Non-steroidal anti-inflammatory drug-induced apoptosis in gastric cancer cells is blocked by protein kinase C activation through inhibition of c-myc

Apoptosis plays a major role in gastrointestinal epithelial cell turnover, ulcerogenesis and tumorigenesis. We have examined apoptosis induction by non-steroidal anti-inflammatory drugs (NSAIDs) in human gastric (AGS) cancer cells and the role of protein kinase C (PKC) and apoptosis-related oncogenes. After treatment with aspirin or indomethacin, cell growth was quantified by MTT assay, and apoptosis was determined by acridine orange staining, DNA fragmentation and flow cytometry. The mRNA and protein of p53, p21waf1/cip1 and c-myc was detected by Northern and Western blotting respectively. The influence of PKC on indomethacin-induced apoptosis was determined by co-incubation of 12-O-tetradecanoylphorbol 13-acetate (TPA). The role of c-myc was determined using its antisense oligonucleotides. The results showed that both aspirin and indomethacin inhibited cell growth and induced apoptosis of AGS cells in a dose- and time-dependent manner, without altering the cell cycle. Indomethacin increased c-myc mRNA and protein, whereas p53 and p21waf1/cip1 were unchanged. Down-regulation of c-myc by its antisense oligonucleotides reduced apoptosis induction by indomethacin. TPA could inhibit indomethacin-induced apoptosis and accumulate cells in G2/M. Overexpression of c-myc was inhibited by TPA and p21waf1/cip1 mRNA increased. In conclusion, NSAIDs induce apoptosis in gastric cancer cells which may be mediated by up-regulation of c-myc proto-oncogene. PKC activation can abrogate the effects of NSAIDs by decreasing c-myc expression. © 1999 Cancer Research Campaig

Distributed hash sketches: scalable, efficient, and accurate cardinality estimation for distributed multisets

Counting items in a distributed system, and estimating the cardinality of multisets in particular, is important for a large variety of applications and a fundamental building block for emerging Internet-scale information systems. Examples of such applications range from optimizing query access plans in peer-to-peer data sharing, to computing the significance (rank/score) of data items in distributed information retrieval. The general formal problem addressed in this article is computing the network-wide distinct number of items with some property (e.g., distinct files with file name containing “spiderman”) where each node in the network holds an arbitrary subset, possibly overlapping the subsets of other nodes. The key requirements that a viable approach must satisfy are: (1) scalability towards very large network size, (2) efficiency regarding messaging overhead, (3) load balance of storage and access, (4) accuracy of the cardinality estimation, and (5) simplicity and easy integration in applications. This article contributes the DHS (Distributed Hash Sketches) method for this problem setting: a distributed, scalable, efficient, and accurate multiset cardinality estimator. DHS is based on hash sketches for probabilistic counting, but distributes the bits of each counter across network nodes in a judicious manner based on principles of Distributed Hash Tables, paying careful attention to fast access and aggregation as well as update costs. The article discusses various design choices, exhibiting tunable trade-offs between estimation accuracy, hop-count efficiency, and load distribution fairness. We further contribute a full-fledged, publicly available, open-source implementation of all our methods, and a comprehensive experimental evaluation for various settings