RESUMEN
In automated scientific fact-checking, machine learning models are trained to verify scientific claims given evidence. A major bottleneck of this task is the availability of large-scale training datasets on different domains, due to the required domain expertise for data annotation. However, multiple-choice question-answering datasets are readily available across many different domains, thanks to the modern online education and assessment systems. As one of the first steps towards addressing the fact-checking dataset scarcity problem in scientific domains, we propose a pipeline for automatically converting multiple-choice questions into fact-checking data, which we call Multi2Claim. By applying the proposed pipeline, we generated two large-scale datasets for scientific-fact-checking: Med-Fact and Gsci-Fact for the medical and general science domains, respectively. These two datasets are among the first examples of large-scale scientific-fact-checking datasets. We developed baseline models for the verdict prediction task using each dataset. Additionally, we demonstrated that the datasets could be used to improve performance measured by weighted F1 on existing fact-checking datasets such as SciFact, HEALTHVER, COVID-Fact, and CLIMATE-FEVER. In some cases, the improvement in performance was up to a 26% increase. The generated datasets are publicly available. © 2023 Association for Computational Linguistics.
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The spread of COVID-19 has increased the risk of contact transmission between people, and imposes challenges and opportunities for the prevention and control of important parasitic diseases. There may be risks of COVID-19 infection during the implementation of the virus control, including in field investigation and intervention, contacting with fecal samples, or with asymptomatic infected people. Therefore, corresponding prevention and control measures should be taken before and during field investigation, and in sample transportation and detection to prevent the risks effectively. In this paper, the risks and corresponding countermeasures in the above processes are discussed in detail, and the opportunities that COVID-19 brought to the disease control system are also analyzed.Copyright © 2021, National Institute of Parasitic Diseases. All rights reserved.
RESUMEN
Glossopharyngeal neuralgia (GPN) is a painful condition characterized by stabbing pain throughout the glossopharyngeal nerve distribution. Since the beginning of the coronavirus disease 2019 (COVID-19) pandemic, we have learned that COVID-19 may induce neurological symptoms and complications. This case report presents a 54-year-old patient diagnosed with GPN, potentially secondary to COVID-19. The pain resolved spontaneously in three months without the need for medication. We discuss our diagnostic approach for this patient and propose a possible theory about the relation between cranial neuralgias and COVID-19.
RESUMEN
Involved in the impending era of 'Internet + ' and big data, educational informatization has been driving the modernization of education and promoting the deep integration of information technology and teaching. The 'Fundamentals of College Computer' course is a public compulsory course for medical colleges. In order to meet the professional requirements of medical colleges, benefitting from the massive online teaching resources during the novel coronavirus epidemic, this paper applies the teaching paradigm of 'online preview + offline teaching + online afterschool test + offline computer assessment' to exploit the advantages of online and offline hybrid teaching. The teaching practice has showed the establishment of the compound hybrid teaching model under medical big data background is an effective exploration to facilitate the development of medical information. © 2021 IEEE.
RESUMEN
There is a pressing need for effective therapeutics for coronavirus disease 2019 (COVID-19), the respiratory disease caused by severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) virus. The process of drug development is a costly and meticulously paced process, where progress is often hindered by the failure of initially promising leads. To aid this challenge, in vitro human microphysiological systems need to be refined and adapted for mechanistic studies and drug screening, thereby saving valuable time and resources during a pandemic crisis. The SARS-CoV-2 virus attacks the lung, an organ where the unique three-dimensional (3D) structure of its functional units is critical for proper respiratory function. The in vitro lung models essentially recapitulate the distinct tissue structure and the dynamic mechanical and biological interactions between different cell types. Current model systems include Transwell, organoid and organ-on-a-chip or microphysiological systems (MPSs). We review models that have direct relevance toward modeling the pathology of COVID-19, including the processes of inflammation, edema, coagulation, as well as lung immune function. We also consider the practical issues that may influence the design and fabrication of MPS. The role of lung MPS is addressed in the context of multi-organ models, and it is discussed how high-throughput screening and artificial intelligence can be integrated with lung MPS to accelerate drug development for COVID-19 and other infectious diseases.