Li Yu^1,2,3, Xiaoyan Zhang^1,2, Yan Feng^1,4, Xinyue Liao^1,4, Tiejun Zhou⁵, Hang Si^1,4, Yun Feng^1,4, Decai Wang^6,*, Yongxian Lai^1,7,*

Oncology Research, Vol.33, No.8, pp. 2037-2053, 2025, DOI:10.32604/or.2025.064276 - 18 July 2025

Abstract Background: Oral squamous cell carcinoma (OSCC) is the most common head and neck malignancy with a low five-year survival rate. ATP-binding cassette subfamily B member 5 (ABCB5) has been linked to tumorigenesis. However, its role in inducing OSCC remains unclear. Methods: Quantitative reverse transcription polymerase chain reaction (qRT-PCR), western blot, and immunocytochemistry (ICC) were performed to examine the level of ABCB5 in OSCC (CAL27 and HSC-3) and human oral keratinocyte (HOK). ABCB5 was knocked down in CAL27 cells using ABCB5-specific small interfering RNA (ABCB5 siRNA), and its contribution to migration, invasion, and epithelial-mesenchymal transition (EMT),… More >

Mohammed Althubyani^1,*, Nidal E. Taha², Khdija O. Taha², Rasmiyah A. Alharb², Sayed Saber^1,3

CMES-Computer Modeling in Engineering & Sciences, Vol.143, No.3, pp. 3491-3521, 2025, DOI:10.32604/cmes.2025.061640 - 30 June 2025

Abstract This study investigates the dynamics of pneumococcal pneumonia using a novel fractal-fractional Susceptible-Carrier-Infected-Recovered model formulated with the Atangana-Baleanu in Caputo (ABC) sense. Unlike traditional epidemiological models that rely on classical or Caputo fractional derivatives, the proposed model incorporates nonlocal memory effects, hereditary properties, and complex transmission dynamics through fractal-fractional calculus. The Atangana-Baleanu operator, with its non-singular Mittag-Leffler kernel, ensures a more realistic representation of disease progression compared to classical integer-order models and singular kernel-based fractional models. The study establishes the existence and uniqueness of the proposed system and conducts a comprehensive stability analysis, including local More >

XUETING HUANG¹, CHAOJIE XU², CHEN LI^3,*, ZHIXIAN PAN^1,*

BIOCELL, Vol.48, No.12, pp. 1805-1813, 2024, DOI:10.32604/biocell.2024.056431 - 30 December 2024

Abstract Background: Hyperuricemia is a metabolic disorder which is characterized by increased serum uric acid levels, which can contribute to serious health issues such as gout, cardiovascular disease, and kidney damage. Epigenetic modifications, for example, DNA methylation, exert a crucial function in gene regulation and have been implicated in various metabolic disorders. The ATP-Binding Cassette Subfamily G Member 2 (ABCG2) gene is involved in uric acid excretion, and its expression can be influenced by methylation of its promoter region. Methods: This study involved the design of three guide RNA (gRNA) sequences targeting specific CpG sites within… More >

Mingyuan Luo¹, Shiyu Tian¹, Xinzhuan Yao², Yue Wan⁴, Zhouzhuoer Chen¹, Zifan Yang⁴, Huagen Hao⁴, Fei Liu³, Hu Tang^1,2,*, Litang Lu^1,2,*

Phyton-International Journal of Experimental Botany, Vol.93, No.8, pp. 2019-2036, 2024, DOI:10.32604/phyton.2024.052938 - 30 August 2024

Abstract The ATP-binding cassette (ABC) transporter is a gene superfamily in plants. ATP-binding cassette subfamily C (ABCC) protein is a multidrug resistance-associated (MRP) transporter. They play various roles in plant growth, development, and secondary metabolite transport. However, there are few studies on ABCC transporters in tea plants. In this study, genome-wide association study (GWAS) analysis of epigallocatechin gallate (EGCG) content in 108 strains of Kingbird revealed that CsABCCs may be involved in EGCG transport. We identified 25 CsABCC genes at the genomic level of the tea plant, their phylogenetic tree, gene structure, targeted miRNA and other bioinformatics… More >

Punam Gulande^*, R. N. Awale

Computer Systems Science and Engineering, Vol.48, No.4, pp. 937-952, 2024, DOI:10.32604/csse.2024.046123 - 17 July 2024

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 >

Yingui Qiu¹, Shuai Huang¹, Danial Jahed Armaghani², Biswajeet Pradhan³, Annan Zhou⁴, Jian Zhou^1,*

CMES-Computer Modeling in Engineering & Sciences, Vol.138, No.3, pp. 2873-2897, 2024, DOI:10.32604/cmes.2023.029938 - 15 December 2023

Abstract As massive underground projects have become popular in dense urban cities, a problem has arisen: which model predicts the best for Tunnel Boring Machine (TBM) performance in these tunneling projects? However, performance level of TBMs in complex geological conditions is still a great challenge for practitioners and researchers. On the other hand, a reliable and accurate prediction of TBM performance is essential to planning an applicable tunnel construction schedule. The performance of TBM is very difficult to estimate due to various geotechnical and geological factors and machine specifications. The previously-proposed intelligent techniques in this field… More >

Shazia Shamas¹, Surya Narayan Panda^1,*, Ishu Sharma^1,*, Kalpna Guleria¹, Aman Singh^2,3,4, Ahmad Ali AlZubi⁵, Mallak Ahmad AlZubi⁶

CMES-Computer Modeling in Engineering & Sciences, Vol.138, No.2, pp. 1051-1075, 2024, DOI:10.32604/cmes.2023.030712 - 17 November 2023

Abstract The distinction and precise identification of tumor nodules are crucial for timely lung cancer diagnosis and planning intervention. This research work addresses the major issues pertaining to the field of medical image processing while focusing on lung cancer Computed Tomography (CT) images. In this context, the paper proposes an improved lung cancer segmentation technique based on the strengths of nature-inspired approaches. The better resolution of CT is exploited to distinguish healthy subjects from those who have lung cancer. In this process, the visual challenges of the K-means are addressed with the integration of four nature-inspired… More >

Tawfeeq Shawly¹, Ahmed A. Alsheikhy^2,*

CMC-Computers, Materials & Continua, Vol.76, No.1, pp. 731-751, 2023, DOI:10.32604/cmc.2023.039433 - 08 June 2023

Abstract More than 500,000 patients are diagnosed with breast cancer annually. Authorities worldwide reported a death rate of 11.6% in 2018. Breast tumors are considered a fatal disease and primarily affect middle-aged women. Various approaches to identify and classify the disease using different technologies, such as deep learning and image segmentation, have been developed. Some of these methods reach 99% accuracy. However, boosting accuracy remains highly important as patients’ lives depend on early diagnosis and specified treatment plans. This paper presents a fully computerized method to detect and categorize tumor masses in the breast using two… More >

Nguyen Hai Minh¹, Nguyen Tu Trung^2,*, Tran Thi Ngan², Tran Manh Tuan²

Computer Systems Science and Engineering, Vol.46, No.3, pp. 3901-3909, 2023, DOI:10.32604/csse.2023.032311 - 03 April 2023

Abstract Fusing satellite (remote sensing) images is an interesting topic in processing satellite images. The result image is achieved through fusing information from spectral and panchromatic images for sharpening. In this paper, a new algorithm based on based the Artificial bee colony (ABC) algorithm with peak signal-to-noise ratio (PSNR) index optimization is proposed to fusing remote sensing images in this paper. Firstly, Wavelet transform is used to split the input images into components over the high and low frequency domains. Then, two fusing rules are used for obtaining the fused images. The first rule is “the More >

Sungjin Lee¹, Ahyoung Lee², Min Hong^3,*

Computer Systems Science and Engineering, Vol.46, No.2, pp. 2543-2554, 2023, DOI:10.32604/csse.2023.037055 - 09 February 2023

Abstract Specific medical data has limitations in that there are not many numbers and it is not standardized. to solve these limitations, it is necessary to study how to efficiently process these limited amounts of data. In this paper, deep learning methods for automatically determining cardiovascular diseases are described, and an effective preprocessing method for CT images that can be applied to improve the performance of deep learning was conducted. The cardiac CT images include several parts of the body such as the heart, lungs, spine, and ribs. The preprocessing step proposed in this paper divided… More >