Reconstructing the early global dynamics of under-ascertained COVID-19 cases and infections.

BACKGROUND: Asymptomatic or subclinical SARS-CoV-2 infections are often unreported, which means that confirmed case counts may not accurately reflect underlying epidemic dynamics. Understanding the level of ascertainment (the ratio of confirmed symptomatic cases to the true number of symptomatic individuals) and undetected epidemic progression is crucial to informing COVID-19 response planning, including the introduction and relaxation of control measures. Estimating case ascertainment over time allows for accurate estimates of specific outcomes such as seroprevalence, which is essential for planning control measures. METHODS: Using reported data on COVID-19 cases and fatalities globally, we estimated the proportion of symptomatic cases (i.e. any person with any of fever ≥ 37.5 °C, cough, shortness of breath, sudden onset of anosmia, ageusia or dysgeusia illness) that were reported in 210 countries and territories, given those countries had experienced more than ten deaths. We used published estimates of the baseline case fatality ratio (CFR), which was adjusted for delays and under-ascertainment, then calculated the ratio of this baseline CFR to an estimated local delay-adjusted CFR to estimate the level of under-ascertainment in a particular location. We then fit a Bayesian Gaussian process model to estimate the temporal pattern of under-ascertainment. RESULTS: Based on reported cases and deaths, we estimated that, during March 2020, the median percentage of symptomatic cases detected across the 84 countries which experienced more than ten deaths ranged from 2.4% (Bangladesh) to 100% (Chile). Across the ten countries with the highest number of total confirmed cases as of 6 July 2020, we estimated that the peak number of symptomatic cases ranged from 1.4 times (Chile) to 18 times (France) larger than reported. Comparing our model with national and regional seroprevalence data where available, we find that our estimates are consistent with observed values. Finally, we estimated seroprevalence for each country. As of 7 June, our seroprevalence estimates range from 0% (many countries) to 13% (95% CrI 5.6-24%) (Belgium). CONCLUSIONS: We found substantial under-ascertainment of symptomatic cases, particularly at the peak of the first wave of the SARS-CoV-2 pandemic, in many countries. Reported case counts will therefore likely underestimate the rate of outbreak growth initially and underestimate the decline in the later stages of an epidemic. Although there was considerable under-reporting in many locations, our estimates were consistent with emerging serological data, suggesting that the proportion of each country's population infected with SARS-CoV-2 worldwide is generally low.

Hydrophilicity, the major determining factor influencing the solvation environment of protic ionic liquids.

The study of solubility of 4-nitroaniline and its N,N-diethyl derivative in ionic liquids (ILs) of varying water content has shown that it is the interaction between water and the ILs' ions, and not with the solute, that is the most important factor determining the solute's preferential hydration parameter and thus the solvation shell structure.

The NCI Thesaurus quality assurance life cycle.

The National Cancer Institute Enterprise Vocabulary Services (NCI EVS) uses a wide range of quality assurance (QA) techniques to maintain and extend NCI Thesaurus (NCIt). NCIt is a reference terminology and biomedical ontology used in a growing number of NCI and other systems that extend from translational and basic research through clinical care to public information and administrative activities. Both automated and manual QA techniques are employed throughout the editing and publication cycle, which includes inserting and editing NCIt in NCI Metathesaurus. NCI EVS conducts its own additional periodic and ongoing content QA. External reviews, and extensive evaluation by and interaction with EVS partners and other users, have also played an important part in the QA process. There have always been tensions and compromises between meeting the needs of dependent systems and providing consistent and well-structured content; external QA and feedback have been important in identifying and addressing such issues. Currently, NCI EVS is exploring new approaches to broaden external participation in the terminology development and QA process.

Unexpected preferential dehydration of artemisinin in ionic liquids.

Thermodynamic measurements (at 298 K) reveal that a crucial step in the extraction process of the key antimalarial drug artemisinin by ionic liquids (ILs), namely, precipitation through the addition of water, is driven by artemisinin dehydration due to the differences in the water's interaction with the bulk ILs, rather than with the artemisinin itself.

NCI Thesaurus: a semantic model integrating cancer-related clinical and molecular information.

Over the last 8 years, the National Cancer Institute (NCI) has launched a major effort to integrate molecular and clinical cancer-related information within a unified biomedical informatics framework, with controlled terminology as its foundational layer. The NCI Thesaurus is the reference terminology underpinning these efforts. It is designed to meet the growing need for accurate, comprehensive, and shared terminology, covering topics including: cancers, findings, drugs, therapies, anatomy, genes, pathways, cellular and subcellular processes, proteins, and experimental organisms. The NCI Thesaurus provides a partial model of how these things relate to each other, responding to actual user needs and implemented in a deductive logic framework that can help maintain the integrity and extend the informational power of what is provided. This paper presents the semantic model for cancer diseases and its uses in integrating clinical and molecular knowledge, more briefly examines the models and uses for drug, biochemical pathway, and mouse terminology, and discusses limits of the current approach and directions for future work.

The CAP cancer protocols--a case study of caCORE based data standards implementation to integrate with the Cancer Biomedical Informatics Grid.

BACKGROUND: The Cancer Biomedical Informatics Grid (caBIG) is a network of individuals and institutions, creating a world wide web of cancer research. An important aspect of this informatics effort is the development of consistent practices for data standards development, using a multi-tier approach that facilitates semantic interoperability of systems. The semantic tiers include (1) information models, (2) common data elements, and (3) controlled terminologies and ontologies. The College of American Pathologists (CAP) cancer protocols and checklists are an important reporting standard in pathology, for which no complete electronic data standard is currently available. METHODS: In this manuscript, we provide a case study of Cancer Common Ontologic Representation Environment (caCORE) data standard implementation of the CAP cancer protocols and checklists model--an existing and complex paper based standard. We illustrate the basic principles, goals and methodology for developing caBIG models. RESULTS: Using this example, we describe the process required to develop the model, the technologies and data standards on which the process and models are based, and the results of the modeling effort. We address difficulties we encountered and modifications to caCORE that will address these problems. In addition, we describe four ongoing development projects that will use the emerging CAP data standards to achieve integration of tissue banking and laboratory information systems. CONCLUSION: The CAP cancer checklists can be used as the basis for an electronic data standard in pathology using the caBIG semantic modeling methodology.

The role of user input in shaping online information from the National Cancer Institute.

The National Cancer Institute (NCI) was among the first federal agencies to recognize the potential of the Internet for disseminating health-related information. The evolution and refinement of NCI's online cancer information has been substantially "user driven"-from the launch of CancerNet in 1995 to the recent redesign of its award-winning successor, the NCI website. This article presents an overview of NCI's multi-pronged approach to gathering input about its online information products, including stakeholder meetings, focus groups, standard and customized online user surveys, usability testing, heuristic reviews, and search log analysis. Also highlighted are some of the many enhancements that have been made to NCI's online cancer information products based on user input.

Personality reconsidered: a considered response.

The article by Hooker and McAdams (2003) challenges psychologists to rethink some of the theoretical distinctions used in everyday research. Although their theory aims to integrate approaches to gathering and understanding data, such an integration may or may not be all encompassing, or indeed, desirable. Nonetheless, the six foci provide a framework as valid as any for the exploration of personality across a lifetime.

NCI Thesaurus: using science-based terminology to integrate cancer research results.

Cancer researchers need to be able to organize and report their results in a way that others can find, build upon, and relate to the specific clinical conditions of individual patients. NCI Thesaurus is a description logic terminology based on current science that helps individuals and software applications connect and organize the results of cancer research, e.g., by disease and underlying biology. Currently containing some 34,000 concepts--covering chemicals, drugs and other therapies, diseases, genes and gene products, anatomy, organisms, animal models, techniques, biologic processes, and administrative categories--NCI Thesaurus serves applications and the Web from a terminology server. As a scalable, formal terminology, the deployed Thesaurus, and associated applications and interfaces, are a model for some of the standards required for the NHII (National Health Information Infrastructure) and the Semantic Web.