11 research outputs found

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

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    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

    Chimeric Receptors Containing CD137 Signal Transduction Domains Mediate Enhanced Survival of T Cells and Increased Antileukemic Efficacy In Vivo

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    Persistence of T cells engineered with chimeric antigen receptors (CARs) has been a major barrier to use of these cells for molecularly targeted adoptive immunotherapy. To address this issue, we created a series of CARs that contain the T cell receptor-ζ (TCR-ζ) signal transduction domain with the CD28 and/or CD137 (4-1BB) intracellular domains in tandem. After short-term expansion, primary human T cells were subjected to lentiviral gene transfer, resulting in large numbers of cells with >85% CAR expression. In an immunodeficient mouse xenograft model of primary human pre-B-cell acute lymphoblastic leukemia, human T cells expressing anti-CD19 CARs containing CD137 exhibited the greatest antileukemic efficacy and prolonged (>6 months) survival in vivo, and were significantly more effective than cells expressing CARs containing TCR-ζ alone or CD28-ζ signaling receptors. We uncovered a previously unrecognized, antigen-independent effect of CARs expressing the CD137 cytoplasmic domain that likely contributes to the enhanced antileukemic efficacy and survival in tumor bearing mice. Furthermore, our studies revealed significant discrepancies between in vitro and in vivo surrogate measures of CAR efficacy. Together these results suggest that incorporation of the CD137 signaling domain in CARs should improve the persistence of CARs in the hematologic malignancies and hence maximize their antitumor activity

    Fludarabine and neurotoxicity in engineered T-cell therapy

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    31st Annual Meeting and Associated Programs of the Society for Immunotherapy of Cancer (SITC 2016): part one

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

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