Control of Vancomycin-Resistant Enterococcus in Health Care Facilities in a Region

Background In late 1996, vancomycin-resistant enterococci were first detected in the Siouxland region of Iowa, Nebraska, and South Dakota. A task force was created, and in 1997 the assistance of the Centers for Disease Control and Prevention was sought in assessing the prevalence of vancomycin-resistant enterococci in the region’s facilities and implementing recommendations for screening, infection control, and education at all 32 health care facilities in the region. Methods The infection-control intervention was evaluated in October 1998 and October 1999. We performed point-prevalence surveys, conducted a case– control study of gastrointestinal colonization with vancomycin-resistant enterococci, and compared infection-control practices and screening policies for vancomycin-resistant enterococci at the acute care and long-term care facilities in the Siouxland region. Results Perianal-swab samples were obtained from 1954 of 2196 eligible patients (89 percent) in 1998 and 1820 of 2049 eligible patients (89 percent) in 1999. The overall prevalence of vancomycin-resistant enterococci at 30 facilities that participated in all three years of the study decreased from 2.2 percent in 1997 to 1.4 percent in 1998 and to 0.5 percent in 1999 (P Conclusions An active infection-control intervention, which includes the obtaining of surveillance cultures and the isolation of infected patients, can reduce or eliminate the transmission of vancomycinresistant enterococci in the health care facilities of a region. (N Engl J Med 2001;344:1427-33.

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

QSOX1 Inhibits Autophagic Flux in Breast Cancer Cells

The QSOX1 protein (Quiescin Sulfhydryl oxidase 1) catalyzes the formation of disulfide bonds and is involved in the folding and stability of proteins. More recently, QSOX1 has been associated with tumorigenesis and protection against cellular stress. It has been demonstrated in our laboratory that QSOX1 reduces proliferation, migration and invasion of breast cancer cells in vitro and reduces tumor growth in vivo. In addition, QSOX1 expression has been shown to be induced by oxidative or ER stress and to prevent cell death linked to these stressors. Given the function of QSOX1 in these two processes, which have been previously linked to autophagy, we wondered whether QSOX1 might be regulated by autophagy inducers and play a role in this catabolic process. To answer this question, we used in vitro models of breast cancer cells in which QSOX1 was overexpressed (MCF-7) or extinguished (MDA-MB-231). We first showed that QSOX1 expression is induced following amino acid starvation and maintains cellular homeostasis. Our results also indicated that QSOX1 inhibits autophagy through the inhibition of autophagosome/lysosome fusion. Moreover, we demonstrated that inhibitors of autophagy mimic the effect of QSOX1 on cell invasion, suggesting that its role in this process is linked to the autophagy pathway. Previously published data demonstrated that extinction of QSOX1 promotes tumor growth in NOG mice. In this study, we further demonstrated that QSOX1 null tumors present lower levels of the p62 protein. Altogether, our results demonstrate for the first time a role of QSOX1 in autophagy in breast cancer cells and tumors

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