provides a robust framework that capitalizes on the complementary strengths of both architectures, resulting in more accurate and timelier predictions. Method: The sepsis prediction method proposed here utilizes… More >

Zezheng Meng, Zefeng Cai, Jie Feng^*, Hanjie Ma, Haixiang Zhang, Shaohua Li

CMC-Computers, Materials & Continua, Vol.79, No.1, pp. 1481-1496, 2024, DOI:10.32604/cmc.2024.048002

Abstract Optical braille recognition methods typically employ existing target detection models or segmentation models for the direct detection and recognition of braille characters in original braille images. However, these methods need improvement in accuracy and generalizability, especially in densely dotted braille image environments. This paper presents a two-stage braille recognition framework. The first stage is a braille dot detection algorithm based on Gaussian diffusion, targeting Gaussian heatmaps generated by the convex dots in braille images. This is applied to the detection of convex dots in double-sided braille, achieving high accuracy in determining the central coordinates of the braille convex dots. The… More >

Sharaf J. Malebary^*

CMC-Computers, Materials & Continua, Vol.79, No.1, pp. 1301-1317, 2024, DOI:10.32604/cmc.2024.047917

Abstract Brain tumors are a pressing public health concern, characterized by their high mortality and morbidity rates. Nevertheless, the manual segmentation of brain tumors remains a laborious and error-prone task, necessitating the development of more precise and efficient methodologies. To address this formidable challenge, we propose an advanced approach for segmenting brain tumor Magnetic Resonance Imaging (MRI) images that harnesses the formidable capabilities of deep learning and convolutional neural networks (CNNs). While CNN-based methods have displayed promise in the realm of brain tumor segmentation, the intricate nature of these tumors, marked by irregular shapes, varying sizes, uneven distribution, and limited available… More >

Syeda Shamaila Zareen^1,*, Guangmin Sun^1,*, Mahwish Kundi², Syed Furqan Qadri³, Salman Qadri⁴

CMC-Computers, Materials & Continua, Vol.79, No.1, pp. 1497-1519, 2024, DOI:10.32604/cmc.2024.047418

Abstract Skin cancer diagnosis is difficult due to lesion presentation variability. Conventional methods struggle to manually extract features and capture lesions spatial and temporal variations. This study introduces a deep learning-based Convolutional and Recurrent Neural Network (CNN-RNN) model with a ResNet-50 architecture which used as the feature extractor to enhance skin cancer classification. Leveraging synergistic spatial feature extraction and temporal sequence learning, the model demonstrates robust performance on a dataset of 9000 skin lesion photos from nine cancer types. Using pre-trained ResNet-50 for spatial data extraction and Long Short-Term Memory (LSTM) for temporal dependencies, the model achieves a high average recognition… More >