ZHAOYING ZHU^1,#, YANJIA HU^2,#, FENG YE², HAIBO TENG², GUOLIANG YOU¹, YUNHUI ZENG², MENG TIAN², JIANGUO XU², JIN LI², ZHIYONG LIU², HAO LIU^2,*, NIANDONG ZHENG^1,*

Oncology Research, Vol.32, No.7, pp. 1173-1184, 2024, DOI:10.32604/or.2024.042456

Abstract Background: Inhibitor of NF-κB kinase-interacting protein (IKIP) is known to promote proliferation of glioblastoma (GBM) cells, but how it affects migration and invasion by those cells is unclear. Methods: We compared levels of IKIP between glioma tissues and normal brain tissue in clinical samples and public databases. We examined the effects of IKIP overexpression and knockdown on the migration and invasion of GBM using transwell and wound healing assays, and we compared the transcriptomes under these different conditions to identify the molecular mechanisms involved. Results: Based on data from our clinical samples and from public databases, More >

Yin Ni¹, Wu Zeng^2,*, Peng Xia¹, Guang Stanley Yang³, Ruochen Tan⁴

CMC-Computers, Materials & Continua, Vol.79, No.3, pp. 5295-5312, 2024, DOI:10.32604/cmc.2024.049911

Abstract Deepfake-generated fake faces, commonly utilized in identity-related activities such as political propaganda, celebrity impersonations, evidence forgery, and familiar fraud, pose new societal threats. Although current deepfake generators strive for high realism in visual effects, they do not replicate biometric signals indicative of cardiac activity. Addressing this gap, many researchers have developed detection methods focusing on biometric characteristics. These methods utilize classification networks to analyze both temporal and spectral domain features of the remote photoplethysmography (rPPG) signal, resulting in high detection accuracy. However, in the spectral analysis, existing approaches often only consider the power spectral density… More >

Nan Zhu¹, Mahui Si¹, Ning Yang, Yingying Jing, Yong Fu, Xijun Zhao, Zhipeng Lin, Guangshun Yang

Oncology Research, Vol.25, No.6, pp. 1001-1008, 2017, DOI:10.3727/096504016X14796039599926

Abstract Ras-association domain family 6 (RASSF6), a member of the RASSF family, is frequently downregulated in various types of cancer. However, the roles of RASSF6 in human hepatocellular carcinoma (HCC) are still unclear. In this study, we investigated the biological functions and related molecular mechanisms in HCC. Our results found that RASSF6 is expressed in low amounts in HCC tissues and cell lines. Overexpression of RASSF6 obviously inhibited the proliferation, invasion, and EMT process in HCC cells. Furthermore, overexpression of RASFF6 greatly downregulated the protein levels of phosphorylated focal adhesion kinase (FAK), MMP-2, and MMP-9 in More >

Li Zhang^*, Hong-bin Duan^†, Yun-sheng Yang^*

Oncology Research, Vol.25, No.1, pp. 19-27, 2017, DOI:10.3727/096504016X14685034103914

Abstract Rap2B, a member of the Ras family of small GTP-binding proteins, was found to be highly expressed in various human tumors and plays an important role in the development of tumors. However, the function of Rap2B in hepatocellular carcinoma (HCC) remains unclear. Therefore, in this study, we investigated the biological functions of Rap2B in HCC and the potential underlying mechanisms. Our results indicated that Rap2B was highly expressed in HCC tissues and cell lines. Rap2B silencing obviously inhibited the proliferation, migration, and invasion of HCC cells, as well as attenuated xenografted tumor growth in vivo. More >

Chang Li^*, Shuohui Gao^*, Xiaoping Li^†, Chang Li^‡, Lianjun Ma^§

Oncology Research, Vol.26, No.2, pp. 209-217, 2018, DOI:10.3727/096504017X14944585873622

Abstract Colon cancer is one of the most lethal varieties of cancer. Chemotherapy remains as one of the principal treatment approaches for colon cancer. The anticancer activity of procaine (PCA), which is a local anesthetic drug, has been explored in different studies. In our study, we aimed to explore the anticancer effect of PCA on colon cancer and its underlying mechanism. The results showed that PCA significantly inhibited cell viability, increased the percentage of apoptotic cells, and decreased the expression level of RhoA in HCT116 cells in a dose-dependent manner (p<0.05 or p<0.01). Moreover, PCA increased the… More >

Dmitry Gura^1,2, Bo Dong^3,*, Duaa Mehiar⁴, Nidal Al Said⁵

CMC-Computers, Materials & Continua, Vol.79, No.2, pp. 1995-2014, 2024, DOI:10.32604/cmc.2024.048238

Abstract The motivation for this study is that the quality of deep fakes is constantly improving, which leads to the need to develop new methods for their detection. The proposed Customized Convolutional Neural Network method involves extracting structured data from video frames using facial landmark detection, which is then used as input to the CNN. The customized Convolutional Neural Network method is the date augmented-based CNN model to generate ‘fake data’ or ‘fake images’. This study was carried out using Python and its libraries. We used 242 films from the dataset gathered by the Deep Fake… More >

Fangfang Shan^1,2,*, Huifang Sun^1,2, Mengyi Wang^1,2

CMC-Computers, Materials & Continua, Vol.79, No.1, pp. 581-605, 2024, DOI:10.32604/cmc.2024.046202

Abstract As social networks become increasingly complex, contemporary fake news often includes textual descriptions of events accompanied by corresponding images or videos. Fake news in multiple modalities is more likely to create a misleading perception among users. While early research primarily focused on text-based features for fake news detection mechanisms, there has been relatively limited exploration of learning shared representations in multimodal (text and visual) contexts. To address these limitations, this paper introduces a multimodal model for detecting fake news, which relies on similarity reasoning and adversarial networks. The model employs Bidirectional Encoder Representation from Transformers… More >

Lifang Fu¹, Huanxin Peng^2,*, Changjin Ma², Yuhan Liu²

CMC-Computers, Materials & Continua, Vol.78, No.3, pp. 4399-4416, 2024, DOI:10.32604/cmc.2024.047053

Abstract In recent years, how to efficiently and accurately identify multi-model fake news has become more challenging. First, multi-model data provides more evidence but not all are equally important. Secondly, social structure information has proven to be effective in fake news detection and how to combine it while reducing the noise information is critical. Unfortunately, existing approaches fail to handle these problems. This paper proposes a multi-model fake news detection framework based on Tex-modal Dominance and fusing Multiple Multi-model Cues (TD-MMC), which utilizes three valuable multi-model clues: text-model importance, text-image complementary, and text-image inconsistency. TD-MMC is… More >

XINFENG ZHANG^1,2,#, SHUANG LI^2,#, MEIRU SONG^1,2, YUE CHEN³, LIANGZHENG CHANG³, ZHERUI LIU⁴, HONGYUAN DAI³, YUTAO WANG⁴, GANGQI YANG³, YUN JIANG^5,6,*, YINYING LU^1,2,*

Oncology Research, Vol.32, No.4, pp. 679-690, 2024, DOI:10.32604/or.2024.046231

Abstract Liver cancer is a prevalent malignant cancer, ranking third in terms of mortality rate. Metastasis and recurrence primarily contribute to the high mortality rate of liver cancer. Hepatocellular carcinoma (HCC) has low expression of focal adhesion kinase (FAK), which increases the risk of metastasis and recurrence. Nevertheless, the efficacy of FAK phosphorylation inhibitors is currently limited. Thus, investigating the mechanisms by which FAK affects HCC metastasis to develop targeted therapies for FAK may present a novel strategy to inhibit HCC metastasis. This study examined the correlation between FAK expression and the prognosis of HCC. Additionally,… More >

Asma Hassan Alshehri^*

CMC-Computers, Materials & Continua, Vol.78, No.2, pp. 2767-2786, 2024, DOI:10.32604/cmc.2023.046838

Abstract Online review platforms are becoming increasingly popular, encouraging dishonest merchants and service providers to deceive customers by creating fake reviews for their goods or services. Using Sybil accounts, bot farms, and real account purchases, immoral actors demonize rivals and advertise their goods. Most academic and industry efforts have been aimed at detecting fake/fraudulent product or service evaluations for years. The primary hurdle to identifying fraudulent reviews is the lack of a reliable means to distinguish fraudulent reviews from real ones. This paper adopts a semi-supervised machine learning method to detect fake reviews on any website, More >