Nitric oxide role in anxiety-like behavior, memory and cognitive impairments in animal model of chronic migraine

The occurrence of cognitive dysfunctions and anxiety and mood disorders has been shown to be higher in migraine patients. Nitric Oxide (NO) is a significant neurotransmitter in the pathophysiology of migraine, anxiety and neurodegenerative disorders. Therefore, the present study was conducted to evaluate the role of NO system in migraine-induced memory impairment and anxiety like behaviors. Nitroglycerin (NTG) was administered to the animals as an animal model of migraine and pretreatment with L-Arginine, L-NAME and saline were implemented to evaluate the role of NO system in possible cognitive impairments in animal model of migraine. Avoidance learning and memory performance, object recognition memory, anxiety-like behavior and motor activity were assessed using a shuttle box apparatus, novel object recognition, elevated plus-maze, and open field tests respectively. The data showed that the injection of nitroglycerin disturbs learning and memory and elicit anxiety like behavior in the animals. L-NAME administration suppressed the observed effect of nitroglycerin on memory and anxiety. Overall, the results indicated that nitric oxide system is implicated in memory impairments and anxiety like behavior in an animal model of migraine. © 2020 The Author(s) Nitric oxide; Migraine; Cognitive function; Anxiety; Neuroscience; Cell biology; Physiology; Neurology; Psychiatry; Psychology © 2020 The Author(s

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