Myocarditis in children after COVID-19 vaccine

Three healthy adolescents presented with myocarditis confirmed on cardiac magnetic resonance imaging after receiving Pfizer-BioNTech COVID-19 vaccine. All patients were hemodynamically stable and had good short-term outcomes. Long-term outcomes are yet to be determined. Larger studies are needed to determine whether an association between Pfizer-BioNTech COVID-19 vaccine and myocarditis exists

Unbiased modifier screen reveals that signal strength determines the regulatory role murine TLR9 plays in autoantibody production.

The autoimmune disease systemic lupus erythematosus has a complex environmental and multifactorial genetic basis. Genome-wide association studies have recently identified numerous disease-associated polymorphisms, but it remains unclear in which cells and during which step of pathogenesis specific polymorphisms interact to cause disease. Using a mouse model in which the same activating mutation (CD45E613R) causes distinct genetic background-dependent disease phenotypes, we performed a screen for genetic modifiers of autoreactivity between anti-nuclear Ab (ANA)-resistant CD45E613R.B6 and ANA-permissive CD45E613R.BALB/c mice. Within a novel autoreactivity-associated locus on chromosome 9, we identify a putative modifier, TLR9. Validating a role for TLR9 in modifying autoreactivity in the context of the CD45E613R mutation, manipulation of TLR9 gene dosage eliminates ANA in CD45E613R.BALB/c mice, but confoundingly permits ANA in CD45E613R.B6 mice. We demonstrate that sensitivity to ANA is modulated by strength of TLR9 signal, because stronger TLR9(B6) signals, but not weaker TLR9(BALB/c) signals, negatively regulate CD45E613R B cell development during competitive reconstitution at the central tolerance checkpoint. Our results identify a novel autoreactivity-associated locus and validate Tlr9 as a candidate gene within the locus. We further demonstrate a novel role for TLR9 signal strength in central tolerance, providing insight into the interplay of disease-associated polymorphisms at a discrete step of systemic lupus erythematosus pathogenesis

Enhanced Critical Congenital Cardiac Disease Screening by Combining Interpretable Machine Learning Algorithms

Critical Congenital Heart Disease (CCHD) screening that only uses oxygen saturation (SpO2), measured by pulse oximetry, fails to detect an estimated 900 US newborns annually. The addition of other pulse oximetry features such as perfusion index (PIx), heart rate, pulse delay and photoplethysmography characteristics may improve detection of CCHD, especially those with systemic blood flow obstruction such as Coarctation of the Aorta (CoA). To comprehensively study the most relevant features associated with CCHD, we investigated interpretable machine learning (ML) algorithms by using Recursive Feature Elimination (RFE) to identify an optimal subset of features. We then incorporated the trained ML models into the current SpO2-alone screening algorithm. Our proposed enhanced CCHD screening system, which adds the ML model, improved sensitivity by approximately 10 percentage points compared to the current standard SpO2-alone method with minimal to no impact on specificity.Clinical relevance- This establishes proof of concept for a ML algorithm that combines pulse oximetry features to improve detection of CCHD with little impact on false positive rate