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The following results are related to COVID-19. Are you interested to view more results? Visit OpenAIRE - Explore.
277 Research products, page 1 of 28

  • COVID-19
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  • COVID-19

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  • Closed Access English
    Authors: 
    Sequeiros, José; Pereira, Maria Teresa Ribeiro; Oliveira, Marisa; Ferreira, Fernanda A.;
    Country: Portugal

    Available studies and reports, as well as real-time experiences, attest to profound, extensive, long-lasting effects on the supply chain caused by the pandemic. It is thus paramount to rethink the supply chain for medication and health care supplies to build a more resilient and adaptable management process. Data obtained from the NHS portal monthly reports from January 2017 to August 2020 – indicates that at the hospital level the impact was felt the most on the scheduling of medical appointments and prescriptions. Two forecasting methods were selected for this study: Simple Linear Regression and Holt-Winters with the trend and additive seasonality. There was a change in the behavior of hospital patients in the first year of COVID-19 pandemics. This change was shown in two main indicators, the number of hospital consultations and medication expenses. This changed behavior resulted in a decrease in demand for scheduled hospital services, 1.9M less than would be expected under normal conditions, and a foreseeable increase of €432M in the consumption of medicines in the last quarter of 2020, €103M more than in the same period. from the previous year.

  • Closed Access
    Authors: 
    Massimo Bellato; Marco Cappellato;
    Publisher: Zenodo

    This is the repository containing the data and results shown in "Uncover a microbiota signature of upper respiratory tract in patients with SARS-CoV-2+" (2023).

  • Closed Access
    Authors: 
    Kufner, Verena; Frey, Andrea C.; Schmutz, Stefan; Zaheri, Maryam; Burkhard, Sara H.; Wiedmer, Caroline V.; Plate, Andreas; Trkola, Alexandra; Huber, Michael; Mueller, Nicolas J.;
    Publisher: Zenodo

    Illumina MiSeq Viral reads after quality passing and detection in zipped FASTQ format. Files are named by Patient's codes and whether DNA or RNA workflow is used for sample preparation.

  • Closed Access English
    Authors: 
    Matsumura Yasufumi;
    Publisher: Zenodo

    This dataset represent raw data for the unpublished article "Analytical and clinical performances of seven direct detection assays for SARS-CoV-2".

  • Closed Access English
    Authors: 
    Pierre, COLIAT;
    Publisher: Zenodo

    Raw dataset for "LEAF4L-6715 was administered to renal impairment patients undergoing dialysis who had acute respiratory distress syndrome due to COVID-19, sepsis or other causes."

  • Closed Access English
    Authors: 
    COLIAT Pierre;
    Publisher: Zenodo

    Raw dataset for "LEAF4L-6715 was administered to renal impairment patients undergoing dialysis who had acute respiratory distress syndrome due to COVID-19, sepsis or other causes."

  • Closed Access English
    Authors: 
    Sharma, Parvarish; Dhanjal, Daljeet Singh; Chopra, Chirag; Tambuwala, Murtaza M.; Sohal, Sukhwinder Singh; van der Spek, Peter J.; Sharma, Hari S.; Satija, Saurabh;

    Asthma, COPD, COVID-19, EGPA, Lung cancer, and Pneumonia are major chronic respiratory diseases (or CRDs) affecting millions worldwide and account for substantial morbidity and mortality. These CRDs are irreversible diseases that affect different parts of the respiratory system, imposing a considerable burden on different socio-economic classes. All these CRDs have been linked to increased eosinophils in the lungs. Eosinophils are essential immune mediators that contribute to tissue homeostasis and the pathophysiology of various diseases. Interestingly, elevated eosinophil level is associated with cellular processes that regulate airway hyperresponsiveness, airway remodeling, mucus hypersecretion, and inflammation in the lung. Therefore, eosinophil is considered the therapeutic target in eosinophil-mediated lung diseases. Although, conventional medicines like antibiotics, anti-inflammatory drugs, and bronchodilators are available to prevent CRDs. But the development of resistance to these therapeutic agents after long-term usage remains a challenge. However, progressive development in nanotechnology has unveiled the targeted nanocarrier approach that can significantly improve the pharmacokinetics of a therapeutic drug. The potential of the nanocarrier system can be specifically targeted on eosinophils and their associated components to obtain promising results in the pharmacotherapy of CRDs. This review intends to provide knowledge about eosinophils and their role in CRDs. Moreover, it also discusses nanocarrier drug delivery systems for the targeted treatment of CRDs.

  • Closed Access
    Authors: 
    Baran, Erdal;
    Publisher: Zenodo

    TweetsCOV19 is a semantically annotated corpus of Tweets about the COVID-19 pandemic. It is a subset of TweetsKB and aims at capturing online discourse about various aspects of the pandemic and its societal impact. Metadata information about the tweets as well as extracted entities, sentiments, hashtags, user mentions, and resolved URLs are exposed in RDF using established RDF/S vocabularies*. We also provide a tab-separated values (tsv) version of the dataset. Each line contains features of a tweet instance. Features are separated by tab character ("\t"). The following list indicate the feature indices: Tweet Id: Long. Username: String. Encrypted for privacy issues*. Timestamp: Format ( "EEE MMM dd HH:mm:ss Z yyyy" ). #Followers: Integer. #Friends: Integer. #Retweets: Integer. #Favorites: Integer. Entities: String. For each entity, we aggregated the original text, the annotated entity and the produced score from FEL library. Each entity is separated from another entity by char ";". Also, each entity is separated by char ":" in order to store "original_text:annotated_entity:score;". If FEL did not find any entities, we have stored "null;". Sentiment: String. SentiStrength produces a score for positive (1 to 5) and negative (-1 to -5) sentiment. We splitted these two numbers by whitespace char " ". Positive sentiment was stored first and then negative sentiment (i.e. "2 -1"). Mentions: String. If the tweet contains mentions, we remove the char "@" and concatenate the mentions with whitespace char " ". If no mentions appear, we have stored "null;". Hashtags: String. If the tweet contains hashtags, we remove the char "#" and concatenate the hashtags with whitespace char " ". If no hashtags appear, we have stored "null;". URLs: String: If the tweet contains URLs, we concatenate the URLs using ":-: ". If no URLs appear, we have stored "null;" To extract the dataset from TweetsKB, we compiled a seed list of 268 COVID-19-related keywords. * For the sake of privacy, we anonymize user IDs and we do not provide the text of the tweets.

  • Closed Access
    Authors: 
    Abduljabbar, Maram Hussen;
    Publisher: Zenodo

    Background: The Covid-19 (SARS-CoV-2) pandemic marked unprecedented on healthcare sector globally with increase demand of hospital beds, intensive care facilities, lifesaving supplies and equipments. This retrospective study of two healthcare centers focuses on length of hospital stay, clinical and therapeutic characteristics of patients with COVID-19. These parameters help to identify the available resources and to determine the optimum requirements of healthcare units in pandemics. Materials and Methods: The retrospective data was collected between 12th of March and 30th of Jun 2020 of polymerase chain reaction (PCR) confirming SARS-COV-2 patients and categorized into mild, moderate, and severe diseased groups based on symptoms and its severity of COVID 19. Results: A total of 843 SARS-COV-2 positive patients were identified for this study, 132 were mild symptomatic cases, 168, moderate symptomatic, and 17 were severe symptomatic with mean age of 34.95 ± 15.9 years. The mean length of hospital stay was 16.38, 13.18, 13.72, 9.30, 6.96, 10.86, 5.77 and 7.37 from 1 to 8 treatments consecutively, with 156.327 Chi-square, and 0.000* p-value. Kruskal-Wallis test shows there were significant differences seen between the length of hospital stay among different treatment groups with chi-square (7) = 156.327, with p-value of 0.000(p-value<0.005). Treatment group 1 who were on antiviral had the highest mean no. of hospital stay while treatment group 7 patients who were on no treatments had the lowest stay with mean duration of 5.7days. Conclusion: Length of hospital stay, clinical and therapeutic characteristics of patients with COVID-19 are crucial indicators of pandemic management. The lower length of hospital stay is a positive outcome of better Covid-19 management. Background: The Covid-19 (SARS-CoV-2) pandemic marked unprecedented on healthcare sector globally with increase demand of hospital beds, intensive care facilities, lifesaving supplies and equipments. This retrospective study of two healthcare centers focuses on length of hospital stay, clinical and therapeutic characteristics of patients with COVID-19. These parameters help to identify the available resources and to determine the optimum requirements of healthcare units in pandemics. Materials and Methods: The retrospective data was collected between 12th of March and 30th of Jun 2020 of polymerase chain reaction (PCR) confirming SARS-COV-2 patients and categorized into mild, moderate, and severe diseased groups based on symptoms and its severity of COVID 19. Results: A total of 843 SARS-COV-2 positive patients were identified for this study, 132 were mild symptomatic cases, 168, moderate symptomatic, and 17 were severe symptomatic with mean age of 34.95 ± 15.9 years. The mean length of hospital stay was 16.38, 13.18, 13.72, 9.30, 6.96, 10.86, 5.77 and 7.37 from 1 to 8 treatments consecutively, with 156.327 Chi-square, and 0.000* p-value. Kruskal-Wallis test shows there were significant differences seen between the length of hospital stay among different treatment groups with chi-square (7) = 156.327, with p-value of 0.000(p-value<0.005). Treatment group 1 who were on antiviral had the highest mean no. of hospital stay while treatment group 7 patients who were on no treatments had the lowest stay with mean duration of 5.7days. Conclusion: Length of hospital stay, clinical and therapeutic characteristics of patients with COVID-19 are crucial indicators of pandemic management. The lower length of hospital stay is a positive outcome of better Covid-19 management.

  • Other research product . Other ORP type . 2022
    Closed Access
    Authors: 
    Gordon, Chivaugn;
    Country: South Africa

    This collection of teaching resources for medical undergraduate students is radically different to conventional teaching. It was specifically created for emergency remote teaching through months of COVID-19 lockdown in 2020, to help students in their learning. My aim was to engage and enthuse students during an exceptionally difficult time, using out-of the box teaching methods. The intensely positive feedback from the majority of students encouraged me to create this website for wider access to facilitate learning beyond my own classroom.