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

Impact of cardiorespiratory fitness and diabetes status on cardiovascular disease and all-cause mortality: An NHANES retrospective cohort study.

UNLABELLED: High cardiorespiratory fitness (CRF) is associated with decreased mortality in people with pre-diabetes (pre-DM) and diabetes mellitus (DM); however, the degree to which CRF attenuates the risk of cardiovascular disease (CVD)-related and all-cause mortality is unclear. STUDY OBJECTIVE: We examined the impact of CRF status on CVD-related morbidity and all-cause mortality in non-DM, Pre-DM, and DM populations. DESIGN AND SETTING: 13,968 adults from the Third US National Health and Nutrition Examination Survey (NHANES III) were stratified into non-DM, pre-DM, or DM groups based on HbA1c levels. VO2Max was calculated using the Fitness Registry and Importance of Exercise: A National Database (FRIEND) equation. PARTICIPANTS: Participants were categorized into tertiles of VO2Max; first VO2Max tertile was the lowest VO2Max and third VO2Max tertile was the highest. MAIN OUTCOME MEASURES: Cox regression was used to analyze the relationship between glycemic levels, VO2Max, and CVD-related and all-cause mortality. RESULTS: Those with DM in the highest fitness tertile had CVD (HR 0.13; 95 % CI 0.06, 0.27; p < 0.0001) and all cause (HR 0.28; 95 % CI 0.21, 0.38; p < 0.0001) mortality rates as low or lower than those with pre-DM (CVD HR 1.02; 95 % CI 0.78, 1.33 p < 0.892; all cause HR 0.96; 95 % CI 0.83, 1.12; p < 0.5496) or non-DM (CVD HR 0.65; 95 % CI 0.52, 0.80; p < 0.0001; all cause HR 0.61; 95 % CI 0.55, 0.68; p < 0.0001) at lower fitness levels. Regardless of DM status, there was lower all-cause mortality with higher CRF levels. CONCLUSIONS: Higher fitness levels in DM individuals are associated with total and CVD mortality rates as low or lower than those without DM with lower fitness

Novel Approaches to Program Cells to Differentiate into Cardiomyocytes in Myocardial Regeneration

With heart failure (HF) being one of the leading causes of hospitalization and death worldwide, multiple stem cell therapies have been attempted to accelerate the regeneration of the infarct zone. Versatile strategies have emerged to establish the cell candidates of cardiomyocyte lineage for regenerative cardiology. This article illustrates critical insights into the emerging technologies, current approaches, and translational promises on the programming of diverse cell types for cardiac regeneration

Knowledge and Educational Needs about Pre-Implantation Genetic Diagnosis (PGD) among Oncology Nurses

Preimplantation genetic diagnosis (PGD), a form of assisted reproductive technology, is a new technology with limited awareness among health care professionals and hereditary cancer families. Nurses play a key role in the care of patients and are often in an ideal position to discuss and refer patients on sensitive quality of life issues, such as PGD. Two hundred and one nurses at Moffitt Cancer Center (MCC) responded to an online survey assessing knowledge and educational needs regarding PGD and families with hereditary cancer. The majority of respondents were female (n = 188), white (n = 175), had an RN/BSN degree (n = 83), and provided outpatient care at the cancer center (n = 102). More than half of respondents (78%) were unfamiliar with PGD prior to the survey and respondents who had heard of PGD had limited knowledge. More than half of the participants reported PGD was an acceptable option for families with hereditary cancer syndromes and thought individuals with a strong family or personal history should be provided with information about PGD. This study indicates that oncology nurses may benefit from and desire education about PGD. With advances in reproductive technology and options, further PGD education is needed among healthcare professionals. An examination of current oncology nursing curriculum and competencies regarding genetic education may identify need for future revisions and updates

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