Coronavirus disease 2019 (COVID-19) mainly manifests as a respiratory syndrome, besides causing other complications. Severe COVID-19 may also present with coagulopathy, leading to venous thrombosis and cerebral infarction. Generally, acute stroke is a secondary complication in patients displaying respiratory syndromes. Here, we present a case of acute stroke in an 84-year-old female patient who did not manifest any respiratory symptoms. The patient had no cough or fever before the stroke onset; nevertheless, COVID-19 PCR test was positive. The patient also had markedly elevated serum D-dimer levels. Our findings suggest that coagulopathy can occur even in a patient with asymptomatic COVID-19 infection, and to our knowledge, this is the first report of such a case. We concluded that elevated D-dimer levels can serve as an additional COVID-19 screening tool in stroke patients.
Objective: COVID-19 has evolved into a seasonal coronavirus disease, characterized by prolonged infection duration and repeated infections, significantly increasing the risk of patients developing long COVID. Our research focused on the immune responses in asymptomatic and mild cases, particularly the critical factors influencing serum immunoglobulin G (IgG) levels and their predictive value.
Methods: We conducted a retrospective analysis on data from 1939 asymptomatic or mildly symptomatic COVID-19 patients hospitalized between September 2022 and June 2023.
Courses of SARS-CoV-2 infections are highly variable, ranging from asymptomatic to lethal COVID-19. Though research has shown that host genetic factors contribute to this variability, cohort-based joint analyses of variants from the entire allelic spectrum in individuals with confirmed SARS-CoV-2 infections are still lacking. Here, we present the results of whole genome sequencing in 1,220 mainly vaccine-naïve individuals with confirmed SARS-CoV-2 infection, including 827 hospitalized COVID-19 cases.
Accurate estimates of the incidence of infectious diseases are key for the control of epidemics. However, healthcare systems are often unable to test the population exhaustively, especially when asymptomatic and paucisymptomatic cases are widespread; this leads to significant and systematic under-reporting of the real incidence. Here, we propose a machine learning approach to estimate the incidence of a pandemic in real-time, using reported cases and the overall test rate.
Background: Understanding COVID-19's impact on children is vital for public health policy, yet age-specific data is scarce, especially in Uganda. This study examines SARS-CoV-2 seroprevalence and risk factors among Ugandan children at two timepoints, along with COVID-19-related knowledge and practices in households, including adult vaccination status.
Methods: Baseline surveys were conducted in 12 communities from April to May 2021 (post-Alpha wave) and follow-up surveys in 32 communities from November 2021 to March 2022 (Omicron wave).
Background: Dyslipidemia, a major cardiovascular risk factor, requires consistent medication adherence, but new patients often struggle due to its asymptomatic nature. The COVID-19 pandemic has disrupted global healthcare. This study examined its impact on medication adherence and persistence among Korean patients with dyslipidemia (PWD), comparing the effects on new versus existing PWD.
