The value of the continuous genotyping of multi-drug resistant tuberculosis over 20 years in Spain

Molecular epidemiology of circulating clinical isolates is crucial to improve prevention strategies. The Spanish Working Group on multidrug resistant tuberculosis (MDR-TB) is a network that monitors the MDR-TB isolates in Spain since 1998. The aim of this study was to present the study of the MDR-TB and extensively drug-resistant tuberculosis (XDR-TB) patterns in Spain using the different recommended genotyping methods over time by a national coordinated system. Based on the proposed genotyping methods in the European Union until 2018, the preservation of one method, MIRU-VNTR, applied to selected clustered strains permitted to maintain our study open for 20 years. The distribution of demographic, clinical and epidemiological characteristics of clustered and non-clustered cases of MDR/XDR tuberculosis with proportion differences as assessed by Pearson’s chi-squared or Fisher’s exact test was compared. The differences in the quantitative variables using the Student's-t test and the Mann–Whitney U test were evaluated. The results obtained showed a total of 48.4% of the cases grouped in 77 clusters. Younger age groups, having a known TB case contact (10.2% vs 4.7%) and XDR-TB (16.5% vs 1.8%) were significantly associated with clustering. The largest cluster corresponded to a Mycobacterium bovis strain mainly spread during the nineties. A total of 68.4% of the clusters detected were distributed among the different Spanish regions and six clusters involving 104 cases were grouped in 17 and 18 years. Comparison of the genotypes obtained with those European genotypes included in The European Surveillance System (TESSy) showed that 87 cases had become part of 20 European clusters. The continuity of MDR strain genotyping in time has offered a widespread picture of the situation that allows better management of this public health problem. It also shows the advantage of maintaining one genotyping method over time, which allowed the comparison between ancient, present and future samples

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