Yuan-Yan Tang | PubMed Health

PubMed2006

[Regulatory effect of curcumin on p300 and HDAC1 in B-NHL cells].

The purpose of this study was to investigate the effect of curcumin on proliferation of B-NHL Raji cell line and explore the relationship between this effect and regulatory expression of p300 and HDAC1 transcription. The in vitro cultured Raji cells were treated with curcumin at various concentrations (6.25-50 micromol/L) and at different time points (0, 6, 12, 24 and 48 hours), the inhibitory ratio of cell growth was measured by MTT assay, the cell apoptosis rate was detected by flow cytometry with Annexin V-FITC/PI double staining, the changes of p300 and HDAC1 mRNA expression and protein level in Raji cells were determined by RT-PCR and Western blot. The results showed that the curcumin could inhibit Raji cell proliferation in significant time-and concentration-dependent manners, IC50 at 24 hours was 25 micromol/L; the curcumin could induce apoptosis of Raji cells in concentration-dependent manner, apoptosis rate was 14.38%-61.18%. The curcumin significantly inhibited activity and expression of p300 and HDAC1. At IC50 concentration, expression of p300 and HDAC1 mRNA and protein level decreased with time-dependent manner, difference between tested and control groups was significant (P < 0.05). It is concluded that the curcumin can inhibit proliferation of B-NHL Raji cells and promote apoptosis of those cells. Curcumin can inhibit the activity and expression of the transcriptional co-activator p300 and HDAC1, which may be involved in its pharmacological mechanisms on B lymphoma cells.

Antineoplastic Agents↗

PubMed2004

An improved LDA approach.

Linear discrimination analysis (LDA) technique is an important and well-developed area of image recognition and to date many linear discrimination methods have been put forward. Despite these efforts, there persist in LDA at least three areas of weakness. The first weakness is that not all the discrimination vectors that are obtained are useful in pattern classification. Second, it remains computationally expensive to make the discrimination vectors completely satisfy statistical uncorrelation. The third weakness is that it is necessary to select the appropriate principal components. In this paper, we propose to improve discrimination technique in these three areas and to that end present an improved LDA (ILDA) approach which synthesizes these improvements. Experimental results on different image databases demonstrate that our improvements on LDA are efficient, and that ILDA outperforms other state-of-the-art linear discrimination methods.

Algorithms↗