• Title/Summary/Keyword: NUF2

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Silencing of NUF2 Inhibits Tumor Growth and Induces Apoptosis in Human Hepatocellular Carcinomas

  • Liu, Qiang;Dai, She-Jiao;Li, Hong;Dong, Lei;Peng, Yu-Ping
    • Asian Pacific Journal of Cancer Prevention
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    • v.15 no.20
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    • pp.8623-8629
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    • 2014
  • Background: As an important component of the NDC80 kinetochore complex, NUF2 is essential for kinetochore-microtubule attachment and chromosome segregation. Previous studies also suggested its involvement in development of various kinds of human cancers, however, its expression and functions in human hepatocellular carcinoma (HCC) are still unclear. Materials and Methods: In the present study, we aimed to test the hypothesis that NUF2 is aberrant in human HCCs and associated with cell growth. Results: Our results showed significantly elevated expression of NUF2 in human HCC tissues compared to adjacent normal tissues, and high expression of NUF2 in HCC cell lines. Using lentivirus-mediated silencing of NUF2 in HepG2 human HCC cells, we found that NUF2 depletion markedly suppressed proliferation and colony formation capacity in vitro, and dramatically hampered tumor growth of xenografts in vivo. Moreover, NUF2 silencing could induce cell cycle arrest and trigger cell apoptosis. Additionally, altered levels of cell cycle and apoptosis related proteins including cyclin B1, Cdc25A, Cdc2, Bad and Bax were also observed. Conclusions: In conclusion, these results demonstrate that NUF2 plays a critical role in the regulation of HCC cell proliferation and apoptosis, indicating that NUF2 may serve as a potential molecular target for therapeutic approaches.

Biological activities of ethanol extract from the seawater algae, Chlorella elliposidea C020 (해수클로렐라 [Chlorella elliposidea C020] 에탄올 추출물에 대한 생리 활성)

  • Kim, Hyun-Jin;Kim, In-Hae;Lee, Jae-Hwa
    • KSBB Journal
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    • v.23 no.2
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    • pp.125-130
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    • 2008
  • We investigated the biological activities of ethanol extract from the seawater algae, Chlorella elliposidea C020 such as antibacterial activity, anti-oxidant activity, tyrosinase inhibitory activity and hemolytic activity against human erythrocytes. Extract was obtained from various solvent, methanol, ethanol, acetone and ethanol + acetone (1:1, v/v%), 95% ethanol proved to be best extraction solvents. The contents of ethanol extract were higher in freeze-dried sample than that in frozen-thawing. Antibacterial activities of ethanol extract showed strong inhibitory effect against Bacillus subtilis PM125, Bacillus licheniformis and fish pathogenic bacteria, Vibrio parahaemolyticus KCTC2471 and Edward tarda NUF251. However, this extract didn't worked against antifungal activity against Candida albicans KCTC1940. And, ethanol extract was without hemolytic activity against human erythorocytes. The ethanol extract showed 75% of free radical scavanging effect on 2.0 mg/mL using DPPH method. In tyrosinase inhibition assay of ethanol extract, $IC_{50}$ (Inhibition Concentration) was measured as 10.87 mg/mL. Conclusionally, ethanol extract of Chlorella elliposidea C020 has good candidate for bioactive materials.

Ultrasonic image diagnosis using pattern recognition (패턴인식을 이용한 초음파 화상의 진단)

  • Choi, K.C.;Kim, S.I.;Lee, D.S.
    • Proceedings of the KOSOMBE Conference
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    • v.1991 no.11
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    • pp.57-60
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    • 1991
  • A new approach to texture classification for ultrasound liver diagnosis using run difference matrix was developed. The run difference matrix consists of the gray level difference along with distance. From this run difference matrix, we defined several parameters such as LDE, LDEL, NUF, SMO, SMG, SHP etc. and three vectors namely DOD, DGD and DAD. Each parameter value calculated in fatty cirrhotic, chronic hepatitic and normal liver mage was plotted in two dimensional plane. We compared our results with run length method. There are several advantages of run difference matrix method over the run lengths. 1) It is more sensitive to small difference of gray level distribution. 2) The parameters provide more statistically significant value. Images were classified with the extracted parameters to each diseases using neural networks. In preliminary clinical exprements, this approach showed satisfying results.

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