Tricuspid transcatheter edge-to-edge repair in a 72-year-old patient with a left ventricular assist device and prior mitral edge-to-edge repair: a case report.

Background: We report a case of a 72-year-old patient developing a significant tricuspid regurgitation (TR) 6 years after a left ventricular assist device (LVAD) implantation. The aim of this case is to demonstrate the feasibility of transcatheter edge-to-edge repair (TEER) of the tricuspid valve and the excellent clinical benefit in long-term follow-up in an LVAD patient.

Case Summary: Our patient presented with recurrent acute heart failure syndrome.

Comprehensive analysis of lung macrophages and dendritic cells in two murine models of allergic airway inflammation reveals model- and subset-specific accumulation and phenotypic alterations.

Introduction: Allergic asthma has been mainly attributed to T helper type 2 (Th2) and proinflammatory responses but many cellular processes remain elusive. There is increasing evidence for distinct roles for macrophage and dendritic cell (DC) subsets in allergic airway inflammation (AAI). At the same time, there are various mouse models for allergic asthma that have been of utmost importance in identifying key inflammatory pathways in AAI but that differ in the allergen and/or route of sensitization.

IκB-deficiency protects mice from fatal infection by blunting pro-inflammatory signature in Ly6C monocytes and preventing exaggerated innate immune responses.

IκB proteins regulate the inhibition and activation of NF-κB transcription factor complexes. While classical IκB proteins keep NF-κB complexes inactive in the cytoplasm, atypical IκB proteins act on activated NF-κB complexes located in the nucleus. Most of the knowledge regarding the function of IκB proteins has been collected , while far less is known regarding their impact on activation and regulation of immune responses during infections.

Predicting Influenza A Virus Infection in the Lung from Hematological Data with Machine Learning.

The tracking of pathogen burden and host responses with minimally invasive methods during respiratory infections is central for monitoring disease development and guiding treatment decisions. Utilizing a standardized murine model of respiratory influenza A virus (IAV) infection, we developed and tested different supervised machine learning models to predict viral burden and immune response markers, i.e.