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

Oligopeptide Sortase Inhibitor Modulates <i>Staphylococcus aureus</i> Cell Adhesion and Biofilm Formation

Prevention of bacterial adhesion is one of the most important antivirulence strategies for meeting the global challenge posed by antimicrobial resistance. We aimed to investigate the influence of a peptidic S. aureus sortase A inhibitor on bacterial adhesion to eukaryotic cells and biofilm formation as a potential method for reducing S. aureus virulence. The pentapeptide LPRDA was synthesized and characterized as a pure individual organic compound. Incubation of MSSA and MRSA strains with LPRDA induced a subsequent reduction in staphylococcal adhesion to Vero cells and biofilm formation, as visualized by microscopic and spectrophotometric methods, respectively. LPRDA did not have a cytotoxic effect on eukaryotic or bacterial cells. The pentapeptide LPRDA deserves further investigation using in vitro and in vivo models of Gram-positive bacteriemia as a potential antibacterial agent with an antiadhesive mechanism of action

Evaluation of LPRDA Pentapeptide for the Prevention and Treatment of Staphylococcus aureus Peritoneal Infection

Targeting virulence determinants is a promising approach to controlling S. aureus infections in the face of the global spread of antibiotic resistance. S. aureus-induced peritonitis often occurs in dialysis, implant and trauma patients. To develop novel prevention and treatment options for peritoneal infection, we investigated the oligopeptide sortase A inhibitor LPRDA as a non-conventional antibacterial that does not affect staphylococcal survival. Administration of LPRDA prior to S. aureus challenge reduced the bacterial load of internal organs and bacterial colonization of the abdominal cavity in animals. In addition, LPRDA inhibited &alpha;-hemolysin production in 80% of the 35 reference and clinical S. aureus strains tested. Consequent research of LPRDA interactions with cefazolin and vancomycin has demonstrated the potential for combined application of the antivirulent and antibiotic agents under study

Protein-Energy Malnutrition as a Predictor of Early Recurrent Revisions After Debridement Surgery in Patients With Difficult-to-Treat Periprosthetic Infection

Background. Protein-energy malnutrition (PEM) is an established risk factor of postoperative complications in orthopedic disorders, including arthroplasty of the large joints.&#x0D; The study aimed to evaluate PEM prevalence and its association with the early postoperative revision in patients with the difficult-to-treat (DTT) prosthetic joint infection (PJI) of the hip.&#x0D; Methods. The retrospective study included 132 patients with chronic DTT PJI of the hip. The patients underwent orthopedic implant removal, radical debridement of the infected tissues, and resection arthroplasty with non-free transplantation of an axial vastus lateralis muscle flap (n = 57) or installation of an antimicrobial spacer (n = 75). DTT PJI was defined as an infection caused by rifampicin-resistant staphylococcal strains, ciprofloxacin-resistant gram-negative bacteria, fungi of the genus Candida, and their associations. The assessment of the patients protein-energy status included the evaluation of reference laboratory parameters, such as levels of hemoglobin, total protein, and albumin and number of lymphocytes. The degree of PEM was determined by the number of laboratory markers below the threshold values. The statistical comparison was performed using Fishers test. The odds ratio (OR, 95% confidence interval [CI]) was calculated to assess the risk of PJI recurrence. Differences were considered significant at p0.05.&#x0D; Results. More than 70% of patients with chronic DTT PJI included in the study were diagnosed with preoperative PEM of varying degrees of severity. Hypoalbuminemia and decreased hemoglobin levels were diagnosed more often: 64.3% and 57.1% in the muscle flap plasty and 57.3% and 31.1% in the antimicrobial spacer group, respectively. In muscle plasty and antimicrobial spacer groups, a decrease in the values of three or more reference PEM markers was detected in 28.5% and 16.0% of patients, and this advanced impairment of the nutritional status increased the risk of early revision intervention by two (OR 2.0; CI 95% 0.478.56; p = 0.35) and six times (OR 6.11; 95% CI 1.0635.35; p0.04), respectively.&#x0D; Conclusion. In general, the analysis of publications and results of our study show that PEM is associated with the development of surgical site infection and recurrence of PJI after revision surgery. A decrease in the values of three or more reference PEM markers is a significant predictor of repeated revisions after debridement surgery with the installation of an antimicrobial spacer. PEM complicates the postoperative course in patients with resection arthroplasty. Given the high incidence of PEM in patients with DTT PJI of the hip joint, further research is needed to develop methods for nutritional status correction and assessment of their effect on the outcomes of debridement surgery.</jats:p

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

Abstract 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.</jats:p

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