A growing body of evidence is suggesting a significant association between the COVID-19 pandemic and population-level mental health. Study findings suggest that individuals with a lifetime history of disordered eating behavior may be negatively affected by COVID-19-related anxiety, and prevention measures may disrupt daily functioning and limit access to treatment. However, data describing the influence of the COVID-19 pandemic on disordered eating behaviors are limited, and most findings focus on individuals in treatment settings. The aim of this study is to characterize the experiences of Reddit users worldwide who post in eating disorder (ED)-related discussion forums describing the influence of the COVID-19 pandemic on their overall mental health and disordered eating behavior. Data were collected from popular subreddits acknowledging EDs as their primary discussion topic. Unique discussion posts dated from January 1 to May 31, 2020 that referenced the COVID-19 pandemic were extracted and evaluated ly during periods of limited treatment access.Reddit discussion forums have provided a therapeutic community for individuals to share experiences and provide support for peers with ED during a period of increased psychiatric distress. Future research is needed to assess the impact of the COVID-19 pandemic on disordered eating behavior and to evaluate the role of social media discussion forums in mental health treatment, especially during periods of limited treatment access. During the COVID-19 pandemic, new digital solutions have been developed for infection control. In particular, contact tracing mobile apps provide a means for governments to manage both health and economic concerns. However, public reception of these apps is paramount to their success, and global uptake rates have been low. In this study, we sought to identify the characteristics of individuals or factors potentially associated with voluntary downloads of a contact tracing mobile app in Singapore. A cohort of 505 adults from the general community completed an online survey. As the primary outcome measure, participants were asked to indicate whether they had downloaded the contact tracing app TraceTogether introduced at the national level. The following were assessed as predictor variables (1) participant demographics, (2) behavioral modifications on account of the pandemic, and (3) pandemic severity (the number of cases and lockdown status). Within our data set, the strongest predictor of the uptake of TraceTogether was the extent to which individuals had already adjusted their lifestyles because of the pandemic (z=13.56; P<.001). Network analyses revealed that uptake was most related to the following using hand sanitizers, avoiding public transport, and preferring outdoor over indoor venues during the pandemic. However, demographic and situational characteristics were not significantly associated with app downloads. Efforts to introduce contact tracing apps could capitalize on pandemic-related behavioral adjustments among individuals. Given that a large number of individuals is required to download contact tracing apps for contact tracing to be effective, further studies are required to understand how citizens respond to contact tracing apps. ClinicalTrials.gov NCT04468581, https//clinicaltrials.gov/ct2/show/NCT04468581.ClinicalTrials.gov NCT04468581, https//clinicaltrials.gov/ct2/show/NCT04468581. Since the first stages of the novel coronavirus 2019 (SARS-CoV-2) outbreak smell and/or taste dysfunction (STD), has been described from 5% to 88% in COVID-19 patients. Objective we aimed to assess STD in healthcare professionals (HCP), mainly allergists, affected with COVID-19, by means of a survey, and to evaluate the association of STD and their severity with demographic characteristics, symptoms, comorbidities, and hospital admission. A 15-item questionnaire was designed including different sections as follows demographics, diagnostic characteristics, STD patterns, medication use as well as comorbidities. The questionnaire was developed using Google forms, implemented and distributed to members of the Spanish Society of Allergology and Clinical Immunology (SEAIC) and spread via Social Media to be completed by HCP affected with COVID-19. HCP (n=234), 76.5% ≤55 yrs, 73.5% female, completed the survey. There was STD in up to 74.4% of the respondents, 95.6% reporting a moderate-severe impairment. Mean recovery time of taste dysfunction was 21.6±24.0 days in HCP ≤55 yrs and 33.61±26.2 days in >55 yrs (p=0.019). Stratified analysis by severity of STD showed that more than a half of COVID-19 subjects presented severe loss of smell. BLZ945 manufacturer An older age (>55 yrs) was associated with fever, anorexia, less headache and with a longer persistence of taste dysfunction. STD is a common symptom in COVID-19, even as a unique or preceding symptom. HCP who declared smell dysfunction (SD) were younger than those not affected with STD. Taste dysfunction (TD) may imply more systemic involvement in COVID-19-positive HCP.STD is a common symptom in COVID-19, even as a unique or preceding symptom. HCP who declared smell dysfunction (SD) were younger than those not affected with STD. Taste dysfunction (TD) may imply more systemic involvement in COVID-19-positive HCP.The need for comprehensive and automated screening methods for retinal image classification has long been recognized. Well-qualified doctors annotated images are very expensive and only a limited amount of data is available for various retinal diseases such as diabetic retinopathy (DR) and age-related macular degeneration (AMD). Some studies show that some retinal diseases such as DR and AMD share some common features like haemorrhages and exudation but most classification algorithms only train those disease models independently when the only single label for one image is available. Inspired by multi-task learning where additional monitoring signals from various sources is beneficial to train a robust model. We propose a method called synergic adversarial label learning (SALL) which leverages relevant retinal disease labels in both semantic and feature space as additional signals and train the model in a collaborative manner using knowledge distillation. Our experiments on DR and AMD fundus image classification task demonstrate that the proposed method can significantly improve the accuracy of the model for grading diseases by 5.