Corrigendum: Epigenetic immune monitoring for COVID-19 disease course prognosis.

[This corrects the article DOI: 10.3389/fimmu.2023.

Specifications of qPCR based epigenetic immune cell quantification.

Objectives: Immune monitoring is an important aspect in diagnostics and clinical trials for patients with compromised immune systems. Flow cytometry is the standard method for immune cell counting but faces limitations. Best practice guidelines are available, but lack of standardization complicates compliance with e.

Epigenetic immune monitoring for COVID-19 disease course prognosis.

Background: The course of COVID-19 is associated with severe dysbalance of the immune system, causing both leukocytosis and lymphopenia. Immune cell monitoring may be a powerful tool to prognosticate disease outcome. However, SARS-CoV-2 positive subjects are isolated upon initial diagnosis, thus barring standard immune monitoring using fresh blood.

Epigenetic immune cell counting in human blood samples for immunodiagnostics.

Immune cell profiles provide valuable diagnostic information for hematologic and immunologic diseases. Although it is the most widely applied analytical approach, flow cytometry is limited to liquid blood. Moreover, either analysis must be performed with fresh samples or cell integrity needs to be guaranteed during storage and transport.

More Specific Signal Detection in Functional Magnetic Resonance Imaging by False Discovery Rate Control for Hierarchically Structured Systems of Hypotheses.

Signal detection in functional magnetic resonance imaging (fMRI) inherently involves the problem of testing a large number of hypotheses. A popular strategy to address this multiplicity is the control of the false discovery rate (FDR). In this work we consider the case where prior knowledge is available to partition the set of all hypotheses into disjoint subsets or families, e.

Simultaneous statistical inference for epigenetic data.

Epigenetic research leads to complex data structures. Since parametric model assumptions for the distribution of epigenetic data are hard to verify we introduce in the present work a nonparametric statistical framework for two-group comparisons. Furthermore, epigenetic analyses are often performed at various genetic loci simultaneously.