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  • Open Access

    ARTICLE

    Exosomal microRNA let-7c-5p enhances cell malignant characteristics by inhibiting TAGLN in oral cancer

    YI LI1, TIANYI WANG1, HAORAN DING1, SHIYONG ZHUANG1, XIAOBO DAI1, BING YAN1,2,*

    Oncology Research, Vol.32, No.10, pp. 1623-1635, 2024, DOI:10.32604/or.2024.048191

    Abstract Background: Oral cancer, a malignancy that is prevalent worldwide, is often diagnosed at an advanced stage. MicroRNAs (miRNAs) in circulating exosomes have emerged as promising cancer biomarkers. The role of miRNA let-7c-5p in oral cancer remains underexplored, and its potential involvement in tumorigenesis warrants comprehensive investigation. Methods: Serum samples from 30 patients with oral cancer and 20 healthy controls were used to isolate exosomes and quantify their RNA content. Isolation of the exosomes was confirmed through transmission electron microscopy. Quantitative PCR was used to assess the miRNA profiles. The effects of let-7c-5p and TAGLN overexpression… More >

  • Open Access

    ARTICLE

    Artificial Intelligence Prediction of One-Part Geopolymer Compressive Strength for Sustainable Concrete

    Mohamed Abdel-Mongy1, Mudassir Iqbal2, M. Farag3, Ahmed. M. Yosri1,*, Fahad Alsharari1, Saif Eldeen A. S. Yousef4

    CMES-Computer Modeling in Engineering & Sciences, Vol.141, No.1, pp. 525-543, 2024, DOI:10.32604/cmes.2024.052505

    Abstract Alkali-activated materials/geopolymer (AAMs), due to their low carbon emission content, have been the focus of recent studies on ecological concrete. In terms of performance, fly ash and slag are preferred materials for precursors for developing a one-part geopolymer. However, determining the optimum content of the input parameters to obtain adequate performance is quite challenging and scarcely reported. Therefore, in this study, machine learning methods such as artificial neural networks (ANN) and gene expression programming (GEP) models were developed using MATLAB and GeneXprotools, respectively, for the prediction of compressive strength under variable input materials and content… More >

  • Open Access

    ARTICLE

    Microarray Gene Expression Classification: An Efficient Feature Selection Using Hybrid Swarm Intelligence Algorithm

    Punam Gulande*, R. N. Awale

    Computer Systems Science and Engineering, Vol.48, No.4, pp. 937-952, 2024, DOI:10.32604/csse.2024.046123

    Abstract The study of gene expression has emerged as a vital tool for cancer diagnosis and prognosis, particularly with the advent of microarray technology that enables the measurement of thousands of genes in a single sample. While this wealth of data offers invaluable insights for disease management, the high dimensionality poses a challenge for multiclass classification. In this context, selecting relevant features becomes essential to enhance classification model performance. Swarm Intelligence algorithms have proven effective in addressing this challenge, owing to their ability to navigate intricate, non-linear feature-class relationships. This paper introduces a novel hybrid swarm More >

  • Open Access

    ARTICLE

    Enhancing Cancer Classification through a Hybrid Bio-Inspired Evolutionary Algorithm for Biomarker Gene Selection

    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… More >

  • Open Access

    ARTICLE

    Selenium Differentially Regulates Flavonoid Accumulation and Antioxidant Capacities in Sprouts of Twenty Diverse Mungbean ( (L.) Wilczek) Genotypes

    Fenglan Zhao1, Jizhi Jin1, Meng Yang1, Franklin Eduardo Melo Santiago2, Jianping Xue1, Li Xu3,*, Yongbo Duan1,*

    Phyton-International Journal of Experimental Botany, Vol.93, No.3, pp. 611-625, 2024, DOI:10.32604/phyton.2024.048295

    Abstract

    Seed germination with selenium (Se) is promising for producing Se-biofortified foods. Mungbean (Vigna radiata (L.) Wilczek) sprout is freshly eaten as a salad dressed with sauce, making it superior for Se biofortification. Since the Se safety range for the human body is extremely narrow, it is imperative to evaluate the genotypic responses of mungbean sprouts to Se. This study evaluated the Se enrichment capacity and interaction with flavonoids and antioxidant systems in sprouts of 20 mungbean germplasms. Selenium treatment was done by immersing mungbean seeds in 20 μM sodium selenite solution for 8 h. Afterward, the

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  • Open Access

    ARTICLE

    A Novel Deep Learning-Based Model for Classification of Wheat Gene Expression

    Amr Ismail1, Walid Hamdy1,2, Aya M. Al-Zoghby3, Wael A. Awad3, Ahmed Ismail Ebada3, Yunyoung Nam4, Byeong-Gwon Kang4,*, Mohamed Abouhawwash5,6

    Computer Systems Science and Engineering, Vol.48, No.2, pp. 273-285, 2024, DOI:10.32604/csse.2023.038192

    Abstract Deep learning (DL) plays a critical role in processing and converting data into knowledge and decisions. DL technologies have been applied in a variety of applications, including image, video, and genome sequence analysis. In deep learning the most widely utilized architecture is Convolutional Neural Networks (CNN) are taught discriminatory traits in a supervised environment. In comparison to other classic neural networks, CNN makes use of a limited number of artificial neurons, therefore it is ideal for the recognition and processing of wheat gene sequences. Wheat is an essential crop of cereals for people around the… More >

  • Open Access

    REVIEW

    New perspectives on biology, disease progression, and therapy response of head and neck cancer gained from single cell RNA sequencing and spatial transcriptomics

    GERWIN HELLER1,*, THORSTEN FUEREDER1, ALEXANDER MICHAEL GRANDITS1, ROTRAUD WIESER1,2,*

    Oncology Research, Vol.32, No.1, pp. 1-17, 2024, DOI:10.32604/or.2023.044774

    Abstract Head and neck squamous cell carcinoma (HNSCC) is one of the most frequent cancers worldwide. The main risk factors are consumption of tobacco products and alcohol, as well as infection with human papilloma virus. Approved therapeutic options comprise surgery, radiation, chemotherapy, targeted therapy through epidermal growth factor receptor inhibition, and immunotherapy, but outcome has remained unsatisfactory due to recurrence rates of ~50% and the frequent occurrence of second primaries. The availability of the human genome sequence at the beginning of the millennium heralded the omics era, in which rapid technological progress has advanced our knowledge… More >

  • Open Access

    ARTICLE

    Application of Plant Growth-Promoting Bacteria as an Eco-Friendly Strategy for Mitigating the Harmful Effects of Abiotic Stress on Plants

    Ahmed Hassan Abdou1,*, Omar Abdullah Alkhateeb2, Hossam Eldin Hamed Mansour3, Hesham S. Ghazzawy4, Muayad Saud Albadrani5, Nadi Awad Al-harbi6, Wasimah B. Al-Shammari7, Khaled Abdelaal8,*

    Phyton-International Journal of Experimental Botany, Vol.92, No.12, pp. 3305-3321, 2023, DOI:10.32604/phyton.2023.044780

    Abstract Plant growth-promoting bacteria (PGPB) play an important role in improving agricultural production under several abiotic stress factors. PGPB can be used to increase crop growth and development through hormonal balance and increase nutrient uptake. The positive effect of PGPB may be due to its pivotal role in morphophysiological and biochemical characteristics like leaf number, leaf area, and stem length. Furthermore, relative water content, chlorophyll content, carotenoids, antioxidant enzymes, and plant hormones were improved with PGPB treatment. Crop yield and yield components were also increased with PGPB treatment in numerous crops. The anatomical structure of plant… More >

  • Open Access

    REVIEW

    Regulatory role of NFAT1 signaling in articular chondrocyte activities and osteoarthritis pathogenesis

    MINGCAI ZHANG, TANNER CAMPBELL, SPENCER FALCON, JINXI WANG*

    BIOCELL, Vol.47, No.10, pp. 2125-2132, 2023, DOI:10.32604/biocell.2023.030161

    Abstract Osteoarthritis (OA), the most common form of joint disease, is characterized clinically by joint pain, stiffness, and deformity. OA is now considered a whole joint disease; however, the breakdown of the articular cartilage remains the major hallmark of the disease. Current treatments targeting OA symptoms have a limited impact on impeding or reversing the OA progression. Understanding the molecular and cellular mechanisms underlying OA development is a critical barrier to progress in OA therapy. Recent studies by the current authors’ group and others have revealed that the nuclear factor of activated T cell 1 (NFAT1), More > Graphic Abstract

    Regulatory role of NFAT1 signaling in articular chondrocyte activities and osteoarthritis pathogenesis

  • Open Access

    REVIEW

    A Survey on Acute Leukemia Expression Data Classification Using Ensembles

    Abdel Nasser H. Zaied1, Ehab Rushdy2, Mona Gamal3,*

    Computer Systems Science and Engineering, Vol.47, No.2, pp. 1349-1364, 2023, DOI:10.32604/csse.2023.033596

    Abstract Acute leukemia is an aggressive disease that has high mortality rates worldwide. The error rate can be as high as 40% when classifying acute leukemia into its subtypes. So, there is an urgent need to support hematologists during the classification process. More than two decades ago, researchers used microarray gene expression data to classify cancer and adopted acute leukemia as a test case. The high classification accuracy they achieved confirmed that it is possible to classify cancer subtypes using microarray gene expression data. Ensemble machine learning is an effective method that combines individual classifiers to… More >

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