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Pabst, A.

Publications and source records attributed to Pabst, A..

2 recordsLinked to original sources

Towards Biohybrid Lung Development? Inflammatory Conditions Disrupt Endothelial Layer Integrity on Gas Exchange Membranes

Systemic inflammation presents a significant challenge to the long-term function of biohybrid implants. While endothelialisation of biohybrid implants has been shown to improve device hemocompatibility, its feasibility under the influence of patients inflammatory status remains largely unexplored. To investigate this, we developed a controlled in vitro model which allows to study endothelial dysfunction under inflammatory stress. Endothelial cells were cultured on polydimethylsiloxane under physiological shear stress and exposed to lipopolysaccharide (LPS)-activated peripheral blood mononuclear cells (PBMCs), simulating inflammatory conditions. Endothelial morphology and confluence was assessed using immunohistochemistry and scanning electron microscopy. Leukocyte adhesion was evaluated directly as well as indirectly, using flow cytometry to analyse cell adhesion molecules. Quantitative PCR was used for gene expression analysis of inflammatory mediators. Notably, neither LPS nor PBMCs alone induced endothelial disruption, whereas their combination significantly impaired endothelial confluence: Inflammatory activation led to substantial loss of endothelial confluence, increased leukocyte adhesion, and elevated expression of adhesion molecules ICAM-1, VCAM-1, and E-selectin. Gene expression analysis highlights the upregulation of inflammatory mediators, such as IL-6, IL-8, IL-10, and MCP-1. This study underscores the challenges of implementing endothelialisation in biohybrid devices, particularly in patients with systemic inflammation. By considering translational hurdles, this work contributes to the development of clinically viable biohybrid constructs and highlights the importance of considering inflammatory dynamics when designing next-generation implants.

bioengineering↗

EntireAxon: Deep learning deciphers four distinct patterns of axonal degeneration

BackgroundAxonal degeneration (AxD) is a pathological hallmark of many neurodegenerative diseases. Deciphering the morphological patterns of AxD will help to understand the underlying mechanisms and to develop effective therapeutic interventions. Here, we evaluated the progression of AxD in cortical neurons using a novel microfluidic device in combination with a deep learning tool, the EntireAxon, that we developed for the enhanced-throughput analysis of AxD on microscopic images. ResultsThe EntireAxon convolutional neural network sensitively and specifically segmented the features of AxD, including axons, axonal swellings, and axonal fragments, and its performance exceeded that of human expert raters. In an in vitro model of AxD in hemorrhagic stroke induced by the hemolysis product hemin, we detected the concentration- and time-dependent degeneration of axons leading to a decrease in axon area, while the axonal swelling and axonal fragment area increased. Time course analysis revealed that axonal swellings preceded axon fragmentation, suggesting that swellings may be reliable predictors of AxD. Using a recurrent neural network, we further identified four morphological patterns of AxD (granular, retraction, swelling, and transport degeneration) in cortical axons subjected to hemin. ConclusionsThese findings indicate a morphological heterogeneity of AxD under pathophysiologic conditions. The combination of the microfluidic device with the EntireAxon deep learning tool enable the systematic analysis of AxD but also unravel a so far unknown intricacy in which AxD can occur in a disease context.

neuroscience↗