Cationic Polybutyl Cyanoacrylate Nanoparticles for DNA Delivery

To enhance the intracellular delivery potential of plasmid DNA using nonviral vectors, we used polybutyl cyanoacrylate (PBCA) and chitosan to prepare PBCA nanoparticles (NPs) by emulsion polymerization and prepared NP/DNA complexes through the complex coacervation of nanoparticles with the DNA. The object of our work is to evaluate the characterization and transfection efficiency of PBCA-NPs. The NPs have a zeta potential of 25.53 mV at pH 7.4 and size about 200 nm. Electrophoretic analysis suggested that the NPs with positive charges could protect the DNA from nuclease degradation and cell viability assay showed that the NPs exhibit a low cytotoxicity to human hepatocellular carcinoma (HepG2) cells. Qualitative and quantitative analysis of transfection in HepG2 cells by the nanoparticles carrying plasmid DNA encoding for enhanced green fluorescent protein (EGFP-N1) was done by digital fluorescence imaging microscopy system and fluorescence-activated cell sorting (FACS). Qualitative results showed highly efficient expression of GFP that remained stable for up to 96 hours. Quantitative results from FACS showed that PBCA-NPs were significantly more effective in transfecting HepG2 cells after 72 hours postincubation. The results of this study suggested that PBCA-NPs have favorable properties for nonviral delivery

Large expert-curated database for benchmarking document similarity detection in biomedical literature search

Document recommendation systems for locating relevant literature have mostly relied on methods developed a decade ago. This is largely due to the lack of a large offline gold-standard benchmark of relevant documents that cover a variety of research fields such that newly developed literature search techniques can be compared, improved and translated into practice. To overcome this bottleneck, we have established the RElevant LIterature SearcH consortium consisting of more than 1500 scientists from 84 countries, who have collectively annotated the relevance of over 180 000 PubMed-listed articles with regard to their respective seed (input) article/s. The majority of annotations were contributed by highly experienced, original authors of the seed articles. The collected data cover 76% of all unique PubMed Medical Subject Headings descriptors. No systematic biases were observed across different experience levels, research fields or time spent on annotations. More importantly, annotations of the same document pairs contributed by different scientists were highly concordant. We further show that the three representative baseline methods used to generate recommended articles for evaluation (Okapi Best Matching 25, Term Frequency-Inverse Document Frequency and PubMed Related Articles) had similar overall performances. Additionally, we found that these methods each tend to produce distinct collections of recommended articles, suggesting that a hybrid method may be required to completely capture all relevant articles. The established database server located at https://relishdb.ict.griffith.edu.au is freely available for the downloading of annotation data and the blind testing of new methods. We expect that this benchmark will be useful for stimulating the development of new powerful techniques for title and title/abstract-based search engines for relevant articles in biomedical research.Peer reviewe

Semantic multi-grain mixture topic model for text analysis

National Natural Science Foundation of China [61073170]; Shanghai Leading Academic Discipline Project [B114]; Fudan UniversityGranular topic extraction and modeling are fundament tasks in text analysis. Hierarchical topic clustering algorithms and hierarchical topic models are usually employed for these purposes. However, it is difficult to make a clear distinguish between each pair of hierarchical topics from the semantic granularity point of view. STG (semantic topic granularity) is proposed to indicate the details degree of topic description, and aim at providing discrimination for topics from semantic aspect. A new model, mgMTM (multi-grain mixture topic model) based on STG is then proposed to model grain topics. DCT (discrete cosine transform) is employed to provide a mechanism for computing STG, extracting grain topics and learning mgMTM. Experiments on real world datasets show that the proposed model has lower perplexity score than that of LDA model and thus has better generalization performance in describing text. Experiments also show that the description of the extracted grain topics can be well explained with respect to a dataset including topics about recent global financial crisis. (C) 2010 Elsevier Ltd. All rights reserved

Identification of MicroRNAs Involved in Growth Arrest and Apoptosis in Hydrogen Peroxide-Treated Human Hepatocellular Carcinoma Cell Line HepG2

Although both oxidative stress and microRNAs (miRNAs) play vital roles in physiological and pathological processes, little is known about the interactions between them. In this study, we first described the regulation of H2O2 in cell viability, proliferation, cycle, and apoptosis of human hepatocellular carcinoma cell line HepG2. Then, miRNAs expression was profiled after H2O2 treatment. The results showed that high concentration of H2O2 (600 μM) could decrease cell viability, inhibit cell proliferation, induce cell cycle arrest, and finally promote cell apoptosis. Conversely, no significant effects could be found under treatment with low concentration (30 μM). miRNAs array analysis identified 131 differentially expressed miRNAs (125 were upregulated and 6 were downregulated) and predicted 13504 putative target genes of the deregulated miRNAs. Gene ontology (GO) analysis revealed that the putative target genes were associated with H2O2-induced cell growth arrest and apoptosis. The subsequent bioinformatics analysis indicated that H2O2-response pathways, including MAPK signaling pathway, apoptosis, and pathways in cancer and cell cycle, were significantly affected. Overall, these results provided comprehensive information on the biological function of H2O2 treatment in HepG2 cells. The identification of miRNAs and their putative targets may offer new diagnostic and therapeutic strategies for liver cancer