COVID-19 has rapidly become a global challenge. 1 We read with interest the article by Bezzio et al 1 that reported the characteristics and outcomes of COVID-19 patients with pre-existing IBD. Patients with pre-existing cirrhosis, who have immune dysfunction and poorer outcomes from acute respiratory distress syndrome (ARDS) than patients without cirrhosis, are also considered a high-risk population for COVID-19. 2 3 In previous studies, the proportion of COVID-19 patients with pre-existing liver conditions ranged from 2% to 11%. 2 However, the clinical course and risk factors for mortality in these patients has not yet been reported. This retrospective multicentre study (COVID-Cirrhosis-CHESS, ClinicalTrials. gov NCT04329559) included consecutive adult patients with laboratory-confirmed severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) infection and pre-existing cirrhosis from 16 designated hospitals in China between 31 December 2019 and 24 March 2020. Patient characteristics are summarised in table 1. Twenty-one COVID-19 patients with preexisting cirrhosis (Child-Pugh class A, B and C in 16, 3 and 2 patients, respectively) were included in the analysis. The median age was 68 years; 11 (52.4%) were male. Most patients had compensated cirrhosis (81.0%) and chronic HBV infection was the most common aetiology (57.1%). Comorbidities other than cirrhosis were present in most patients (66.7%). In previous studies, older age, male sex and pre-existing comorbidities were associated with higher risk of mortality for COVID-19. 4 5 Here, there were no significant differences between survivors (n=16) and non-survivors (n=5) in age, sex, comorbidities, aetiology of cirrhosis, stage of cirrhosis, Child-Pugh class, Model for End-stage Liver Disease (MELD) score, interval between onset and admission, or onset symptoms of COVID-19. Comorbidities have been associated with adverse outcomes in cirrhosis, 6 but our analysis did not show clear prognostic associations-possibly due to the small size and narrow composition of the study population.
Background: The coronavirus disease 2019 (COVID-19) has become a global challenge since the December 2019. The hospital stay is one of the prognostic indicators, and its predicting model based on CT radiomics features is important for assessing the patients' clinical outcome. The study aimed to develop and test machine learning-based CT radiomics models for predicting hospital stay in patients with COVID-19 pneumonia.Methods: This retrospective, multicenter study enrolled patients with laboratory-confirmed SARS-CoV-2 infection and their initial CT images from 5 designated hospitals in Ankang,
Severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) is a novel coronavirus causing acute respiratory tract infection in humans. The virus has the characteristics of rapid transmission, long incubation period and strong pathogenicity, and has spread all over the world. Therefore, it is of great significance to select appropriate animal models for antiviral drug development and therapeutic effect evaluation. Here, we review and compare the current animal models of SARS-CoV-2.
Background and objectiveIn order for computers to extract useful information from unstructured text, a concept normalization system is needed to link relevant concepts in a text to sources that contain further information about the concept. Popular concept normalization tools in the biomedical field are dictionary-based. In this study we investigate the usefulness of natural language processing (NLP) as an adjunct to dictionary-based concept normalization.MethodsWe compared the performance of two biomedical concept normalization systems, MetaMap and Peregrine, on the Arizona Disease Corpus, with and without the use of a rule-based NLP module. Performance was assessed for exact and inexact boundary matching of the system annotations with those of the gold standard and for concept identifier matching.ResultsWithout the NLP module, MetaMap and Peregrine attained F-scores of 61.0% and 63.9%, respectively, for exact boundary matching, and 55.1% and 56.9% for concept identifier matching. With the aid of the NLP module, the F-scores of MetaMap and Peregrine improved to 73.3% and 78.0% for boundary matching, and to 66.2% and 69.8% for concept identifier matching. For inexact boundary matching, performances further increased to 85.5% and 85.4%, and to 73.6% and 73.3% for concept identifier matching.ConclusionsWe have shown the added value of NLP for the recognition and normalization of diseases with MetaMap and Peregrine. The NLP module is general and can be applied in combination with any concept normalization system. Whether its use for concept types other than disease is equally advantageous remains to be investigated.
The CALBC initiative aims to provide a large-scale biomedical text corpus that contains semantic annotations for named entities of different kinds. The generation of this corpus requires that the annotations from different automatic annotation systems be harmonized. In the first phase, the annotation systems from five participants (EMBL-EBI, EMC Rotterdam, NLM, JULIE Lab Jena, and Linguamatics) were gathered. All annotations were delivered in a common annotation format that included concept identifiers in the boundary assignments and that enabled comparison and alignment of the results. During the harmonization phase, the results produced from those different systems were integrated in a single harmonized corpus ("silver standard" corpus) by applying a voting scheme. We give an overview of the processed data and the principles of harmonization--formal boundary reconciliation and semantic matching of named entities. Finally, all submissions of the participants were evaluated against that silver standard corpus. We found that species and disease annotations are better standardized amongst the partners than the annotations of genes and proteins. The raw corpus is now available for additional named entity annotations. Parts of it will be made available later on for a public challenge. We expect that we can improve corpus building activities both in terms of the numbers of named entity classes being covered, as well as the size of the corpus in terms of annotated documents.
scite is a Brooklyn-based organization that helps researchers better discover and understand research articles through Smart Citations–citations that display the context of the citation and describe whether the article provides supporting or contrasting evidence. scite is used by students and researchers from around the world and is funded in part by the National Science Foundation and the National Institute on Drug Abuse of the National Institutes of Health.
hi@scite.ai
10624 S. Eastern Ave., Ste. A-614
Henderson, NV 89052, USA
Copyright © 2024 scite LLC. All rights reserved.
Made with 💙 for researchers
Part of the Research Solutions Family.