Home / Advanced Search

  • Title/Keywords

  • Author/Affliations

  • Journal

  • Article Type

  • Start Year

  • End Year

Update SearchingClear
  • Articles
  • Online
Search Results (5)
  • Open Access

    ARTICLE

    MicroRNA-296 Targets Specificity Protein 1 to Suppress Cell Proliferation and Invasion in Cervical Cancer

    Lili Lv*, Xiaodong Wang

    Oncology Research, Vol.26, No.5, pp. 775-783, 2018, DOI:10.3727/096504017X15132494420120

    Abstract Cervical cancer is the third most commonly diagnosed malignancy and the fourth leading cause of cancer-related deaths in women worldwide. MicroRNA-296 (miR-296) is aberrantly expressed in a variety of human cancer types. However, the expression levels, biological roles, and underlying molecular mechanisms of miR-296 in cervical cancer remain unclear. This study aimed to detect miR-296 expression in cervical cancer and evaluate its roles and underlying mechanisms in cervical cancer. This study demonstrated that miR-296 was significantly downregulated in cervical cancer tissues and cell lines. Restoring the expression of miR-296 inhibited the proliferation and invasion of More >

  • Open Access

    ARTICLE

    Classification of Electroencephalogram Signals Using LSTM and SVM Based on Fast Walsh-Hadamard Transform

    Saeed Mohsen1,2,*, Sherif S. M. Ghoneim3, Mohammed S. Alzaidi3, Abdullah Alzahrani3, Ashraf Mohamed Ali Hassan4

    CMC-Computers, Materials & Continua, Vol.75, No.3, pp. 5271-5286, 2023, DOI:10.32604/cmc.2023.038758

    Abstract Classification of electroencephalogram (EEG) signals for humans can be achieved via artificial intelligence (AI) techniques. Especially, the EEG signals associated with seizure epilepsy can be detected to distinguish between epileptic and non-epileptic regions. From this perspective, an automated AI technique with a digital processing method can be used to improve these signals. This paper proposes two classifiers: long short-term memory (LSTM) and support vector machine (SVM) for the classification of seizure and non-seizure EEG signals. These classifiers are applied to a public dataset, namely the University of Bonn, which consists of 2 classes –seizure and… More >

  • Open Access

    ARTICLE

    Reducing Dataset Specificity for Deepfakes Using Ensemble Learning

    Qaiser Abbas1, Turki Alghamdi1, Yazed Alsaawy1, Tahir Alyas2,*, Ali Alzahrani1, Khawar Iqbal Malik3, Saira Bibi4

    CMC-Computers, Materials & Continua, Vol.74, No.2, pp. 4261-4276, 2023, DOI:10.32604/cmc.2023.034482

    Abstract The emergence of deep fake videos in recent years has made image falsification a real danger. A person’s face and emotions are deep-faked in a video or speech and are substituted with a different face or voice employing deep learning to analyze speech or emotional content. Because of how clever these videos are frequently, Manipulation is challenging to spot. Social media are the most frequent and dangerous targets since they are weak outlets that are open to extortion or slander a human. In earlier times, it was not so easy to alter the videos, which… More >

  • Open Access

    RETRACTION

    MicroRNA-296 Targets Specificity Protein 1 to Suppress Cell Proliferation and Invasion in Cervical Cancer [Oncology Research 26(5) (2018) 775–783]

    Lili Lv*, Xiaodong Wang

    Oncology Research, Vol.28, No.7-8, pp. 835-835, 2020

    Abstract This article has no abstract. More >

  • Open Access

    ARTICLE

    PPARγ LBD and its ligand specificity reveal a selection of potential partial agonist: Molecular dynamics based T2D drug discovery initiative

    BIDYUT MALLICK1,#, ASHISH RANJAN SHARMA2,#, MANOJIT BHATTACHARYA3, SANG-SOO LEE1,*, CHIRANJIB CHAKRABORTY4,*

    BIOCELL, Vol.45, No.4, pp. 953-961, 2021, DOI:10.32604/biocell.2021.015530

    Abstract PPARγ is a peroxisome proliferator-activated receptor (PPAR) family protein and is a target for type 2 diabetes (T2D). In this paper, we have performed a molecular docking analysis between ligand molecules (CID9816265, CID11608015, CID20251380, CID20251343, CID20556263, CID624491, CID42609928, and CID86287562) and PPARγ to determine the ligand specificity. It also helps to understand the ligand-binding domain (LBD) activity of PPARγ during the binding of the ligand. Further, a molecular dynamics simulation study was performed to determine the ligand biding stability in the PPARγ LBD. Its ligand specificity informed us about the potentiality of selecting a partial… More >

Displaying 1-10 on page 1 of 5. Per Page