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叶黄素的抗癌作用及其研究现状 总被引:10,自引:0,他引:10
叶黄素是自然界广泛存在的类胡萝卜素,可以提高人体的免疫能力,也是一种抗氧化剂,对老年视黄斑退行性变化有很好的预防作用,但更重要的是研究表明,叶黄素对一些类型的癌症具有预防效果。本综述了近年来有关叶黄素预防癌症作用的流行病学调查、分析,以及与癌症关系的实验研究及作用机制等方面的研究进展。 相似文献
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植物多酚的防癌抗癌作用 总被引:7,自引:1,他引:7
植物多酚是植物中广泛存在的一大类多酚化合物的总称,包括多酚(如单宁)、黄酮、酚酸等。体外实验研究表明植物多酚对多种人癌细胞具有增殖抑制作用,抑制动物体内肿瘤生长。其作用机理与抗氧化、调控细胞周期、诱导肿瘤细胞凋亡和分化、影响血管生成和肿瘤细胞信号传导等有关。对植物多酚的防癌抗癌作用,值得进一步深入研究。 相似文献
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生物类黄酮抗癌作用研究进展 总被引:4,自引:1,他引:4
生物类黄酮广泛分布于植物源食物中,具有广泛的生理和药理作用,如具有抗癌和抗突变作用,可以抑制肿瘤的形成,这些功能与其显著的抗氧化、抗自由基作用等密切相关。笔者就类黄酮抗癌作用的研究进展作一综述。 相似文献
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灵芝多糖的抗癌构效关系及其抗癌作用机制 总被引:14,自引:0,他引:14
灵芝多糖是灵芝的主要抗癌活性成分,但灵芝多糖的抗癌构效关系和抗癌机制仍不明了。现有的研究表明,具有抗癌活性的灵芝多糖大多是β-(1→3)-D-葡聚糖。β-葡聚糖中分支度高的有较高的活性,分子量高的也较分子量小的活性大。但新近的一些研究发现,含有其它结构的灵芝多糖和某些小分子量的灵芝多糖也具有免疫调节和抗癌活性。因此,这些灵芝多糖在灵芝抗癌中的作用不可忽视。灵芝多糖的抗癌作用机制目前尚不明了。现已知通过免疫介导作用发挥抗癌作用是灵芝多糖抗癌的主要机制之一。新近的研究发现,膜Ig和TLR-4为灵芝多糖激活机体B细胞的免疫受体,TLR-4也与灵芝多糖激活机体巨噬细胞有关。此外,灵芝多糖抗癌的可能机制还包括活化促分裂原活化蛋白(MAP)激酶,以及抑制肿瘤血管新生等。 相似文献
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眼镜蛇毒及其组分C对大鼠实验性肝癌抗癌作用的病理学研究 总被引:4,自引:0,他引:4
目的 观察眼镜蛇毒及其组分C抗小鼠肝癌的病理学改变。方法 采用不同剂量眼镜蛇毒及其抗癌活性组分C与BALA/c小鼠腹水型肝癌细胞体外孵育,空白对照组用生理盐水与肝癌细胞孵育,然后取孵育液接种于小鼠前肢腋下,接种后第10d坏死,解剖取出瘤结,进行病理组织学研究。 相似文献
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眼镜蛇毒及其组分C对小鼠实验性肝癌抗癌作用的病理学研究 总被引:3,自引:0,他引:3
目的 观察眼镜蛇毒及其组分 C 抗小鼠肝癌的病理学改变。 方法 采用不同剂量眼镜蛇毒及其抗癌活性组分 C 与 B A L A/c 小鼠腹水型肝癌细胞体外孵育, 空白对照组用生理盐水与肝癌细胞孵育, 然后取孵育液接种于小鼠前肢腋下, 接种后第 10d 处死, 解剖取出瘤结, 进行病理组织学研究。 结果 空白对照组瘤结较大, 显微镜下见瘤细胞生长活跃、核大、核仁明显、核分裂多见, 而坏死灶少见, 且瘤细胞向周围浸润扩散; 治疗组瘤体较小, 瘤细胞固缩、核仁不明显、核分裂少见, 而坏死灶多见, 瘤细胞周围有纤维组织增生围绕, 限制了瘤细胞向周围蔓延浸润。 结论 眼镜蛇毒及其组分 C 对小鼠实验性肝癌的体外抗癌作用是明显的, 不同剂量及不同孵育时间的抗癌作用亦显著不同, 其中组分 C 对抗癌作用最强。 相似文献
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John C. Lucchesi 《Genesis (New York, N.Y. : 2000)》1982,3(4):275-282
In Drosophila, the ratio of the number of X chromosomes to sets of other chromosomes initiates a series of events which result in sexual differentiation. In addition, this ratio establishes dosage compensation, a mechanism which equalizes the products of X-linked genes in males and females. The present review discusses possible genetic entities responsible for the interpretation of chromosomal sex and subsequent sex-mediated regulation during development. 相似文献
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Gene expression profiles of 14 common tumors and their counterpart normal tissues were analyzed with machine learning methods
to address the problem of selection of tumor-specific genes and analysis of their differential expressions in tumor tissues.
First, a variation of the Relief algorithm, “RFE_Relief algorithm” was proposed to learn the relations between genes and tissue
types. Then, a support vector machine was employed to find the gene subset with the best classification performance for distinguishing
cancerous tissues and their counterparts. After tissue-specific genes were removed, cross validation experiments were employed
to demonstrate the common deregulated expressions of the selected gene in tumor tissues. The results indicate the existence
of a specific expression fingerprint of these genes that is shared in different tumor tissues, and the hallmarks of the expression
patterns of these genes in cancerous tissues are summarized at the end of this paper. 相似文献
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Two rice cDNA clones (COS6 and COS9) were isolated, corresponding to genes that were highly expressed in roots from seedlings and mature plants. A genomic clone (GOS9) corresponding to cDNA clone COS9 was isolated and the intron/exon structure was determined by comparing the nucleotide sequences of the mRNA and the genomic clone. 5 ends and 3 ends of the mRNA were determined by primer extension and S1-nuclease mapping respectively. The open reading frame present in GOS9 potentially encodes a protein (14kDa) that does not show any significant homology to other proteins in databases. 相似文献
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Arash Kianianmomeni 《Trends in plant science》2014,19(8):488-490
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Energy constraints on the evolution of gene expression 总被引:8,自引:0,他引:8
Wagner A 《Molecular biology and evolution》2005,22(6):1365-1374
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Microarray has become a popular biotechnology in biological and medical research. However, systematic and stochastic variabilities in microarray data are expected and unavoidable, resulting in the problem that the raw measurements have inherent “noise” within microarray experiments. Currently, logarithmic ratios are usually analyzed by various clustering methods directly, which may introduce bias interpretation in identifying groups of genes or samples. In this paper, a statistical method based on mixed model approaches was proposed for microarray data cluster analysis. The underlying rationale of this method is to partition the observed total gene expression level into various variations caused by different factors using an ANOVA model, and to predict the differential effects of GV (gene by variety) interaction using the adjusted unbiased prediction (AUP) method. The predicted GV interaction effects can then be used as the inputs of cluster analysis. We illustrated the application of our method with a gene expression dataset and elucidated the utility of our approach using an external validation. 相似文献