Poor association between 13-valent pneumococcal conjugate vaccine-induced serum and mucosal antibody responses with experimental Streptococcus pneumoniae serotype 6B colonisation

Background Pneumococcal carriage is the primary reservoir for transmission and a prerequisite for invasive pneumococcal disease. Pneumococcal Conjugate Vaccine 13 (PCV13) showed a 62% efficacy in protection against experimental Streptococcus pneumoniae serotype 6B (Spn6B) carriage in a controlled human infection model (CHIM) of healthy Malawian adults. We, therefore, measured humoral responses to experimental challenge and PCV-13 vaccination and determined the association with protection against pneumococcal carriage. Methods We vaccinated 204 young, healthy Malawian adults with PCV13 or placebo and nasally inoculated them with Spn6B at least four weeks post-vaccination to establish carriage. We collected peripheral blood and nasal lining fluid at baseline, 4 weeks post-vaccination (7 days pre-inoculation), 2, 7, 14 and > 1 year post-inoculation. We measured the concentration of anti-serotype 6B Capsular Polysaccharide (CPS) Immunoglobulin G (IgG) and IgA antibodies in serum and nasal lining fluid using the World Health Organization (WHO) standardised enzyme-linked immunosorbent assay (ELISA). Results PCV13-vaccinated adults had higher serum IgG and nasal IgG/IgA anti-Spn6B CPS-specific binding antibodies than placebo recipients 4 to 6 weeks post-vaccination, which persisted for at least a year after vaccination. Nasal challenge with Spn6B did not significantly alter serum or nasal anti-CPS IgG binding antibody titers with or without experimental pneumococcal carriage. Pre-challenge titers of PCV13-induced serum IgG and nasal IgG/IgA anti-Spn6B CPS binding antibodies did not significantly differ between those that got experimentally colonised by Spn6B compared to those that did not. Conclusion This study demonstrates that despite high PCV13 efficacy against experimental Spn6B carriage in young, healthy Malawian adults, robust vaccine-induced systemic and mucosal anti-Spn6B CPS binding antibodies did not directly relate to protection

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

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

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