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      From loss to recovery: how to effectively assess chemosensory impairments during COVID-19 pandemic

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          Abstract

          Chemosensory impairments have been established as a specific indicator of COVID-19. They affect most patients and may persist long past the resolution of respiratory symptoms, representing an unprecedented medical challenge. Since the SARS-CoV-2 pandemic started, we now know much more about smell, taste, and chemesthesis loss associated with COVID-19. However, the temporal dynamics and characteristics of recovery are still unknown. Here, capitalizing on data from the Global Consortium for Chemosensory Research (GCCR) crowdsourced survey, we assessed chemosensory abilities after the resolution of respiratory symptoms in participants diagnosed with COVID-19 during the first wave of the pandemic in Italy. This analysis led to the identification of two patterns of chemosensory recovery, limited (partial) and substantial, which were found to be associated with differential age, degrees of chemosensory loss, and regional patterns. Uncovering the self-reported phenomenology of recovery from smell, taste, and chemesthetic disorders is the first, yet essential step, to provide healthcare professionals with the tools to take purposeful and targeted action to address chemosensory disorders and its severe discomfort.

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          COVID-19 and Italy: what next?

          Summary The spread of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) has already taken on pandemic proportions, affecting over 100 countries in a matter of weeks. A global response to prepare health systems worldwide is imperative. Although containment measures in China have reduced new cases by more than 90%, this reduction is not the case elsewhere, and Italy has been particularly affected. There is now grave concern regarding the Italian national health system's capacity to effectively respond to the needs of patients who are infected and require intensive care for SARS-CoV-2 pneumonia. The percentage of patients in intensive care reported daily in Italy between March 1 and March 11, 2020, has consistently been between 9% and 11% of patients who are actively infected. The number of patients infected since Feb 21 in Italy closely follows an exponential trend. If this trend continues for 1 more week, there will be 30 000 infected patients. Intensive care units will then be at maximum capacity; up to 4000 hospital beds will be needed by mid-April, 2020. Our analysis might help political leaders and health authorities to allocate enough resources, including personnel, beds, and intensive care facilities, to manage the situation in the next few days and weeks. If the Italian outbreak follows a similar trend as in Hubei province, China, the number of newly infected patients could start to decrease within 3–4 days, departing from the exponential trend. However, this cannot currently be predicted because of differences between social distancing measures and the capacity to quickly build dedicated facilities in China.
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            Generalized linear mixed models: a practical guide for ecology and evolution.

            How should ecologists and evolutionary biologists analyze nonnormal data that involve random effects? Nonnormal data such as counts or proportions often defy classical statistical procedures. Generalized linear mixed models (GLMMs) provide a more flexible approach for analyzing nonnormal data when random effects are present. The explosion of research on GLMMs in the last decade has generated considerable uncertainty for practitioners in ecology and evolution. Despite the availability of accurate techniques for estimating GLMM parameters in simple cases, complex GLMMs are challenging to fit and statistical inference such as hypothesis testing remains difficult. We review the use (and misuse) of GLMMs in ecology and evolution, discuss estimation and inference and summarize 'best-practice' data analysis procedures for scientists facing this challenge.
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              NbClust: AnRPackage for Determining the Relevant Number of Clusters in a Data Set

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                Author and article information

                Journal
                medRxiv
                MEDRXIV
                medRxiv
                Cold Spring Harbor Laboratory
                26 March 2021
                : 2021.03.25.21254253
                Affiliations
                [1 ]Department of General Psychology, University of Padova, Italy
                [2 ]Leibniz-Institute for Food Systems Biology at the Technical University of Munich, Germany
                [3 ]Department of Physiology, Monell Chemical Senses Center, USA
                [4 ]Private practice VMPCT, Milan, Italy
                [5 ]ENT department, Italian Academy Of Rhinology - ASST sette laghi Varese
                [6 ]Institute of Psychology, University of Muenster, Germany
                [7 ]Department of Agriculture, Food, Environment and Forestry (DAGRI), University of Florence, Italy
                [8 ]Interdepartmental Neuroscience Program, Yale University, USA
                [9 ]National Institutes of Nursing Research
                [10 ]National Institute of Alcohol Abuse and Alcoholism
                [11 ]National Institutes of Health
                [12 ]Neurobiology Section, SISSA, International School for Advanced Studies, Italy
                [13 ]Department of Neuroscience, ENT section, Federico II University of Naples, Italy
                [14 ]Department of Neurosciences, Biomedicine and Movement Sciences, Anatomy and Histology Section, University of Verona, Italy
                [15 ]Department of Neurobiology, Goethe Universität Frankfurt, Germany
                [16 ]Department of Molecular Medicine, University of Padova, Italy
                [17 ]Department of Psychology, Temple University, USA
                [18 ]Department of Basic Medical Sciences, University of Bari A. Moro
                Author notes
                Corresponding Author: Michele Dibattista, PhD, Department of Basic Medical Sciences, Neuroscience and Sense Organs, University of Bari A. Moro, Piazza Giulio Cesare n.11, 70125 Bari, Italy. michele.dibattista@ 123456uniba.it
                Article
                10.1101/2021.03.25.21254253
                8010774
                33791742
                027638c9-af05-4539-b525-bf29d0ed931e

                This work is licensed under a Creative Commons Attribution-NonCommercial 4.0 International License, which allows reusers to distribute, remix, adapt, and build upon the material in any medium or format for noncommercial purposes only, and only so long as attribution is given to the creator.

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