Photoinduced antibacterial activity of the essential oils from Eugenia brasiliensis lam and Piper mosenii C. DC. by blue led light

The objective of this work was to evaluate the phytochemical composition and the antibacterial and antibiotic-modulating activities of the essential oils of Eugenia brasiliensis Lam (OEEb) and Piper mosenii C. DC (OEPm) singly or in association with blue LED (Light-emitting diode) light. The antibacterial and antibiotic-modulatory activities of the essential oils on the activity of aminoglycosides were evaluated to determine the minimum inhibitory concentration (MIC, \u3bcg/mL) in the presence or absence of exposure to blue LED light. The chemical analysis showed \u3b1-pinene and bicyclogermacrene as major constituents of OEPm, whereas \u3b1-muurolol was the main compound of OEEb. Both OEEb and OEPm showed MIC 65 512 \u3bcg/mL against the strains under study. However, the association of these oils with the blue LED light enhanced the action of the aminoglycosides amikacin and gentamicin. In conclusion, the association of aminoglycosides with the blue LED light and essential oils was effective against resistant bacteria

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