Analyzing the vast coronavirus literature with CoronaCentral.
Ontology highlight
ABSTRACT:
SUBMITTER: Lever J
PROVIDER: S-EPMC8202008 | biostudies-literature |
REPOSITORIES: biostudies-literature
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Project description:The global SARS-CoV-2 pandemic has caused a surge in research exploring all aspects of the virus and its effects on human health. The overwhelming rate of publications means that human researchers are unable to keep abreast of the research. To ameliorate this, we present the CoronaCentral resource which uses machine learning to process the research literature on SARS-CoV-2 along with articles on SARS-CoV and MERS-CoV. We break the literature down into useful categories and enable analysis of the contents, pace, and emphasis of research during the crisis. These categories cover therapeutics, forecasting as well as growing areas such as "Long Covid" and studies of inequality and misinformation. Using this data, we compare topics that appear in original research articles compared to commentaries and other article types. Finally, using Altmetric data, we identify the topics that have gained the most media attention. This resource, available at https://coronacentral.ai , is updated multiple times per day and provides an easy-to-navigate system to find papers in different categories, focussing on different aspects of the virus along with currently trending articles.
| S-EPMC7781314 | biostudies-literature
Project description:On the behest of the Office of Science and Technology Policy in the White House, six institutions, including ours, have created an open research dataset called COVID-19 Research Dataset (CORD-19) to facilitate the development of question-answering systems that can assist researchers in finding relevant research on COVID-19. As of May 27, 2020, CORD-19 includes more than 100,000 open access publications from major publishers and PubMed as well as preprint articles deposited into medRxiv, bioRxiv, and arXiv. Recent years, however, have also seen question-answering and other machine learning systems exhibit harmful behaviors to humans due to biases in the training data. It is imperative and only ethical for modern scientists to be vigilant in inspecting and be prepared to mitigate the potential biases when working with any datasets. This article describes a framework to examine biases in scientific document collections like CORD-19 by comparing their properties with those derived from the citation behaviors of the entire scientific community. In total, three expanded sets are created for the analyses: 1) the enclosure set CORD-19E composed of CORD-19 articles and their references and citations, mirroring the methodology used in the renowned "A Century of Physics" analysis; 2) the full closure graph CORD-19C that recursively includes references starting with CORD-19; and 3) the inflection closure CORD-19I, that is, a much smaller subset of CORD-19C but already appropriate for statistical analysis based on the theory of the scale-free nature of the citation network. Taken together, all these expanded datasets show much smoother trends when used to analyze global COVID-19 research. The results suggest that while CORD-19 exhibits a strong tilt toward recent and topically focused articles, the knowledge being explored to attack the pandemic encompasses a much longer time span and is very interdisciplinary. A question-answering system with such expanded scope of knowledge may perform better in understanding the literature and answering related questions. However, while CORD-19 appears to have topical coverage biases compared to the expanded sets, the collaboration patterns, especially in terms of team sizes and geographical distributions, are captured very well already in CORD-19 as the raw statistics and trends agree with those from larger datasets.
| S-EPMC8025972 | biostudies-literature
Project description:Drug-drug interaction (DDI) is becoming a serious clinical safety issue as the use of multiple medications becomes more common. Searching the MEDLINE database for journal articles related to DDI produces over 330,000 results. It is impossible to read and summarize these references manually. As the volume of biomedical reference in the MEDLINE database continues to expand at a rapid pace, automatic identification of DDIs from literature is becoming increasingly important. In this article, we present a random-sampling-based statistical algorithm to identify possible DDIs and the underlying mechanism from the substances field of MEDLINE records. The substances terms are essentially carriers of compound (including protein) information in a MEDLINE record. Four case studies on warfarin, ibuprofen, furosemide and sertraline implied that our method was able to rank possible DDIs with high accuracy (90.0% for warfarin, 83.3% for ibuprofen, 70.0% for furosemide and 100% for sertraline in the top 10% of a list of compounds ranked by p-value). A social network analysis of substance terms was also performed to construct networks between proteins and drug pairs to elucidate how the two drugs could interact.
| S-EPMC4661569 | biostudies-literature
Project description:This study aimed to analyze stressors to which medical staff is vulnerable due to the coronavirus disease 2019 (COVID-19) pandemic. It also imposes a hierarchy on complex relations among stressors for excavating underlying structure and builds a model of interrelationships contrasting reality. The design of this study comprises a literature survey, data collection from primary sources, and analysis. Stressors have been explored from within current published/unpublished literature and validated by experts through approval vote. Data were collected from the focus group (panel of experts), and interpretive structural modeling (ISM) was used as the research methodology. Findings of ISM are avowed through "cross-impact matrix multiplication applied to classification" (MICMAC) analysis. As a result of the literature survey, a list of stressors was generated, and a total of 19 stressors qualified as representative of the phenomenon. The results of ISM show that two stressors (i.e., "unavailability of proper personal protective equipment (PPE)" and "lack of proper communication") emerged as the most critical stressors since they occupy the bottom of the model, whereas, four stressors (i.e., "anxious about isolation/quarantine," "subject to violent crimes," "feeling frustrated and powerless," and "exhausting shifts/hours without clear end") are relatively less critical since they occupy the top of the model. The rest of the stressors occupy the middle of the model and therefore, have moderate-severe effects on frontline soldiers. The results of MICMAC show that the stressor "subject to violent crimes" is classified in the dependent cluster and the remaining fall in the linkage cluster but no stressor falls in independent and autonomous. Overall results indicate that all stressors are relevant to the phenomenon under this study, but they are currently not settled. This study is invaluable for policymakers, frontline soldiers, researchers, the international community, and society since it provides a lot of new information that is helpful in refining strategies and combating influential stressors.
| S-EPMC8636429 | biostudies-literature
Project description:We compare the sets of experimentally validated long intergenic non-coding (linc)RNAs from human and mouse and apply a maximum likelihood approach to estimate the total number of lincRNA genes as well as the size of the conserved part of the lincRNome. Under the assumption that the sets of experimentally validated lincRNAs are random samples of the lincRNomes of the corresponding species, we estimate the total lincRNome size at approximately 40,000 to 50,000 species, at least twice the number of protein-coding genes. We further estimate that the fraction of the human and mouse euchromatic genomes encoding lincRNAs is more than twofold greater than the fraction of protein-coding sequences. Although the sequences of most lincRNAs are much less strongly conserved than protein sequences, the extent of orthology between the lincRNomes is unexpectedly high, with 60 to 70% of the lincRNA genes shared between human and mouse. The orthologous mammalian lincRNAs can be predicted to perform equivalent functions; accordingly, it appears likely that thousands of evolutionarily conserved functional roles of lincRNAs remain to be characterized.
| S-EPMC3585383 | biostudies-literature
Project description:Reading primary literature is a popular classroom practice that exposes students to the process of science. However, the analysis of primary literature can be taxing and time-consuming for students. For this reason, it is important to determine the source of student challenges and what motivates them to read primary literature. To better understand students' challenges, preferences, and motivations towards analyzing primary literature, we held focus groups with biology undergraduates where we asked them about their thoughts and perceptions on this practice. Students felt they struggle with understanding the big picture of an article, certain aspects of scientific literacy like data interpretation and experimental setup, and lack of knowledge of terms and techniques. Further analysis of the data using the achievement goal and expectancy-value theories of motivation revealed that students: 1) demonstrate mastery and performance approach goal orientations, which are typically associated with positive learning outcomes, 2) value the usefulness of reading primary literature, and 3) feel most engaged in the process of reading an article when the topic interests them. We provide pedagogical recommendations based on our findings.
| S-EPMC8109785 | biostudies-literature
Project description:The number of species on Earth is highly uncertain. A recent study has suggested that there are less than 2 million prokaryotic species on Earth; this Formal Comment suggests instead that there are more likely hundreds of millions or billions of species, and that the majority of these are bacteria associated with insects and other animals.
| S-EPMC8360379 | biostudies-literature
Project description:AimTo collect, organize and appraise evidence of socioeconomic and demographic inequalities in health and mortality among the older population using a summary measure of population health: Health Expectancy.MethodsA systematic literature review was conducted. Literature published in English before November 2014 was searched via two possible sources: three electronic databases (Web of Science, Medline and Embase), and references in selected articles. The search was developed combining terms referring to outcome, exposure and participants, consisting in health expectancy, socioeconomic and demographic groups, and older population, respectively.ResultsOf 256 references identified, 90 met the inclusion criteria. Six references were added after searching reference lists of included articles. Thirty-three studies were focused only on gender-based inequalities; the remaining sixty-three considered gender along with other exposures. Findings were organized according to two leading perspectives: the type of inequalities considered and the health indicators chosen to measure health expectancy. Evidence of gender-based differentials and a socioeconomic gradient were found in all studies. A remarkable heterogeneity in the choice of health indicators used to compute health expectancy emerged as well as a non-uniform way of defining same health conditions.ConclusionsHealth expectancy is a useful and convenient measure to monitor and assess the quality of ageing and compare different groups and populations. This review showed a general agreement of results obtained in different studies with regard to the existence of inequalities associated with several factors, such as gender, education, behaviors, and race. However, the lack of a standardized definition of health expectancy limits its comparability across studies. The need of conceiving health expectancy as a comparable and repeatable measure was highlighted as fundamental to make it an informative instrument for policy makers.
| S-EPMC4482630 | biostudies-literature
Project description:The novel coronavirus (COVID-19) pandemic has become a real challenge for healthcare providers around the world and has significantly affected the dental professionals in practices, universities and research institutions. The aim of this article was to review the available literature on the relevant aspects of dentistry in relation to COVID-19 and to discuss potential impacts of COVID-19 outbreak on clinical dentistry, dental education and research. Although the coronavirus pandemic has caused many difficulties for provision of clinical dentistry, there would be an opportunity for the dental educators to modernize their teaching approaches using novel digital concepts in teaching of clinical skills and by enhancement of online communication and learning platforms. This pandemic has also highlighted some of the major gaps in dental research and the need for new relevant knowledge to manage the current crisis and minimize the impact of such outbreaks on dentistry in the future. In conclusion, COVID-19 has had many immediate complications for dentistry of which some may have further long-term impacts on clinical practice, dental education and dental research.
| S-EPMC7345990 | biostudies-literature
Project description:Given the growing influence of non-academic organizations in the policy sphere, it is important to investigate the evidence both produced by and relied on by these organizations. Using citation analysis, a methodology primarily used in academic literature, we investigated the evidence base supporting the grey literature published by leading global management consulting firms (GMCFs) and international organizations (IOs). With the topic of the skills needed for the future of work as a case study, we collected 234 reports published by influential GMCFs and IOs over twenty years. By extracting references from the bibliographies of these reports we: 1) analyzed referencing patterns by measuring citation counts, institutional self-referencing and utilization of scholarly sources; 2) compared reference patterns across GMCFs and IOs; and 3) described the most influential sources. Overall, both GMCFs and IOs showed increasing reliance on grey literature, demonstrated high levels of self-referencing, and had considerable variation in the number of sources referred to. Across type of publishing organization, we found that IOs had better referencing practices than GMCFs. Our findings call into question the evidence-base behind the reports published by these policy actors. We emphasize the need to rely on strong academic literature to inform policy decisions around the future of work.
| S-EPMC9974120 | biostudies-literature