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 >

Shahan Yamin Siddiqui^1,2, Iftikhar Naseer³, Muhammad Adnan Khan⁴, Muhammad Faheem Mushtaq⁵, Rizwan Ali Naqvi^6,*, Dildar Hussain⁷, Amir Haider⁸

CMC-Computers, Materials & Continua, Vol.67, No.1, pp. 1033-1049, 2021, DOI:10.32604/cmc.2021.013952 - 12 January 2021

Abstract Breast cancer is the most frequently detected tumor that eventually could result in a significant increase in female mortality globally. According to clinical statistics, one woman out of eight is under the threat of breast cancer. Lifestyle and inheritance patterns may be a reason behind its spread among women. However, some preventive measures, such as tests and periodic clinical checks can mitigate its risk thereby, improving its survival chances substantially. Early diagnosis and initial stage treatment can help increase the survival rate. For that purpose, pathologists can gather support from nondestructive and efficient computer-aided diagnosis… More >

Mavra Mehmood¹, Ember Ayub¹, Fahad Ahmad^1,6,*, Madallah Alruwaili², Ziyad A. Alrowaili³, Saad Alanazi², Mamoona Humayun², Muhammad Rizwan¹, Shahid Naseem⁴, Tahir Alyas⁵

CMC-Computers, Materials & Continua, Vol.67, No.1, pp. 641-657, 2021, DOI:10.32604/cmc.2021.013774 - 12 January 2021

Abstract Clinical image processing plays a significant role in healthcare systems and is currently a widely used methodology. In carcinogenic diseases, time is crucial; thus, an image’s accurate analysis can help treat disease at an early stage. Ductal carcinoma in situ (DCIS) and lobular carcinoma in situ (LCIS) are common types of malignancies that affect both women and men. The number of cases of DCIS and LCIS has increased every year since 2002, while it still takes a considerable amount of time to recommend a controlling technique. Image processing is a powerful technique to analyze preprocessed… More >

Diyar Qader Zeebaree¹, Adnan Mohsin Abdulazeez², Dilovan Asaad Zebari^3,*, Habibollah Haron⁴, Haza Nuzly Abdull Hamed⁴

CMC-Computers, Materials & Continua, Vol.66, No.3, pp. 3363-3382, 2021, DOI:10.32604/cmc.2021.013314 - 28 December 2020

Abstract Collective improvement in the acceptable or desirable accuracy level of breast cancer image-related pattern recognition using various schemes remains challenging. Despite the combination of multiple schemes to achieve superior ultrasound image pattern recognition by reducing the speckle noise, an enhanced technique is not achieved. The purpose of this study is to introduce a features-based fusion scheme based on enhancement uniform-Local Binary Pattern (LBP) and filtered noise reduction. To surmount the above limitations and achieve the aim of the study, a new descriptor that enhances the LBP features based on the new threshold has been proposed.… More >

Ihsan Jasim Hussein¹, M. A. Burhanuddin², Mazin Abed Mohammed^3,*, Mohamed Elhoseny⁴, Begonya Garcia-Zapirain⁵, Marwah Suliman Maashi⁶, Mashael S. Maashi⁷

CMC-Computers, Materials & Continua, Vol.66, No.3, pp. 3161-3182, 2021, DOI:10.32604/cmc.2021.012691 - 28 December 2020

Abstract One of the most complex tasks for computer-aided diagnosis (Intelligent decision support system) is the segmentation of lesions. Thus, this study proposes a new fully automated method for the segmentation of ovarian and breast ultrasound images. The main contributions of this research is the development of a novel Viola–James model capable of segmenting the ultrasound images of breast and ovarian cancer cases. In addition, proposed an approach that can efficiently generate region-of-interest (ROI) and new features that can be used in characterizing lesion boundaries. This study uses two databases in training and testing the proposed… More >

Jian Zhao^*1, Xia Zhang^†1, Xiaonan Cui^*, Di Wang^*, Bin Zhang^*‡, Liying Ban^*

Oncology Research, Vol.28, No.1, pp. 103-106, 2020, DOI:10.3727/096504019X15605078731913

Abstract Hand–foot syndrome (HFS) is the main side effect of capecitabine and affects the compression zones of the body such as the palms and soles, causing numbness, paresthesias, skin swelling or erythema, scaling, chapping, hard nodule-like blisters, and severe pain. Loss of fingerprints is also observed in some cases. Severe cases of HFS are common in the review of clinical reports. However, loss of fingerprints has not received significant attention. Two reported cases of loss of fingerprints in The New England Journal of Medicine and The BMJ have drawn attention to this side effect of capecitabine. Loss of More >