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  • Open Access
    Authors: 
    Michel, Gisela;
    Publisher: Zenodo
    Country: Switzerland

    Abstract Childhood, adolescent, and young adult (CAYA) cancer survivors may be at risk for a severe course of COVID-19. Little is known about the clinical course of COVID-19 in CAYA cancer survivors, or if additional preventive measures are warranted. We established a working group within the International Late Effects of Childhood Cancer Guideline Harmonization Group (IGHG) to summarize existing evidence and worldwide recommendations regarding evidence about factors/conditions associated with risk for a severe course of COVID-19 in CAYA cancer survivors, and to develop a consensus statement to provide guidance for healthcare practitioners and CAYA cancer survivors regarding COVID-19. + ID der Publikation: unilu_50168 + Sprache: Englisch + Bemerkungen: doi: 10.1002/pbc.28702 + Letzte Aktualisierung: 2020-11-04 18:28:47

  • Open Access
    Authors: 
    Huber, Sebastian; Farrar Schmidhauser, Jillaine; Stolz, Ingo;
    Publisher: Zenodo
    Country: Switzerland

    Auf internationalen Märkten hinterlässt die COVID-19 Pandemie ein Kaleidoskop von Auswirkungen, neuen Rahmenbedingungen und Herausforderungen. Mit Blick nach vorne stellt sich für viele Unternehmen und ihre Mitarbeitenden im In- und Ausland die Frage, was davon ihr Geschäftsmodell nachhaltig verändern wird, und wie.

  • Open Access English
    Authors: 
    Matteo Pennisi; Isaak Kavasidis; Concetto Spampinato; Vincenzo Schininà; Simone Palazzo; Federica Proietto Salanitri; Giovanni Bellitto; Francesco Rundo; Marco Aldinucci; Massimo Cristofaro; +8 more
    Publisher: Published by Elsevier B.V.
    Country: Italy
    Project: EC | DeepHealth (825111), EC | DeepHealth (825111)

    COVID-19 infection caused by SARS-CoV-2 pathogen has been a catastrophic pandemic outbreak all over the world, with exponential increasing of confirmed cases and, unfortunately, deaths. In this work we propose an AI-powered pipeline, based on the deep-learning paradigm, for automated COVID-19 detection and lesion categorization from CT scans. We first propose a new segmentation module aimed at automatically identifying lung parenchyma and lobes. Next, we combine the segmentation network with classification networks for COVID-19 identification and lesion categorization. We compare the model's classification results with those obtained by three expert radiologists on a dataset of 166 CT scans. Results showed a sensitivity of 90.3% and a specificity of 93.5% for COVID-19 detection, at least on par with those yielded by the expert radiologists, and an average lesion categorization accuracy of about 84%. Moreover, a significant role is played by prior lung and lobe segmentation, that allowed us to enhance classification performance by over 6 percent points. The interpretation of the trained AI models reveals that the most significant areas for supporting the decision on COVID-19 identification are consistent with the lesions clinically associated to the virus, i.e., crazy paving, consolidation and ground glass. This means that the artificial models are able to discriminate a positive patient from a negative one (both controls and patients with interstitial pneumonia tested negative to COVID) by evaluating the presence of those lesions into CT scans. Finally, the AI models are integrated into a user-friendly GUI to support AI explainability for radiologists, which is publicly available at http://perceivelab.com/covid-ai. The whole AI system is unique since, to the best of our knowledge, it is the first AI-based software, publicly available, that attempts to explain to radiologists what information is used by AI methods for making decisions and that proactively involves them in the decision loop to further improve the COVID-19 understanding.

  • Open Access
    Authors: 
    Frei, Beat; Huber, Heinrich; Bienz, Moritz; Bucheli, Kevin;
    Publisher: Zenodo
    Country: Switzerland

    We report in this paper on the use of tracer gas methods and two tracer gases to determine ventilation effectiveness under COVID-19 conditions in a large concert hall in Lucerne, Switzerland. The occupancy of the concert hall was simulated by using thermal dummies and partial occupancy of people because of the COVID-19 protection regulations. Contaminants are removed very efficiently in the parquet (by factors better than with mixed ventilation). On the stage and balconies, the local ventilation effectiveness with displacement ventilation is partly comparable to mixed ventilation or even lower. The ventilation of balconies and galleries is demanding and must be carefully assessed in the case of pandemic risks. For the assessment of infection risk through aerosol transmission, a characteristic value for the entire room is not sufficient. The ventilation effectiveness and contaminant removal effectiveness depend very strongly on local boundary conditions and the prevailing flow conditions when dosed locally. The investigations show that the tracer gases sulphur hexafluoride (SF6) and 2,3,3,3-tetrafluoropropene (R1234yf) provide comparable results in determining the air exchange rate and ventilation effectiveness. With both tracer methods, it is possible to gain knowledge about the operation of the ventilation system (e.g. volume air flows, heat recovery leakage). CLIMA 2022 conference, 2022: CLIMA 2022 The 14th REHVA HVAC World Congress

  • Open Access English
    Authors: 
    Martin Müller; Marcel Salathé; Per E Kummervold;
    Project: EC | VACMA (797876), EC | VEO (874735), EC | VACMA (797876), EC | VEO (874735)

    In this work, we release COVID-Twitter-BERT (CT-BERT), a transformer-based model, pretrained on a large corpus of Twitter messages on the topic of COVID-19. Our model shows a 10-30% marginal improvement compared to its base model, BERT-Large, on five different classification datasets. The largest improvements are on the target domain. Pretrained transformer models, such as CT-BERT, are trained on a specific target domain and can be used for a wide variety of natural language processing tasks, including classification, question-answering and chatbots. CT-BERT is optimised to be used on COVID-19 content, in particular social media posts from Twitter.

  • Open Access
    Authors: 
    Jennifer C E Lane; James Weaver; Kristin Kostka; Talita Duarte-Salles; Maria Tereza Fernandes Abrahão; Heba Alghoul; Osaid Alser; Thamir M. Alshammari; Carlos Areia; Patricia Biedermann; +38 more
    Publisher: Oxford University Press (OUP)
    Countries: Denmark, United States, United Kingdom, Croatia, Netherlands, Croatia, Netherlands, United Kingdom, Denmark
    Project: EC | EHDEN (806968), EC | EHDEN (806968)

    Author(s): Lane, Jennifer CE; Weaver, James; Kostka, Kristin; Duarte-Salles, Talita; Abrahao, Maria Tereza F; Alghoul, Heba; Alser, Osaid; Alshammari, Thamir M; Areia, Carlos; Biedermann, Patricia; Banda, Juan M; Burn, Edward; Casajust, Paula; Fister, Kristina; Hardin, Jill; Hester, Laura; Hripcsak, George; Kaas-Hansen, Benjamin Skov; Khosla, Sajan; Kolovos, Spyros; Lynch, Kristine E; Makadia, Rupa; Mehta, Paras P; Morales, Daniel R; Morgan-Stewart, Henry; Mosseveld, Mees; Newby, Danielle; Nyberg, Fredrik; Ostropolets, Anna; Woong Park, Rae; Prats-Uribe, Albert; Rao, Gowtham A; Reich, Christian; Rijnbeek, Peter; Sena, Anthony G; Shoaibi, Azza; Spotnitz, Matthew; Subbian, Vignesh; Suchard, Marc A; Vizcaya, David; Wen, Haini; Wilde, Marcel de; Xie, Junqing; You, Seng Chan; Zhang, Lin; Lovestone, Simon; Ryan, Patrick; Prieto-Alhambra, Daniel; OHDSI-COVID-19 consortium | Abstract: ObjectivesConcern has been raised in the rheumatology community regarding recent regulatory warnings that HCQ used in the coronavirus disease 2019 pandemic could cause acute psychiatric events. We aimed to study whether there is risk of incident depression, suicidal ideation or psychosis associated with HCQ as used for RA.MethodsWe performed a new-user cohort study using claims and electronic medical records from 10 sources and 3 countries (Germany, UK and USA). RA patients ≥18 years of age and initiating HCQ were compared with those initiating SSZ (active comparator) and followed up in the short (30 days) and long term (on treatment). Study outcomes included depression, suicide/suicidal ideation and hospitalization for psychosis. Propensity score stratification and calibration using negative control outcomes were used to address confounding. Cox models were fitted to estimate database-specific calibrated hazard ratios (HRs), with estimates pooled where I2 l40%.ResultsA total of 918 144 and 290 383 users of HCQ and SSZ, respectively, were included. No consistent risk of psychiatric events was observed with short-term HCQ (compared with SSZ) use, with meta-analytic HRs of 0.96 (95% CI 0.79, 1.16) for depression, 0.94 (95% CI 0.49, 1.77) for suicide/suicidal ideation and 1.03 (95% CI 0.66, 1.60) for psychosis. No consistent long-term risk was seen, with meta-analytic HRs of 0.94 (95% CI 0.71, 1.26) for depression, 0.77 (95% CI 0.56, 1.07) for suicide/suicidal ideation and 0.99 (95% CI 0.72, 1.35) for psychosis.ConclusionHCQ as used to treat RA does not appear to increase the risk of depression, suicide/suicidal ideation or psychosis compared with SSZ. No effects were seen in the short or long term. Use at a higher dose or for different indications needs further investigation.Trial registrationRegistered with EU PAS (reference no. EUPAS34497; http://www.encepp.eu/encepp/viewResource.htm? id=34498). The full study protocol and analysis source code can be found at https://github.com/ohdsi-studies/Covid19EstimationHydroxychloroquine2.

  • Open Access
    Authors: 
    Damonti, Lauro; Kronenberg, Andreas; Marschall, Jonas; Jent, Philipp; Sommerstein, Rami; De Kraker, Marlieke E. A.; Harbarth, Stephan; Gasser, Michael; Buetti, Niccol��;
    Publisher: Zenodo
    Country: Switzerland
    Project: EC | COMBACTE-CARE (115620), EC | COMBACTE-MAGNET (115737), EC | COMBACTE-NET (115523), EC | COMBACTE-CARE (115620), EC | COMBACTE-MAGNET (115737), EC | COMBACTE-NET (115523)

    Background Evidence about the impact of the pandemic of COVID-19 on the incidence rates of blood cultures contaminations and bloodstream infections in intensive care units (ICUs) remains scant. The objective of this study was to investigate the nationwide epidemiology of positive blood cultures drawn in ICUs during the first two pandemic waves of COVID-19 in Switzerland. Methods We analyzed data on positive blood cultures among ICU patients, prospectively collected through a nationwide surveillance system (ANRESIS), from March 30, 2020, to May 31, 2021, a 14-month timeframe that included a first wave of COVID-19, which affected the French and Italian-speaking regions, an interim period (summer 2020) and a second wave that affected the entire country. We used the number of ICU patient-days provided by the Swiss Federal Office of Public Health as denominator to calculate incidence rates of blood culture contaminations and bloodstream infections (ICU-BSI). Incidence rate ratios comparing the interim period with the second wave were determined by segmented Poisson regression models. Results A total of 1099 blood culture contaminations and 1616 ICU-BSIs were identified in 52 ICUs during the study. Overall, more episodes of blood culture contaminations and ICU-BSI were observed during the pandemic waves, compared to the interim period. The proportions of blood culture contaminations and ICU-BSI were positively associated with the ICU occupancy rate, which was higher during the COVID-19 waves. During the more representative second wave (versus interim period), we observed an increased incidence of blood culture contaminations (IRR 1.57, 95% CI 1.16–2.12) and ICU-BSI (IRR 1.20, 95% CI 1.03–1.39). Conclusions An increase in blood culture contaminations and ICU-BSIs was observed during the second COVID-19 pandemic wave, especially in months when the ICU burden of COVID-19 patients was high. Supplementary Information The online version contains supplementary material available at 10.1186/s13054-021-03814-z.

  • Publication . Conference object . Other literature type . 2021
    Open Access
    Authors: 
    Wenger, Ines; Morgenthaler, Thomas; Schulze, Christina;
    Publisher: Zenodo
    Country: Switzerland
    Project: EC | P4PLAY (861257), EC | P4PLAY (861257)

    Background: Children with and without disabilities consider playgrounds as significant places in their life, which offer opportunities for play and social interactions. Playgrounds are especially important in times when opportunities to meet are restricted, for example, due to the Covid-19 pandemic. Even though the primary purpose of playgrounds is to be a place for children to play, playground design is mainly determined by adults. Children`s voices regarding their use of playgrounds, and their wishes and needs to enhance participation on playgrounds are seldom addressed. In particularly perspectives of children with disabilities are often absent. The aim of this presentation is to give insight into how the perspectives of children with disabilities may contribute to playground design. Method: The presentation draws on an international project that explores children's perspectives through literature reviews and semi-structured interviews. Results: Children make important contributions to playground design. For example, children with disabilities show a unique understanding of their own situation and propose inclusive playground adjustments. Their unique perspectives on play could help playground providers to adapt playgrounds to their needs and wishes to ensure participation in play including opportunities for social interactions and challenges according to their abilities. Discussion: Children are the main user group of playgrounds. Child-focused participatory methods that include children with different abilities and a variety of needs and backgrounds should be included in processes of playground planning, evaluating, and monitoring to enhance playground design. This project has received funding from the European Union & Horizon 2020 research and innovation programme under the Marie Skłodowska-Curie grant agreement No 861257.

  • Open Access
    Authors: 
    van de Ven, Dirk-Jan; Nikas, Alexandros; Koasidis, Konstantinos; Forouli, Aikaterini; Cassetti, Gabriele; Chiodi, Alessandro; Gargiulo, Maurizio; Giarola, Sara; Köberle, Alexandre C.; Koutsellis, Themistoklis; +6 more
    Country: Switzerland
    Project: EC | PARIS REINFORCE (820846)

    To meet the Paris temperature targets and recover from the effects of the pandemic, many countries have launched economic recovery plans, including specific elements to promote clean energy technologies and green jobs. However, how to successfully manage investment portfolios of green recovery packages to optimize both climate mitigation and employment benefits remains unclear. Here, we use three energy-economic models, combined with a portfolio analysis approach, to find optimal low-carbon technology subsidy combinations in six major emitting regions: Canada, China, the European Union (EU), India, Japan, and the United States (US). We find that, although numerical estimates differ given different model structures, results consistently show that a >50% investment in solar photovoltaics is more likely to enable CO2 emissions reduction and green jobs, particularly in the EU and China. Our study illustrates the importance of strategically managing investment portfolios in recovery packages to enable optimal outcomes and foster a post-pandemic green economy.

  • Open Access
    Authors: 
    Orlandic, Lara; Teijeiro, Tomas; Atienza, David;
    Publisher: Zenodo
    Country: Switzerland
    Project: EC | DeepHealth (825111), EC | DeepHealth (825111)

    Cough audio signal classification has been successfully used to diagnose a variety of respiratory conditions, and there has been significant interest in leveraging Machine Learning (ML) to provide widespread COVID-19 screening. However, there is currently no validated database of cough sounds with which to train such ML models. The COUGHVID dataset provides over 20,000 crowdsourced cough recordings representing a wide range of subject ages, genders, geographic locations, and COVID-19 statuses. First, we filtered the dataset using our open-sourced cough detection algorithm. Second, experienced pulmonologists labeled more than 2,000 recordings to diagnose medical abnormalities present in the coughs, thereby contributing one of the largest expert-labeled cough datasets in existence that can be used for a plethora of cough audio classification tasks. Finally, we ensured that coughs labeled as symptomatic and COVID-19 originate from countries with high infection rates, and that their expert labels are consistent. As a result, the COUGHVID dataset contributes a wealth of cough recordings for training ML models to address the world's most urgent health crises. 11 pages, 3 figures