The Infectious Disease Ontology in the age of COVID-19.

BACKGROUND: Effective response to public health emergencies, such as we are now experiencing with COVID-19, requires data sharing across multiple disciplines and data systems. Ontologies offer a powerful data sharing tool, and this holds especially for those ontologies built on the design principles of the Open Biomedical Ontologies Foundry. These principles are exemplified by the Infectious Disease Ontology (IDO), a suite of interoperable ontology modules aiming to provide coverage of all aspects of the infectious disease domain. At its center is IDO Core, a disease- and pathogen-neutral ontology covering just those types of entities and relations that are relevant to infectious diseases generally. IDO Core is extended by disease and pathogen-specific ontology modules. RESULTS: To assist the integration and analysis of COVID-19 data, and viral infectious disease data more generally, we have recently developed three new IDO extensions: IDO Virus (VIDO); the Coronavirus Infectious Disease Ontology (CIDO); and an extension of CIDO focusing on COVID-19 (IDO-COVID-19). Reflecting the fact that viruses lack cellular parts, we have introduced into IDO Core the term acellular structure to cover viruses and other acellular entities studied by virologists. We now distinguish between infectious agents - organisms with an infectious disposition - and infectious structures - acellular structures with an infectious disposition. This in turn has led to various updates and refinements of IDO Core's content. We believe that our work on VIDO, CIDO, and IDO-COVID-19 can serve as a model for yielding greater conformance with ontology building best practices. CONCLUSIONS: IDO provides a simple recipe for building new pathogen-specific ontologies in a way that allows data about novel diseases to be easily compared, along multiple dimensions, with data represented by existing disease ontologies. The IDO strategy, moreover, supports ontology coordination, providing a powerful method of data integration and sharing that allows physicians, researchers, and public health organizations to respond rapidly and efficiently to current and future public health crises.

COVID19 Disease Map, a computational knowledge repository of virus-host interaction mechanisms.

We need to effectively combine the knowledge from surging literature with complex datasets to propose mechanistic models of SARS-CoV-2 infection, improving data interpretation and predicting key targets of intervention. Here, we describe a large-scale community effort to build an open access, interoperable and computable repository of COVID-19 molecular mechanisms. The COVID-19 Disease Map (C19DMap) is a graphical, interactive representation of disease-relevant molecular mechanisms linking many knowledge sources. Notably, it is a computational resource for graph-based analyses and disease modelling. To this end, we established a framework of tools, platforms and guidelines necessary for a multifaceted community of biocurators, domain experts, bioinformaticians and computational biologists. The diagrams of the C19DMap, curated from the literature, are integrated with relevant interaction and text mining databases. We demonstrate the application of network analysis and modelling approaches by concrete examples to highlight new testable hypotheses. This framework helps to find signatures of SARS-CoV-2 predisposition, treatment response or prioritisation of drug candidates. Such an approach may help deal with new waves of COVID-19 or similar pandemics in the long-term perspective.

Impact of the COVID-19 pandemic on workplace violence at an academic emergency department.

STUDY OBJECTIVES: COVID-19 brought unique challenges; however, it remains unclear what effect the pandemic had on violence in healthcare. The objective of this study was to identify the impact of the pandemic on workplace violence at an academic emergency department (ED). METHODS: This mixed-methods study involved a prospective descriptive survey study and electronic medical record review. Within our hospital referral region (HRR), the first COVID-19 case was documented on 3/11/2020 and cases peaked in mid-November 2020. We compared the monthly HRR COVID-19 case rate per 100,000 people to the rate of violent incidents per 1000 ED visits. Multidisciplinary ED staff were surveyed both pre/early-pandemic (April 2020) and mid/late-pandemic (December 2020) regarding workplace violence experienced over the prior 6-months. The study was deemed exempt by the Mayo Clinic Institutional Review Board. RESULTS: There was a positive association between the monthly HRR COVID-19 case rate and rate of violent ED incidents (r = 0.24). Violent incidents increased overall during the pandemic (2.53 incidents per 1000 visits) compared to the 3 months prior (1.13 incidents per 1000 visits, p < .001), as well as compared to the previous year (1.24 incidents per 1000 patient visits, p < .001). Survey respondents indicated a higher incidence of assault during the pandemic, compared to before (p = .019). DISCUSSION: Incidents of workplace violence at our ED increased during the pandemic and there was a positive association of these incidents with the COVID-19 case rate. Our findings indicate health systems should prioritize employee safety during future pandemics.

Discovering New Trends & Connections: Current Applications of Biomedical Text Mining.

The explosive growth of digital information in recent years has amplified the information overload experienced by today's health-care professionals. In particular, the wide variety of unstructured text makes it difficult for researchers to find meaningful data without spending a considerable amount of time reading. Text mining can be used to facilitate better discoverability and analysis, and aid researchers in identifying critical trends and connections. This column will introduce key text-mining terms, recent use cases of biomedical text mining, and current applications for this technology in medical libraries.

Impact of Coronavirus disease 2019 on patients with toothache: Analysis of tweets on Weibo.

OBJECTIVE: The aim of this study is to investigate the impact of Coronavirus disease 2019 (COVID-19) on toothache patients through posts on Sina Weibo. METHODS: Using Gooseeker, we searched and screened 24,108 posts about toothache on Weibo during the dental clinical closure period of China (February 1, 2020-February 29, 2020), and then divided them into 4 categories (causes of toothache, treatments of toothache, impacts of COVID-19 on toothache treatment, popular science articles of toothache), including 10 subcategories, to analyze the proportion of posts in each category. RESULTS: There were 12,603 postings closely related to toothache. Among them, 87.6% of posts did not indicate a specific cause of pain, and 92.8% of posts did not clearly indicate a specific method of treatment. There were 38.9% of the posts that clearly showed that their dental treatment of toothache was affected by COVID-19, including 10.5% of the posts in which patients were afraid to see the dentists because of COVID-19, and 28.4% of the posts in which patients were unable to see the dentists because the dental clinic was closed. Only 3.5% of all posts were about popular science of toothache. CONCLUSIONS: We have studied and analyzed social media data about toothache during the COVID-19 epidemic, so as to provide some insights for government organizations, the media and dentists to better guide the public to pay attention to oral health through social media. Research on social media data can help formulate public health policies.

Application of Data Mining Algorithms for Dementia in People with HIV/AIDS.

Dementia interferes with the individual's motor, behavioural, and intellectual functions, causing him to be unable to perform instrumental activities of daily living. This study is aimed at identifying the best performing algorithm and the most relevant characteristics to categorise individuals with HIV/AIDS at high risk of dementia from the application of data mining. Principal component analysis (PCA) algorithm was used and tested comparatively between the following machine learning algorithms: logistic regression, decision tree, neural network, KNN, and random forest. The database used for this study was built from the data collection of 270 individuals infected with HIV/AIDS and followed up at the outpatient clinic of a reference hospital for infectious and parasitic diseases in the State of Ceará, Brazil, from January to April 2019. Also, the performance of the algorithms was analysed for the 104 characteristics available in the database; then, with the reduction of dimensionality, there was an improvement in the quality of the machine learning algorithms and identified that during the tests, even losing about 30% of the variation. Besides, when considering only 23 characteristics, the precision of the algorithms was 86% in random forest, 56% logistic regression, 68% decision tree, 60% KNN, and 59% neural network. The random forest algorithm proved to be more effective than the others, obtaining 84% precision and 86% accuracy.

Ageism in COVID-Related Newspaper Coverage: The First Month of a Pandemic.

OBJECTIVES: Media sources have consistently described older adults as a medically vulnerable population during the coronavirus disease 2019 (COVID-19) pandemic, yet a lack of concern over their health and safety has resulted in dismissal and devaluation. This unprecedented situation highlights ongoing societal ageism and its manifestations in public discourse. This analysis asks how national news sources performed explicit and implicit ageism during the first month of the pandemic. METHOD: Using content and critical discourse analysis methods, we analyzed 287 articles concerning older adults and COVID-19 published between March 11 and April 10, 2020, in 4 major U.S.-based newspapers. RESULTS: Findings indicate that while ageism was rarely discussed explicitly, ageist bias was evident in implicit reporting patterns (e.g., frequent use of the term "elderly," portrayals of older adults as "vulnerable"). Infection and death rates and institutionalized care were among the most commonly reported topics, providing a limited portrait of aging during the pandemic. The older "survivor" narrative offers a positive alternative by suggesting exceptional examples of resilience and grit. However, the survivor narrative may also implicitly place blame on those unable to survive or thrive in later life. DISCUSSION: This study provides insight for policy makers, researchers, and practitioners exploring societal perceptions of older adults and how these perceptions are disseminated and maintained by the media.

Culture Linked to Increasing Ageism During COVID-19: Evidence From a 10-Billion-Word Corpus Across 20 Countries.

OBJECTIVES: Older adults experience higher risks of getting severely ill from coronavirus disease 2019 (COVID-19), resulting in widespread narratives of frailty and vulnerability. We test: (a) whether global aging narratives have become more negative from before to during the pandemic (October 2019 to May 2020) across 20 countries; (b) model pandemic (incidence and mortality), and cultural factors associated with the trajectory of aging narratives. METHODS: We leveraged a 10-billion-word online-media corpus, consisting of 28 million newspaper and magazine articles across 20 countries, to identify nine common synonyms of "older adults" and compiled their most frequently used descriptors (collocates) from October 2019 to May 2020-culminating in 11,504 collocates that were rated to create a Cumulative Aging Narrative Score per month. Widely used cultural dimension scores were taken from Hofstede, and pandemic variables, from the Oxford COVID-19 Government Response Tracker. RESULTS: Aging narratives became more negative as the pandemic worsened across 20 countries. Globally, scores were trending neutral from October 2019 to February 2020, and plummeted in March 2020, reflecting COVID-19's severity. Prepandemic (October 2019), the United Kingdom evidenced the most negative aging narratives; peak pandemic (May 2020), South Africa took on the dubious honor. Across the 8-month period, the Philippines experienced the steepest trend toward negativity in aging narratives. Ageism, during the pandemic, was, ironically, not predicted by COVID-19's incidence and mortality rates, but by cultural variables: Individualism, Masculinity, Uncertainty Avoidance, and Long-term Orientation. DISCUSSION: The strategy to reverse this trajectory lay in the same phenomenon that promoted it: a sustained global campaign-though, it should be culturally nuanced and customized to a country's context.

COVID19 Drug Repository: text-mining the literature in search of putative COVID19 therapeutics.

The recent outbreak of COVID-19 has generated an enormous amount of Big Data. To date, the COVID-19 Open Research Dataset (CORD-19), lists ∼130,000 articles from the WHO COVID-19 database, PubMed Central, medRxiv, and bioRxiv, as collected by Semantic Scholar. According to LitCovid (11 August 2020), ∼40,300 COVID19-related articles are currently listed in PubMed. It has been shown in clinical settings that the analysis of past research results and the mining of available data can provide novel opportunities for the successful application of currently approved therapeutics and their combinations for the treatment of conditions caused by a novel SARS-CoV-2 infection. As such, effective responses to the pandemic require the development of efficient applications, methods and algorithms for data navigation, text-mining, clustering, classification, analysis, and reasoning. Thus, our COVID19 Drug Repository represents a modular platform for drug data navigation and analysis, with an emphasis on COVID-19-related information currently being reported. The COVID19 Drug Repository enables users to focus on different levels of complexity, starting from general information about (FDA-) approved drugs, PubMed references, clinical trials, recipes as well as the descriptions of molecular mechanisms of drugs' action. Our COVID19 drug repository provide a most updated world-wide collection of drugs that has been repurposed for COVID19 treatments around the world.

LitCovid: an open database of COVID-19 literature.

Since the outbreak of the current pandemic in 2020, there has been a rapid growth of published articles on COVID-19 and SARS-CoV-2, with about 10,000 new articles added each month. This is causing an increasingly serious information overload, making it difficult for scientists, healthcare professionals and the general public to remain up to date on the latest SARS-CoV-2 and COVID-19 research. Hence, we developed LitCovid (https://www.ncbi.nlm.nih.gov/research/coronavirus/), a curated literature hub, to track up-to-date scientific information in PubMed. LitCovid is updated daily with newly identified relevant articles organized into curated categories. To support manual curation, advanced machine-learning and deep-learning algorithms have been developed, evaluated and integrated into the curation workflow. To the best of our knowledge, LitCovid is the first-of-its-kind COVID-19-specific literature resource, with all of its collected articles and curated data freely available. Since its release, LitCovid has been widely used, with millions of accesses by users worldwide for various information needs, such as evidence synthesis, drug discovery and text and data mining, among others.

Europe PMC in 2020.

Europe PMC (https://europepmc.org) is a database of research articles, including peer reviewed full text articles and abstracts, and preprints - all freely available for use via website, APIs and bulk download. This article outlines new developments since 2017 where work has focussed on three key areas: (i) Europe PMC has added to its core content to include life science preprint abstracts and a special collection of full text of COVID-19-related preprints. Europe PMC is unique as an aggregator of biomedical preprints alongside peer-reviewed articles, with over 180 000 preprints available to search. (ii) Europe PMC has significantly expanded its links to content related to the publications, such as links to Unpaywall, providing wider access to full text, preprint peer-review platforms, all major curated data resources in the life sciences, and experimental protocols. The redesigned Europe PMC website features the PubMed abstract and corresponding PMC full text merged into one article page; there is more evident and user-friendly navigation within articles and to related content, plus a figure browse feature. (iii) The expanded annotations platform offers ∼1.3 billion text mined biological terms and concepts sourced from 10 providers and over 40 global data resources.

Database Resources of the National Genomics Data Center, China National Center for Bioinformation in 2021.

The National Genomics Data Center (NGDC), part of the China National Center for Bioinformation (CNCB), provides a suite of database resources to support worldwide research activities in both academia and industry. With the explosive growth of multi-omics data, CNCB-NGDC is continually expanding, updating and enriching its core database resources through big data deposition, integration and translation. In the past year, considerable efforts have been devoted to 2019nCoVR, a newly established resource providing a global landscape of SARS-CoV-2 genomic sequences, variants, and haplotypes, as well as Aging Atlas, BrainBase, GTDB (Glycosyltransferases Database), LncExpDB, and TransCirc (Translation potential for circular RNAs). Meanwhile, a series of resources have been updated and improved, including BioProject, BioSample, GWH (Genome Warehouse), GVM (Genome Variation Map), GEN (Gene Expression Nebulas) as well as several biodiversity and plant resources. Particularly, BIG Search, a scalable, one-stop, cross-database search engine, has been significantly updated by providing easy access to a large number of internal and external biological resources from CNCB-NGDC, our partners, EBI and NCBI. All of these resources along with their services are publicly accessible at https://bigd.big.ac.cn.