Faizah A. AlMalki¹, Aziza M. Hassan^2,*, Zeinab M. Klaab¹, Soliman Abdulla³, Antonio Pizzi⁴

Journal of Renewable Materials, Vol.9, No.12, pp. 2077-2092, 2021, DOI:10.32604/jrm.2021.016173 - 22 June 2021

Abstract Recently, natural substances in the form of nanoparticles are increasingly being used in different field, particularly in medicines to enhance their beneficial effects in treatment and prevention. Cancer cells of the breast (MCF-7) have been chosen to be examined and treated in vitro with conventional drug Tamoxifen (Tam) and tannin nanoparticles extract (NP99) individually or in combination. MTT reagent has been applied to assess the cell viability and propagation percentage, DNA fragmentation and mRNA relative expression of apoptotic genes to study the cell death pathway. The results showed that Tam and tannin NP99 triggered cytotoxic activity More >

Keiichi Kontani^1,*, Kana Kuraishi¹, Shin-ichiro Hashimoto¹, Shoko Norimura², Nozomi Hashimoto¹, Masahiro Ohtani³, Naomi Fujiwara-Honjo⁴, Manabu Date⁵, Koji Teramoto⁶, Hiroyasu Yokomise¹

Oncologie, Vol.23, No.2, pp. 229-239, 2021, DOI:10.32604/Oncologie.2021.016277 - 22 June 2021

Abstract The prognosis of patients with human epidermal growth factor receptor-2 (HER2)-overexpressed metastatic breast cancer (MBC) has improved drastically following the development of anti-HER2 therapies. We question what factors are involved in the improved outcome by the treatment. One hundred and two MBC patients who received chemotherapy were classified into groups according to breast cancer subtype: luminal/HER2-negative (n = 50), HER2 (n = 26), and triple-negative subtypes (n = 26). Clinicopathologic features and clinical outcomes of the groups were compared. Disease-free intervals in the triple-negative group were significantly shorter than those in the other two groups.… More >

Asma Baccouche^1,*, Begonya Garcia-Zapirain², Cristian Castillo Olea², Adel S. Elmaghraby¹

CMC-Computers, Materials & Continua, Vol.69, No.1, pp. 1407-1425, 2021, DOI:10.32604/cmc.2021.018461 - 04 June 2021

Abstract With recent breakthroughs in artificial intelligence, the use of deep learning models achieved remarkable advances in computer vision, ecommerce, cybersecurity, and healthcare. Particularly, numerous applications provided efficient solutions to assist radiologists for medical imaging analysis. For instance, automatic lesion detection and classification in mammograms is still considered a crucial task that requires more accurate diagnosis and precise analysis of abnormal lesions. In this paper, we propose an end-to-end system, which is based on You-Only-Look-Once (YOLO) model, to simultaneously localize and classify suspicious breast lesions from entire mammograms. The proposed system first preprocesses the raw images,… More >

Ornela Bardhi^1,2,*, Begonya Garcia Zapirain¹

CMC-Computers, Materials & Continua, Vol.68, No.2, pp. 1595-1613, 2021, DOI:10.32604/cmc.2021.015326 - 13 April 2021

Abstract Breast cancer (BCa) and prostate cancer (PCa) are the two most common types of cancer. Various factors play a role in these cancers, and discovering the most important ones might help patients live longer, better lives. This study aims to determine the variables that most affect patient survivability, and how the use of different machine learning algorithms can assist in such predictions. The AURIA database was used, which contains electronic healthcare records (EHRs) of 20,006 individual patients diagnosed with either breast or prostate cancer in a particular region in Finland. In total, there were 178… More >

Shaoguan Huang¹, Rong Zhang², Lizhen Liu^3,*

Oncologie, Vol.23, No.1, pp. 159-171, 2021, DOI:10.32604/Oncologie.2021.012489 - 30 March 2021

Abstract Breast cancer is one of malignant severe diseases that cause cancer death in women. Although research about the pathogenesis and studies about treatment mechanisms in breast cancer have become clear focuses, we have no clear conclusion yet. Therefore, this research is based on a modular approach to explore key factors and molecular mechanisms that affect breast cancer metastasis. First of all, it is necessary to download breast cancer-related data on the GEO database, and we analyzed the difference between primary tumors and metastatic lesions to obtain differential gene expression profiles. On this basis, a series… More >

DILIXIATI JINSIHAN, DAN LI, MINGSHUAI ZHANG, JINCHUN FENG, QIAN ZHAO^*

BIOCELL, Vol.45, No.3, pp. 671-684, 2021, DOI:10.32604/biocell.2021.012519 - 03 March 2021

Abstract Hypoxia affects the advancement, metastasis, and metabolism of breast cancer (BC). The circular RNA ribonuclease P RNA component H1 (circRPPH1) (has_circ_0000515) is implicated in tumor progression. Nevertheless, the regulatory mechanism related to circRPPH1 in hypoxia-mediated triple-negative breast cancer (TNBC) progression is indistinct. The expression levels of circRPPH1, miR-1296-5p, tripartite motif-containing 14 (TRIM14) mRNA in tissue samples and cells were examined through quantitative real-time polymerase chain reaction (qRT-PCR). Cell viability, migration, and invasion were determined with Cell Counting Kit-8 (CCK-8) or transwell assays. The levels of glucose consumption and lactate production were assessed via the Glucose… More >

YANFEI LI¹, LULU DAI², KE CAI², YINGKUI SONG², XIQING LIU^3,*

BIOCELL, Vol.45, No.3, pp. 685-694, 2021, DOI:10.32604/biocell.2021.013612 - 03 March 2021

Abstract RPA3 (Replication Protein A3) (14 kD) is a part of the canonical heterotrimeric replication protein A complex (RPA/RP-A). This study aimed to explore the functional role of RPA3 and the mechanisms of its dysregulation in breast cancer. Data from the Cancer Genome Atlas (TCGA)-breast cancer patients and GSE75688 were utilized for gene expression and survival analysis. Breast cancer cell lines MDA-MB-231 and SK-BR-3 were used for in-vitro cell studies. Clonogenic assay and immunofluorescent staining of γ-H2AX were performed to examine radiation-induced cytotoxicity. Systemic correlation analysis was performed to identify potential transcription factors (TFs) regulating RPA3… More >

Charles Arputham^1,*, Krishnaraj Nagappan², Lenin Babu Russeliah³, AdalineSuji Russeliah⁴

Intelligent Automation & Soft Computing, Vol.27, No.3, pp. 747-759, 2021, DOI:10.32604/iasc.2021.012077 - 01 March 2021

Abstract Breast cancer detection is a crucial topic in the healthcare sector. Breast cancer is a major reason for the increased mortality rate in recent years among women, specifically in developed and underdeveloped countries around the world. The incidence rate is less in India than in developed countries, but awareness must be increased. This paper focuses on an efficient deep learning-based diagnosis and classification technique to detect breast cancer from mammograms. The model includes preprocessing, segmentation, feature extraction, and classification. At the initial level, Laplacian filtering is applied to identify the portions of edges in mammogram… More >

Heyam H. Al-Baity^*, Nourah Al-Mutlaq

CMC-Computers, Materials & Continua, Vol.67, No.3, pp. 3089-3106, 2021, DOI:10.32604/cmc.2021.015291 - 01 March 2021

Abstract Machine-learning algorithms have been widely used in breast cancer diagnosis to help pathologists and physicians in the decision-making process. However, the high dimensionality of genetic data makes the classification process a challenging task. In this paper, we propose a new optimized wrapper gene selection method that is based on a nature-inspired algorithm (simulated annealing (SA)), which will help select the most informative genes for breast cancer prediction. These optimal genes will then be used to train the classifier to improve its accuracy and efficiency. Three supervised machine-learning algorithms, namely, the support vector machine, the decision… More >

Ishu Anand¹, Himani Negi¹, Deepika Kumar¹, Mamta Mittal², Tai-hoon Kim^3,*, Sudipta Roy⁴

CMC-Computers, Materials & Continua, Vol.67, No.3, pp. 3107-3127, 2021, DOI:10.32604/cmc.2021.014229 - 01 March 2021

Abstract Breast cancer positions as the most well-known threat and the main source of malignant growth-related morbidity and mortality throughout the world. It is apical of all new cancer incidences analyzed among females. Two features substantially influence the classification accuracy of malignancy and benignity in automated cancer diagnostics. These are the precision of tumor segmentation and appropriateness of extracted attributes required for the diagnosis. In this research, the authors have proposed a ResU-Net (Residual U-Network) model for breast tumor segmentation. The proposed methodology renders augmented, and precise identification of tumor regions and produces accurate breast tumor… More >