Behavioral assessment of neuropathic pain, fatigue, and anxiety in experimental autoimmune encephalomyelitis (EAE) and attenuation by interleukin-10 gene therapy

Relapsing-remitting multiple sclerosis is commonly associated with motor impairments, neuropathic pain, fatigue, mood disorders, and decreased life expectancy. However, preclinical pharmacological studies predominantly rely on clinical scoring of motor deficit as the sole behavioral endpoint. Thus, the translational potential of these studies is limited. Here, we have assessed the therapeutic potential of a novel anti-inflammatory interleukin-10 (IL-10) non-viral gene therapy formulation (XT-101-R) in a rat relapsing remitting experimental autoimmune encephalomyelitis (EAE) model. EAE induced motor deficits and neuropathic pain as reflected by induction of low-threshold mechanical allodynia, suppressed voluntary wheel running, decreased social exploration, and was associated with markedly enhanced mortality. We also noted that voluntary wheel running was depressed prior to the onset of motor deficit, and may therefore serve as a predictor of clinical symptoms onset. XT-101-R was intrathecally dosed only once at the onset of motor deficits, and attenuated each of the EAE-induced symptoms and improved survival, relative to vehicle control. This is the first pharmacological assessment of such a broad range of EAE symptoms, and provides support for IL-10 gene therapy as a clinical strategy for the treatment of multiple sclerosis.Peter M.Grace, Lisa C.Loram, John P.Christianson, Keith A.Strand, Johanna G.Flyer-Adams, Kathryn R.Penzkover ... et al

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 science. © The Author(s) 2019. Published by Oxford University Press