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Biology subjects

Hocke, A.

Publications and source records attributed to Hocke, A..

2 recordsLinked to original sources

Overventilation-induced airspace acidification increases susceptibility to Pseudomonas pneumonia

Ventilator-associated pneumonia (VAP) is the most frequent nosocomial infection in critically ill patients. Local pH variations affect bacterial growth. Whether airway acidification contributes to the pathogenesis and pathophysiology of Pseudomonas aeruginosa (PA)-induced VAP is currently unknown. This study was undertaken to investigate the role and mechanisms of airspace acidification by mechanical ventilation (MV) in PA-induced VAP. C57BL/6J mice were subjected to high (HVt: 34 mL/kg) or low (LVt: 9 mL/kg) tidal volume MV for 4 h. PA was instilled via the tracheal tube, and animals were allowed to recover from sedation and breathe spontaneously for 24 h following extubation. Fluorescence microscopy was applied to determine alveolar pH in ex vivo perfused and ventilated murine lungs. Bacterial growth and adhesion on cyclically stretched A549 and human alveolar epithelial cells was examined. Upon PA infection, HVt mice showed increased alveolo-capillary permeability, elevated lung and blood leukocyte counts, and higher bacterial load in lungs and extrapulmonary organs as compared to LVt controls. HVt MV induced acidification of alveolar lining fluid (ALF) in lungs and decreased pulmonary expression of Na+/H+ exchanger 1 (NHE1). Inhibition of NHE1 enhanced PA growth in vitro on alveolar epithelial cells and increased pulmonary bacterial loads in LVt-MV mice in vivo. In a novel murine VAP model, key characteristics of PA-VAP were replicated. HVt MV induced mild VILI with acidification of airway lining fluid, increasing susceptibility to PA pneumonia. NHE1 was identified as critical factor for MV-induced airspace acidification, and thus as potential target to combat PA-VAP.

immunology↗

VoltRon: A Spatial Omics Analysis Platform for Multi-Resolution and Multi-omics Integration using Image Registration

The growing number of spatial omic technologies have created a demand for computational tools capable of managing, storing, and analyzing spatial datasets with multiple modalities and spatial resolutions. Meanwhile, computer vision is becoming an integral part of processing spatial data readouts where image registration and spatial data alignment of tissue sections are essential prior to data integration. Hence, there is a need for computational platforms that analyze data across spatial datasets with diverse resolutions as well as those that manipulate and process images of microanatomical tissue structures. To this end, we have developed VoltRon, a novel R package for spatial omics analysis with a unique data structure that accommodates data readouts with many levels of spatial resolutions (i.e., multi-resolution) including regions of interest (ROIs), spots, single cells, and even subcellular entities such as molecules. To connect and integrate these spatially diverse omic profiles, VoltRon accounts for spatial organization of tissue blocks (samples), layers (sections) and assays given a multi-resolution collection of spatial data readouts. An easy-to-use computer vision toolbox, OpenCV, is fully embedded in VoltRon that allows users to both automatically and manually register spatial coordinates across adjacent layers for data transfer without the need for external software tools. VoltRon is implemented in the R programming language and is freely available at https://github.com/BIMSBbioinfo/VoltRon.

bioinformatics↗