Implication of Covid-19 on Neurological Complications with Specific Emphasis on Alzheimer's and Parkinson's Disease.

It is noticeable how the novel coronavirus has spread from the Wuhan region of China to the whole world, devastating the lives of people worldwide. All the data related to the precautionary measures, diagnosis, treatment, and even the epidemiological data are being made freely accessible and reachable in a very little time as well as being rapidly published to save humankind from this pandemic. There might be neurological complications of COVID-19 and patients suffering from neurodegenerative conditions like Alzheimer's disease and Parkinson's disease might have repercussions as a result of the pandemic. In this review article, we have discussed the effect of SARS-CoV-2 viral infection on the people affected with neurodegenerative disorders such as Parkinson's and Alzheimer's. It primarily emphasizes two issues, i.e., vulnerability to infection and modifications of course of the disease concerning the clinical neurological manifestations, the advancement of the disease and novel approaches to support health care professionals in disease management, the susceptibility to these diseases, and impact on the severity of disease and management.

Using a Secure, Continually Updating, Web Source Processing Pipeline to Support the Real-Time Data Synthesis and Analysis of Scientific Literature: Development and Validation Study.

BACKGROUND: The 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. OBJECTIVE: The 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. METHODS: To 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. RESULTS: REDASA (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. CONCLUSIONS: This 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.