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This dataset provides access to the metadata records of publications, research data, software and projects that may be relevant to the Corona Virus Disease (COVID-19) fight. The dataset contains the OpenAIRE COVID-19 Gateway records, identified via full-text mining and inference techniques applied to the OpenAIRE Graph. The OpenAIRE Graph is one of the largest Open Access collections of metadata records and links between publications, datasets, software, projects, funders, and organizations, aggregating 12,000+ scientific data sources world-wide, among which the Covid-19 data sources Zenodo COVID-19 Community, WHO (World Health Organization), BIP! FInder for COVID-19, Protein Data Bank, Dimensions, scienceOpen, and RSNA. The dataset consists of a tar archive containing gzip files with one json per line. Each json is compliant to the schema available at https://doi.org/10.5281/zenodo.8238913.

Database COVID

Background: Acute diarrhea is one of the most serious problems in global public health that causes considerable morbidity and mortality worldwide. Human caliciviruses (HuCV) including norovirus (NoV, genogroup GI and GII) and sapovirus (SaV), is a leading cause of acute sporadic diarrhea in individuals across all age groups. However, few studies had been conducted clarifying the characteristics of HuCV in diarrhea cases across all age groups in China. Our study was aimed at assessing the HuCV-related diarrhea burden and NoV genotypes distribution in southwest China. Methods: The study was conducted in four hospitals in Kunming city, Yunnan province, from June 2014 to July 2015. Stool specimens were collected from 1,121 diarrhea cases and 319 healthy controls in outpatient departments. Reverse transcription polymerase chain reaction (RT-PCR) was used to detect NoV (GI, GII) and SaV. Sequencing was applied to confirm the three viral infections and phylogenetic analysis was performed to determine their genotypes. A structured questionnaire was used to record the demographic information and clinical symptoms of subjects. Results: HuCV was detected at an 11.0 % infection rate in 1,121 diarrhea cases and at 3.4 % rate in 319 non-diarrhea subjects (p < 0.0001, OR = 3.5, 95 % CI 1.8–6.5). The prevalence of the NoV genogroup GII and genotype GII.4 in diarrhea cases was significantly higher than that found in healthy controls (p < 0.0001, p = 0.018, respectively). NoV GII (n = 118, 10.5 %) was the most common HuCV subtype in diarrhea cases, followed by SaV (n = 3, 0.3 %) and NoV GI (n = 2, 0.2 %). Of 118 NoV GII strains isolated from diarrhea patients. GII.4 (n = 55, 46.6 %) was the predominant strain, followed by GII.3 (n = 28, 23.7 %), GII.12 (n = 25, 21.2 %), GII.17 (n = 8, 6.8 %), and GII.5 (n = 2, 1.7 %). Of the 55 GII.4 strains, the GII.4 Sydney 2012 variant had absolutely predominant prevalence (n = 52, 94.5 %), followed by the NoV GII.4-2006b variant (n = 3, 5.5 %). The GII.4 Orleans 2009 variant was not found in diarrhea cases of the study. Conclusions: NoV GII was the major genogroup and GII.4 was the most predominant strain detected in diarrhea patients. The GII.17 is an emergent variant in sporadic diarrhea and might become the predominant strain in diarrhea cases in the near future. Rapid, accurate detection kits need to be developed to help us find and treat NoV-associated diarrhea in clinical settings in a timely manner. Figure 1Phylogenetic trees of SaV and NoV(GI, GII) based on dependent RNA polymerase ●: diarrhea cases over five years; ○: controls over five years; ▲: diarrhea cases under five years; △: controls under five years; The contents in brackets is that the number of same genotype The molecular analysis of SaV and NoV (GI, GII) among diarrhea cases and healthy controls in this study, NoV GII was the major pathogen and GII.4 was the most predominant strain detected in diarrhea patients, novel GII.17 variant was emergence in diarrhea cases.Phylogenetic trees of SaV and NoV(GI, GII)The representative gene sequences

In the course of our PubMed searches and preprints from MedRxiv, we identified a number of protocols for RCTs on preventive measures and treatments for Covid-19. This file is updated regularly.

As of December 2020, COVID-19 has spread all over the world with more than 81 million cases and more than 1.8 million deaths. The rapidly increasing number of patients mandates the consideration of potential treatments for patients under severe and critical conditions. Convalescent plasma (CP) treatment refers to the approach of infusing patients with plasma from recently recovered patients. CP appears to be a possible therapeutic option to manage patients suffering from severe or even lethal infectious disorders, in which “traditional therapies” have failed to obtain any result. In the present study, we develop a mathematical model on the treatment-donation-stockpile dynamics for an optimal implementation of CP therapy to examine potential benefits and complications in the logistic realization of this therapy in a large-scale population. We parameterize the model with COVID-19 epidemics in Italy, and conduct scenario analyses to estimate outcomes of population-wide CP therapy and to examine the maximum number of CP donation processions per day. Under the assumption that the efficacy of CP is 90%, we show that by the end of year 2020, initiating the population-wide CP therapy from April 2020 can save as many as 19,215 lives (ranging from 5000 - 28,000 depending on donor availability), while the demand for apheresis use is manageable in all scenarios: the maximum daily demand is 156 (ranging from 27 - 519 depending on donor availability) for the first outbreak wave and 1,434 (ranging from 224 - 4,817 depending on donor availability) for the second wave. Given that Italy has 61 centers with apheresis this maximum demand level corresponds to a daily average of 2.5 and 23.5 processions of CP donation being performed by each center with respect to each outbreak wave. Our analyses show that population-wide CP therapy can contribute to curbing COVID-19 related deaths, and the logistic implementation is feasible for developed countries. The reduction of deaths can be very significant if the CP therapy is started earlier at the outbreak, and remains significant even if it is implemented during the outbreak peak time.

This viewpoint note discusses the covid-19 pandemic from the lens of complexity thinking and resilience engineering (RE). It intends to raise questions and encourage critical thinking on the underlying theoretical foundations of the responses adopted so far to cope with the pandemic. Insights arising from this analysis can be useful for the refinement of complexity and RE theory and practice, as well as for the further development of non-medical practices to address the pandemic and its effects.

Introduction The COVID-19 pandemic may have led to an increase in the alcohol-specific mortality. Against this backdrop, the aim of this report is to explore alcohol-specific mortality trends in Germany of the years 2010 to 2020. Method Alcohol-specific mortality data aggregated by sex, 5-year age groups and state were collected from the annual cause-of-death statistics and analysed descriptively by visual inspection. Results The overall alcohol-specific mortality rate (age-standardised) has mainly decreased between 2010 and 2020. However, increased alcohol-specific mortality rates for the year 2020 compared to 2019 were found for both, women (+4.8%) and men (+5.5%), particularly in age groups between 40 and 69 years. Changes in alcohol-specific mortality rates differed between federated states, with steeper increases in East Germany. Discussion and Conclusions Different mechanisms related to the increase in alcohol consumption, particularly among high-risk drinkers, and reduced resources in health care may have led to an increase in alcohol-specific mortality in Germany in 2020. Despite the recent decline in the alcohol-specific mortality in Germany, an increase in the death toll was observed in 2020.