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Dysregulated intestinal epithelial apoptosis initiates gut injury, alters the intestinal barrier, and can facilitate bacterial translocation leading to a systemic inflammatory response syndrome (SIRS) and/or multi-organ dysfunction syndrome (MODS). A variety of gastrointestinal disorders, including inflammatory bowel disease, have been linked to intestinal apoptosis. Similarly, intestinal hyperpermeability and gut failure occur in critically ill patients, putting the gut at the center of SIRS pathology. Regulation of apoptosis and immune-modulatory functions have been ascribed to Thirty-eight-negative kinase 1 (TNK1), whose activity is regulated merely by expression. We investigated the effect of TNK1 on intestinal integrity and its role in MODS. TNK1 expression induced crypt-specific apoptosis, leading to bacterial translocation, subsequent septic shock, and early death. Mechanistically, TNK1 expression in vivo resulted in STAT3 phosphorylation, nuclear translocation of p65, and release of IL-6 and TNF-alpha. A TNF-alpha neutralizing antibody partially blocked development of intestinal damage. Conversely, gut-specific deletion of TNK1 protected the intestinal mucosa from experimental colitis and prevented cytokine release in the gut. Finally, TNK1 was found to be deregulated in the gut in murine and porcine trauma models and human inflammatory bowel disease. Thus, TNK1 might be a target during MODS to prevent damage in several organs, notably the gut.

The manual outlining of hepatic metastasis in (US) ultrasound acquisitions from patients suffering from pancreatic cancer is common practice. However, such pure manual measurements are often very time consuming, and the results repeatedly differ between the raters. In this contribution, we study the in-depth assessment of an interactive graph-based approach for the segmentation for pancreatic metastasis in US images of the liver with two specialists in Internal Medicine. Thereby, evaluating the approach with over one hundred different acquisitions of metastases. The two physicians or the algorithm had never assessed the acquisitions before the evaluation. In summary, the physicians first performed a pure manual outlining followed by an algorithmic segmentation over one month later. As a result, the experts satisfied in up to ninety percent of algorithmic segmentation results. Furthermore, the algorithmic segmentation was much faster than manual outlining and achieved a median Dice Similarity Coefficient (DSC) of over eighty percent. Ultimately, the algorithm enables a fast and accurate segmentation of liver metastasis in clinical US images, which can support the manual outlining in daily practice., 5 pages, 3 Figures, 1 Table, The 2017 Biomedical Engineering International Conference (BMEiCON-2017)

Manual segmentation of hepatic metastases in ultrasound images acquired from patients suffering from pancreatic cancer is common practice. Semiautomatic measurements promising assistance in this process are often assessed using a small number of lesions performed by examiners who already know the algorithm. In this work, we present the application of an algorithm for the segmentation of liver metastases due to pancreatic cancer using a set of 105 different images of metastases. The algorithm and the two examiners had never assessed the images before. The examiners first performed a manual segmentation and, after five weeks, a semiautomatic segmentation using the algorithm. They were satisfied in up to 90% of the cases with the semiautomatic segmentation results. Using the algorithm was significantly faster and resulted in a median Dice similarity score of over 80%. Estimation of the inter-operator variability by using the intra class correlation coefficient was good with 0.8. In conclusion, the algorithm facilitates fast and accurate segmentation of liver metastases, comparable to the current gold standard of manual segmentation., Comment: 7 pages, 3 Figures, 3 Tables, 46 References