Publications by authors named "M R Wagner"

The immunohistochemical expression of various members of the claudin family has already been studied in pathological affections of the vulva whether to differentiate precancerous lesions from vulvar squamous cell carcinoma or in inflammatory conditions such as lichen sclerosus. From an oncological perspective, however, immunohistochemical analysis of claudin 18.2 protein expression has become increasingly clinically relevant nowadays since the impressive therapeutic benefits of the claudin 18.

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This article aims to highlight high-quality observational and intervention studies focused on promoting psychological well-being among cardiac arrest (CA) survivors and their families. Following CA, many patients experience significant psychological distress, including depression, generalized anxiety, and post-traumatic stress. Recent studies indicate that this distress can narrow patients' focus, resulting in heightened awareness of cardiac signals-such as fluctuations in heart rate or blood pressure-that lead to constant monitoring and increased anxiety.

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Background: Correlative structural and chemical imaging of biofilms allows for the combined analysis of microbial identity and metabolism at the microscale. Here, we developed pH-FISH, a method that combines pH ratiometry with fluorescence in situ hybridization (FISH) in structurally intact biofilms for the coupled investigation of microbial acid metabolism and biofilm composition. Careful biofilm handling and modified sample preparation procedures for FISH allowed preservation of the three-dimensional biofilm structure throughout all processing and imaging steps.

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Psoriasis is a chronic skin disease occasionally associated with abdominal symptoms and IBD. We aimed to characterize intestinal immune cells and the integrity of the intestinal barrier in psoriasis. Biopsies from the duodenum and colon were analyzed by flow cytometry and immunohistochemistry for the presence and activation status of different immune cell populations.

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Medical image analysis has significantly benefited from advancements in deep learning, particularly in the application of Generative Adversarial Networks (GANs) for generating realistic and diverse images that can augment training datasets. The common GAN-based approach is to generate entire image volumes, rather than the region of interest (ROI). Research on deep learning-based brain tumor classification using MRI has shown that it is easier to classify the tumor ROIs compared to the entire image volumes.

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