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Temporal knowledge graphs can be used to represent the current state of the world and, as daily events happen, the need to update the temporal knowledge graph, in order to stay consistent with the state of the world, becomes very important. However, there is currently no reliable method to accurately validate the update and evolution of knowledge graphs. There has been a recent development in text summarisation, whereby question answering is used to both guide and fact-check summarisation quality. The exact process can be applied to the temporal knowledge graph update process. To the best of our knowledge, there is currently no dataset that connects temporal knowledge graphs with documents with question–answer pairs. In this paper, we proposed the TKGQA dataset, consisting of over 5000 financial news documents related to M&A. Each document has extracted facts, question–answer pairs, and before and after temporal knowledge graphs, to highlight the state of temporal knowledge and any changes caused by the facts extracted from the document. As we parse through each document, we use question–answering to check and guide the update process of the temporal knowledge graph. Dataset: https://doi.org/10.17605/OSF.IO/XQWA4 Dataset License: CC BY 4.0 [ABSTRACT FROM AUTHOR]

BackgroundThe scale and quality of the global scientific response to the COVID-19 pandemic have unquestionably saved lives. However, the COVID-19 pandemic has also triggered an unprecedented “infodemic”; the velocity and volume of data production have overwhelmed many key stakeholders such as clinicians and policy makers, as they have been unable to process structured and unstructured data for evidence-based decision making. Solutions that aim to alleviate this data synthesis–related challenge are unable to capture heterogeneous web data in real time for the production of concomitant answers and are not based on the high-quality information in responses to a free-text query. ObjectiveThe main objective of this project is to build a generic, real-time, continuously updating curation platform that can support the data synthesis and analysis of a scientific literature framework. Our secondary objective is to validate this platform and the curation methodology for COVID-19–related medical literature by expanding the COVID-19 Open Research Dataset via the addition of new, unstructured data. MethodsTo create an infrastructure that addresses our objectives, the PanSurg Collaborative at Imperial College London has developed a unique data pipeline based on a web crawler extraction methodology. This data pipeline uses a novel curation methodology that adopts a human-in-the-loop approach for the characterization of quality, relevance, and key evidence across a range of scientific literature sources. ResultsREDASA (Realtime Data Synthesis and Analysis) is now one of the world’s largest and most up-to-date sources of COVID-19–related evidence; it consists of 104,000 documents. By capturing curators’ critical appraisal methodologies through the discrete labeling and rating of information, REDASA rapidly developed a foundational, pooled, data science data set of over 1400 articles in under 2 weeks. These articles provide COVID-19–related information and represent around 10% of all papers about COVID-19. ConclusionsThis data set can act as ground truth for the future implementation of a live, automated systematic review. The three benefits of REDASA’s design are as follows: (1) it adopts a user-friendly, human-in-the-loop methodology by embedding an efficient, user-friendly curation platform into a natural language processing search engine; (2) it provides a curated data set in the JavaScript Object Notation format for experienced academic reviewers’ critical appraisal choices and decision-making methodologies; and (3) due to the wide scope and depth of its web crawling method, REDASA has already captured one of the world’s largest COVID-19–related data corpora for searches and curation.

Obstructive sleep apnea syndrome (OSAS) represents a major health problem associated with serious long-term consequences, affecting 2-5% of men and 2% of women from the general population. The most important risk-factors that lead to this condition are obesity, age, male sex, craniofacial abnormalities, malocclusions, personal habits such as smoking and alcohol consumption, the use of sedative medications, endocrine disorders. The purpose of this literature review is to evaluate the relationship between craniofacial morphologies, malloclussions and the use of sedative madication and the development of obstructive sleep apnea.

ABSTRACT Basal cell carcinoma (BCC) arises and evolves through intricately mixed, intertwined actions between phenotypic, genetic and environmental factors. Despite being the preponderant type of neoplasia and its frequent location in the accessible and visible region of head and neck, it is commonly considered that a substantial number of BCC cases are diagnosed in the late stages, or even remain undiagnosed at all. Since a proper and timely discovery of all malignancies are key factors in the treatment and life prognostic of patients, a literature survey was made on BCCs to point out current and perspective elements of diagnosis that could be relevant in increasing the success rate of a clear, precise diagnostic. The cumulative use of non-invasive methods based on visual inspection, dermoscopy and other imagistic/computational tools is the first choice in early detection of BCC, while the biopsy followed by histopathological examination are recommended when reasonable uncertainties regarding the type and aggressivity of disease still remain unacknowledged after the initial follow-up.