Increased risk of chronic fatigue and hair loss following COVID-19 in individuals with hypohidrotic ectodermal dysplasia.

BACKGROUND: Hypohidrotic ectodermal dysplasia (HED) is a group of genodermatoses in which deficient ectodysplasin A signalling leads to maldevelopment of skin appendages, various eccrine glands, and teeth. Individuals with HED often have disrupted epithelial barriers and, therefore, were suspected to be more susceptible to coronavirus infection. METHODS: 56 households with at least one member who had coronavirus disease of 2019 (COVID-19) were enrolled in a longitudinal study to compare the course of illness, immune responses, and long-term consequences of severe acute respiratory syndrome-coronavirus 2 (SARS-CoV-2) infection in HED patients (n = 15, age 9-52 years) and control subjects of the same age group (n = 149). RESULTS: In 14 HED patients, mild or moderate typical COVID-19 symptoms were observed that lasted for 4-45 days. Fever during the first days sometimes required external cooling measures. The course of COVID-19 was similar to that in control subjects if patients developed antibodies blocking the SARS-CoV-2 spike protein. Five out of six HED patients with completely abrogated ectodysplasin A signalling (83%) suffered from chronic, in two cases very severe fatigue following COVID-19, while only 25% of HED patients with residual activity of this pathway and 21% of control subjects recovering from COVID-19 experienced postinfectious fatigue. Hair loss after COVID-19 was also more frequent among HED patients (64%) than in the control group (13%). CONCLUSIONS: HED appears to be associated with an increased risk of long-term consequences of a SARS-CoV-2 infection. Preventive vaccination against COVID-19 should be recommended for individuals affected by this rare genetic disorder.

On the Temporal Analysis of COVID-19 Pandemic and Prediction of R 0 (preprint)

Background: The COVID-19 pandemic has affected millions of people and claimed numerous lives already. As of now, there are no available vaccines for the virus and it has become both imperative and challenging to forecast the COVID-19 cases, which will help to design effective clinical management and policy to fight the pandemic. Methods: With the objective to forecast the COVID-19 cases and Basic Reproductive Number (R0) country-wise, for more than a month ahead, we have adopted a data driven approach that employs Multiple Aggregation Prediction Algorithm (MAPA) for temporal predictions. Our strategy applies MAPA in two separate ways. First application is directly onto the number of cases and second is by calculation of R0 from the total number of cases, followed by application of MAPA. Findings: This novel workflow generates a Principal Prediction along with an Exponential Prediction that provides a range of values within which the total number of cases is expected to lie. The strategy and workflow have been validated for long term predictions (upto 45 days) with 51 countries showing Rising, Exponential growth and Plateauing number of cases, which contribute to at least 91% of the total number of cases in the world. Thereafter, we have made predictions of the possible number of COVID-19 cases that is likely to be witnessed in the next 45 days by these 51 countries, the world as a whole and the other 160 countries combined, that are affected by the pandemic. Interpretation: The integrative strategy developed in this study using machine learning and statistical tools can accurately predict the expected number of COVID-19 cases for the next 45 days with better accuracy as compared to other existing models and data driven approaches.Funding Statement: RRS acknowledges funding from Department of Science and Technology, Govt. of India (Sanction Order No. DST/ICPS/EDA/2018/General, dated 30.04.2019), PG acknowledges CSIR for Senior Research Fellowship. SN acknowledges DST INSPIRE Fellowship.Declaration of Interests: Authors declare no competing interests.