Identification of phenolic constituents of cytisus multiflorus

The phenolic composition of the ethanolic extract obtained from the flowers of the medicinal plant Cytisus multiflorus has been elucidated by high performance liquid chromatography, electrospray mass spectrometry and nuclear magnetic resonance analysis. The extract was mainly composed of flavones, including the common chrysin, orientin, luteolin-5-O-glucoside, luteolin-7-O-glucoside, apigenin and apigenin-7-O-glucoside, which appeared as minor components. The major flavone in the extract was chrysin-7-O-B-D-glucopyranoside, and it also contained moderate amounts of a dihydroxyflavone isomer of chrysin, as well as of 2''-O-pentosyl-6-C-hexosyl-luteolin, 2''-O-pentosyl-8-C-hexosyl-luteolin and 6''- O-(3-hydroxy-3-methylglutaroyl)-2''-O-pentosyl-C-hexosyl-apigenin, which are not commonly found in the Fabaceae family. Other novel phenolic compounds found in the ethanolic extract of C. multiflorus comprised the flavones 2''-O-pentosyl-6-C-hexosyl-apigenin, 2''-O-pentosyl-8-C-hexosyl-apigenin and 6''-O-(3-hydroxy-3-methylglutaroyl)-200-O-pentosyl-C-hexosyl-luteolin. The assessment of the biological activities of the main compounds of this extract are now keen, in order to determine their relevance in the beneficial properties of the plant

The Influence of Vitamin D on Neurodegeneration and Neurological Disorders: A Rationale for its Physio-pathological Actions

International audienc

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