TY - JOUR A1 - Riederer, Peter A1 - ter Meulen, Volker T1 - Coronaviruses: a challenge of today and a call for extended human postmortem brain analyses JF - Journal of Neural Transmission N2 - While there is abounding literature on virus-induced pathology in general and coronavirus in particular, recent evidence accumulates showing distinct and deleterious brain affection. As the respiratory tract connects to the brain without protection of the blood–brain barrier, SARS-CoV-2 might in the early invasive phase attack the cardiorespiratory centres located in the medulla/pons areas, giving rise to disturbances of respiration and cardiac problems. Furthermore, brainstem regions are at risk to lose their functional integrity. Therefore, long-term neurological as well as psychiatric symptomatology and eventual respective disorders cannot be excluded as evidenced from influenza-A triggered post-encephalitic Parkinsonism and HIV-1 triggered AIDS–dementia complex. From the available evidences for coronavirus-induced brain pathology, this review concludes a number of unmet needs for further research strategies like human postmortem brain analyses. SARS-CoV-2 mirroring experimental animal brain studies, characterization of time-dependent and region-dependent spreading behaviours of coronaviruses, enlightening of pathological mechanisms after coronavirus infection using long-term animal models and clinical observations of patients having had COVID-19 infection are calling to develop both protective strategies and drug discoveries to avoid early and late coronavirus-induced functional brain disturbances, symptoms and eventually disorders. To fight SARS-CoV-2, it is an urgent need to enforce clinical, molecular biological, neurochemical and genetic research including brain-related studies on a worldwide harmonized basis. KW - coronavirus KW - COVID-19 KW - SARS-CoV-2 brain disorders KW - cardiorespiratory centre KW - brain pathology KW - neurological symptoms/disorders KW - brain stem KW - Parkinson’s disease KW - Parkinsonism KW - Alzheimer’s disease KW - multiple sclerosis KW - movement disorders KW - neuroinvasion KW - therapy KW - neuroprotection KW - depression KW - cognitive dysfunction KW - brain bank KW - postmortem studies Y1 - 2020 U6 - http://nbn-resolving.de/urn/resolver.pl?urn:nbn:de:bvb:20-opus-314637 SN - 0300-9564 SN - 1435-1463 VL - 127 IS - 9 ER - TY - JOUR A1 - Hesselbach, Robert T1 - <> – A Corpus-based Approach of Official French, Italian, and Spanish Social Media Discourse in the Light of the Coronavirus Crisis JF - promptus - Würzburger Beiträge zur Romanistik N2 - France, Italy, and Spain are three Romance-speaking countries which – at least in Europe – have been affected to a very high degree by the consequences of the Corona pandemic. This paper examines discursive strategies on social media (Twitter and Facebook) by the three heads of government/state of the aforementioned countries – namely Emmanuel Macron (France), Giuseppe Conte (Italy), and Pedro Sánchez (Spain)- from a corpuslinguistic point of view. For this purpose, a corpus was created which contains all Twitter and Facebook messages posted by these heads of government/state from the beginning of February until the end of April 2020. By applying corpus-linguistic methods we find that all three politicians consciously use social media to sensitize, inform, and – in view of a dramatic pandemic situation – unite their respective populations behind them. KW - corpus linguistics KW - coronavirus KW - Covid-19 KW - political discourse KW - social media KW - lexical co-occurrences Y1 - 2020 U6 - http://nbn-resolving.de/urn/resolver.pl?urn:nbn:de:bvb:20-opus-244251 VL - 6 ER - TY - JOUR A1 - Beierle, Felix A1 - Pryss, Rüdiger A1 - Aizawa, Akiko T1 - Sentiments about mental health on Twitter — before and during the COVID-19 pandemic JF - Healthcare N2 - During the COVID-19 pandemic, the novel coronavirus had an impact not only on public health but also on the mental health of the population. Public sentiment on mental health and depression is often captured only in small, survey-based studies, while work based on Twitter data often only looks at the period during the pandemic and does not make comparisons with the pre-pandemic situation. We collected tweets that included the hashtags #MentalHealth and #Depression from before and during the pandemic (8.5 months each). We used LDA (Latent Dirichlet Allocation) for topic modeling and LIWC, VADER, and NRC for sentiment analysis. We used three machine-learning classifiers to seek evidence regarding an automatically detectable change in tweets before vs. during the pandemic: (1) based on TF-IDF values, (2) based on the values from the sentiment libraries, (3) based on tweet content (deep-learning BERT classifier). Topic modeling revealed that Twitter users who explicitly used the hashtags #Depression and especially #MentalHealth did so to raise awareness. We observed an overall positive sentiment, and in tough times such as during the COVID-19 pandemic, tweets with #MentalHealth were often associated with gratitude. Among the three classification approaches, the BERT classifier showed the best performance, with an accuracy of 81% for #MentalHealth and 79% for #Depression. Although the data may have come from users familiar with mental health, these findings can help gauge public sentiment on the topic. The combination of (1) sentiment analysis, (2) topic modeling, and (3) tweet classification with machine learning proved useful in gaining comprehensive insight into public sentiment and could be applied to other data sources and topics. KW - COVID-19 KW - coronavirus KW - public health KW - sentiment analysis KW - topic modeling KW - machine learning Y1 - 2023 U6 - http://nbn-resolving.de/urn/resolver.pl?urn:nbn:de:bvb:20-opus-355192 SN - 2227-9032 VL - 11 IS - 21 ER -