Characterizing COVID-19 clinical phenotypes and associated comorbidities and complication profiles

Purpose Heterogeneity has been observed in outcomes of hospitalized patients with coronavirus disease 2019 (COVID-19). Identification of clinical phenotypes may facilitate tailored therapy and improve outcomes. The purpose of this study is to identify specific clinical phenotypes across COVID-19 patients and compare admission characteristics and outcomes. Methods This is a retrospective analysis of COVID-19 patients from March 7, 2020 to August 25, 2020 at 14 U.S. hospitals. Ensemble clustering was performed on 33 variables collected within 72 hours of admission. Principal component analysis was performed to visualize variable contributions to clustering. Multinomial regression models were fit to compare patient comorbidities across phenotypes. Multivariable models were fit to estimate associations between phenotype and in-hospital complications and clinical outcomes. Results The database included 1,022 hospitalized patients with COVID-19. Three clinical phenotypes were identified (I, II, III), with 236 [23.1%] patients in phenotype I, 613 [60%] patients in phenotype II, and 173 [16.9%] patients in phenotype III. Patients with respiratory comorbidities were most commonly phenotype III (p = 0.002), while patients with hematologic, renal, and cardiac (all p<0.001) comorbidities were most commonly phenotype I. Adjusted odds of respiratory, renal, hepatic, metabolic (all p<0.001), and hematological (p = 0.02) complications were highest for phenotype I. Phenotypes I and II were associated with 7.30- fold (HR:7.30, 95% CI:(3.11-17.17), p<0.001) and 2.57-fold (HR:2.57, 95% CI:(1.10-6.00), p = 0.03) increases in hazard of death relative to phenotype III. Conclusion We identified three clinical COVID-19 phenotypes, reflecting patient populations with different comorbidities, complications, and clinical outcomes. Future research is needed to determine the utility of these phenotypes in clinical practice and trial design

A fully integrated GIS-based model of particulate waste distribution from marine fish-cage sites

Modern Geographical Information System (GIS) offers a powerful modelling environment capable of handling large databases. It is a very suitable environment in which to develop a suite of tools designed for environmental management of aquaculture sites, including carrying capacity prediction, land–water interactions and multi-site effects. One such tool, presented here, is a fully integrated and validated particulate fish waste dispersion module which uses mass balance to estimate waste input and takes account of variable bathymetry and variable settling velocity for feed and faecal components. The model also incorporates the effect of cage movement on waste dispersion, the first such model to do so. When tidal range was low (1.67 m), the maximum movement of a 22 m diameter circular cage was 10.1 m and 7.7 m easting and northing, respectively. Highest deposition from particulate fish waste is under the cage and incorporation of cage movement increased the effective area under a cage by 72%. This reduced peak deposition measurements by up to 32% and reduced the average modelled feed and faecal settlement at the cage centre by 23% and 11%, respectively. The model was validated by comparing model predictions with observed deposition measured using sediment traps during three 2-week field trips at a fish farm on the west coast of Scotland. The mean ratio of observed to predicted waste deposition at 5–25 m from the cage centre ranged from 0.9 to 1.06, whilst under the cage the model over-predicts deposition (observed/predicted=2.21). Although far-field data was seen to be comparable the near-field discrepancies resulted in variable overall accuracy in the model. The overall accuracy based on August 2001 data was ±50.9%, on February 2002, ±72.8% and on April 2002, ±50.6%. Summarizing the data resulted in an overall average predictive accuracy of ±58.1%. © 2006 Elsevier B.V. All rights reserved

Absorption and quasinormal modes of classical fields propagating on 3D and 4D de Sitter spacetime

We extensively study the exact solutions of the massless Dirac equation in 3D de Sitter spacetime that we published recently. Using the Newman-Penrose formalism, we find exact solutions of the equations of motion for the massless classical fields of spin s=1/2,1,2 and to the massive Dirac equation in 4D de Sitter metric. Employing these solutions, we analyze the absorption by the cosmological horizon and de Sitter quasinormal modes. We also comment on the results given by other authors.Comment: 31 page

Large expert-curated database for benchmarking document similarity detection in biomedical literature search

Document recommendation systems for locating relevant literature have mostly relied on methods developed a decade ago. This is largely due to the lack of a large offline gold-standard benchmark of relevant documents that cover a variety of research fields such that newly developed literature search techniques can be compared, improved and translated into practice. To overcome this bottleneck, we have established the RElevant LIterature SearcH consortium consisting of more than 1500 scientists from 84 countries, who have collectively annotated the relevance of over 180 000 PubMed-listed articles with regard to their respective seed (input) article/s. The majority of annotations were contributed by highly experienced, original authors of the seed articles. The collected data cover 76% of all unique PubMed Medical Subject Headings descriptors. No systematic biases were observed across different experience levels, research fields or time spent on annotations. More importantly, annotations of the same document pairs contributed by different scientists were highly concordant. We further show that the three representative baseline methods used to generate recommended articles for evaluation (Okapi Best Matching 25, Term Frequency-Inverse Document Frequency and PubMed Related Articles) had similar overall performances. Additionally, we found that these methods each tend to produce distinct collections of recommended articles, suggesting that a hybrid method may be required to completely capture all relevant articles. The established database server located at https://relishdb.ict.griffith.edu.au is freely available for the downloading of annotation data and the blind testing of new methods. We expect that this benchmark will be useful for stimulating the development of new powerful techniques for title and title/abstract-based search engines for relevant articles in biomedical research.Peer reviewe