Lactobacillus sp and some fungi from termite nests on kolanut trees had mild antagonistic effects against pathogens isolated from paediatric patients

Background: Residents in a rural suburb of Akure jettisoned antibiotic treatment; sought alternative cure to rising incidence of paediatric infections in 2017 from local herbal dealers, with many residents claiming of better treatment response. We investigated these claims since the local herbal formula included kola nut barks and ground termites.Methodology: Microorganisms associated with termite nests on kola nut trees in the affected community were characterized and identified using standard techniques. The Kirby Bauer disk diffusion was used to evaluate the susceptibility of the bacterial isolates to selected antibiotics. Plasmid profile of multiple antibiotic resistant bacterial isolates (MDRIs) was determined by the Birnboim and Doly method while post plasmid curing antibiotic susceptibility was performed on the MDRIs against the same selected antibiotics. The microorganisms were also evaluated for possible antagonistic effects against Salmonella sp, Staphylococcus aureus and Streptococcus pyogenes isolated from paediatric patients during the period of study using previously described methods.Results: Bacteria (Corynebacterium sp, Streptococcus sp, Acinetobacter sp and Lactobacillus sp) and fungal (Geotrichum condidum, Aspergillus niger, Fusarium oxysporum and Fusarium fujikuroi) were isolated from the termite nests. The antibiotic susceptibility revealed that Corynebacterium sp and Streptococcus sp were multiply antibiotic resistant, and this was confirmed to be plasmid mediated based on plasmid analysis and curing. The Lactobacillus sp, Aspergillus niger, Fusarium fujikuroi and Geotrichum condidum exhibited mild antagonisms against Staphylococcus aureus, Salmonella sp and Streptococcus pyogenes isolated from paediatric patients.Conclusion: This study suggests that termite nests on kola nut trees contain microbes that possess antagonistic actions against pathogens from paediatric patients and that some bacteria associated with termite guts may pose significant risk of increased antibiotic resistance if implicated in human infections.Keywords: Termite nests, Resistance, Antagonistic microbes, Termites, Plasmid, Kola nut tre

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