Prophylactic effects of triptolide on colon cancer development in azoxymethane/dextran sulfate sodiuminduced mouse model

Purpose: To investigate effects of triptolide on colon cancer cell growth and its capacity to prevent tumor development in an azoxymethane (AOM)/dextran sulfate sodium (DSS) mouse model of colon cancer.Methods: HCT116 cell viability and migration potential were assessed. Control and AOM/DSS-treated mice (with and without triptolide) were analyzed for tumor development. The animals were divided into five groups (n = 5). Normal control group was given saline, animals in the untreated control group received AOM and DSS while animals in the treatment groups received 10, 50 and 100 mg/kg doses of triptolide intraperitoneally alternately for 2 months after AOM and DSS injection.Results: Triptolide enhanced nuclear material condensation, significantly (p < 0.05) increased the levels of cleaved poly (ADP-ribose) polymerase, reduced the levels of pro-caspase-3 and pro-caspase-8 in HCT116 cells. Triptolide also significantly (p < 0.05) decreased the expression of pIκBα, activated peroxisome proliferator-activated receptor γ, and markedly reduced the activity of both metalloproteinase-2 and metalloproteinase-9. Treatment of AOM/DSS mice with triptolide significantly reduced adenocarcinoma multiplicity compared to the control group.Conclusion: Triptolide administration suppresses growth of HCT116 cells and colon cancer development in mice by inhibiting inflammatory responses. Therefore, triptolide has potentials to be developed for colon cancer therapy.Keywords: Cell viability, Condensation, Metalloproteinase, Apoptotic, Triptolide, Adenocarcinoma, Colon cance

Is I-Voting I-Llegal?

The Voting Rights Act was passed to prevent racial discrimination in all voting booths. Does the existence of a racial digital divide make Internet elections for public office merely a computer geek\u27s pipe dream? Or can i-voting withstand scrutiny under the current state of the law? This i-Brief will consider the current state of the law, and whether disproportionate benefits will be enough to stop this extension of technology dead in its tracks

A Peculiar Flaring Episode of Cygnus X-1

Recent monitoring of Cyg X-1 with {\em RXTE} revealed a period of intense flaring, which started in October of 2000 and lasted until March of 2001. The source exhibited some quite unusual behaviors during this period. The soft X-ray flux of the source went up and down three times on a timescale of about one month, as discovered by the ASM aboard RXTE, before finally returning to the normal level (of the hard state). The observed spectral and temporal X-ray properties of Cyg X-1 are mostly intermediate between the canonical hard and soft states. This is known previously for strong X-ray flares, however, we show that the source did enter a period that resembles, in many ways, a sustained soft state during the last of the three flares. We make detailed comparisons between this flare and the 1996 state transition, in terms of the observed X-ray properties, such as flux--hardness correlation, X-ray spectrum, and power density spectrum. We point out the similarities and differences, and discuss possible implications of the results on our understanding of the phenomena of flares and state transitions associated with Cyg X-1.Comment: 4 pages, 3 figures, accepted for publication in ApJ Letter

(2E,5E)-2,5-Bis(3,4,5-trimethoxy­benzyl­idene)cyclo­penta­none

The title compound, C25H28O7, was prepared by the base-catalysed reaction of 3,4,5-trimethoxy­benzaldehyde with cyclo­penta­none. The mol­ecule has crystallographic twofold rotation symmetry and adopts an E-configuration about the central olefinic bonds. The two benzene rings and the central cyclo­penta­none ring are almost coplanar [dihedral angle = 4.7 (2)°]

GNE: A Deep Learning Framework for Gene Network Inference by Aggregating Biological Information

BACKGROUND: The topological landscape of gene interaction networks provides a rich source of information for inferring functional patterns of genes or proteins. However, it is still a challenging task to aggregate heterogeneous biological information such as gene expression and gene interactions to achieve more accurate inference for prediction and discovery of new gene interactions. In particular, how to generate a unified vector representation to integrate diverse input data is a key challenge addressed here. RESULTS: We propose a scalable and robust deep learning framework to learn embedded representations to unify known gene interactions and gene expression for gene interaction predictions. These low- dimensional embeddings derive deeper insights into the structure of rapidly accumulating and diverse gene interaction networks and greatly simplify downstream modeling. We compare the predictive power of our deep embeddings to the strong baselines. The results suggest that our deep embeddings achieve significantly more accurate predictions. Moreover, a set of novel gene interaction predictions are validated by up-to-date literature-based database entries. CONCLUSION: The proposed model demonstrates the importance of integrating heterogeneous information about genes for gene network inference. GNE is freely available under the GNU General Public License and can be downloaded from GitHub ( https://github.com/kckishan/GNE )