JING HUI^1,2, NANA SUN³, YONG LIU⁴, CHUNBO YU^1,2, YONG KE⁴, YONG CAO⁴, ANXIAO YU⁴, QINGHONG KONG^1,2,*, YUN LIU^1,2,4,*

BIOCELL, Vol.48, No.5, pp. 803-815, 2024, DOI:10.32604/biocell.2024.047122 - 06 May 2024

Abstract Background: Although it has been established that the human Solute Carrier Family 22 (SLC22) functions as a cationic transporter, influencing cellular biological metabolism by modulating the uptake of various cations, its impact on cancer prognosis remains unclear. Methods: We conducted a comprehensive analysis utilizing data from The Cancer Genome Atlas (TCGA) and other databases to assess the prognostic value and functional implications across various tumors. Silence of SLC22A1 RNA in glioma U251 cells was performed to access the impact of SLC22A1 on lower-grade glioma (LGG) progression. Results: Our findings demonstrated a significant correlation between SLC22A1 expression… More >

Maham Khan¹, Syed Adnan Shah¹, Tenvir Ali², Quratulain², Aymen Khan², Gyu Sang Choi^3,*

CMC-Computers, Materials & Continua, Vol.71, No.3, pp. 5005-5020, 2022, DOI:10.32604/cmc.2022.023007 - 14 January 2022

Abstract Brain tumors are considered as most fatal cancers. To reduce the risk of death, early identification of the disease is required. One of the best available methods to evaluate brain tumors is Magnetic resonance Images (MRI). Brain tumor detection and segmentation are tough as brain tumors may vary in size, shape, and location. That makes manual detection of brain tumors by exploring MRI a tedious job for radiologists and doctors’. So an automated brain tumor detection and segmentation is required. This work suggests a Region-based Convolution Neural Network (RCNN) approach for automated brain tumor identification… More >

Tahir Abbas Khan¹, Sagheer Abbas¹, Allah Ditta², Muhammad Adnan Khan^{3, *}, Hani Alquhayz⁴, Areej Fatima³, Muhammad Farhan Khan⁵

CMC-Computers, Materials & Continua, Vol.65, No.3, pp. 2591-2605, 2020, DOI:10.32604/cmc.2020.011892 - 16 September 2020

Abstract The prediction of human diseases, particularly COVID-19, is an extremely challenging task not only for medical experts but also for the technologists supporting them in diagnosis and treatment. To deal with the prediction and diagnosis of COVID-19, we propose an Internet of Medical Things-based Smart Monitoring Hierarchical Mamdani Fuzzy Inference System (IoMTSM-HMFIS). The proposed system determines the various factors like fever, cough, complete blood count, respiratory rate, Ct-chest, Erythrocyte sedimentation rate and C-reactive protein, family history, and antibody detection (lgG) that are directly involved in COVID-19. The expert system has two input variables in layer… More >