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Hepatic encephalopathy (HE) is a frequent and debilitating complication of liver disease and is oftentimes associated with hyperammonaemia. However, hyperammonaemia may occur in underlying conditions other than acute or chronic liver failure and clinical awareness is needed in order to be recognized and treated properly. A 24-year-old woman presented herself to our emergency department with acute confusion and altered mental state due to severe hyperammonaemia. The patient was diagnosed in the age of 14 with ornithine transcarbamylase (OTC) deficiency and was incompliant regarding her diet and suggested medication to treat this disorder. She was treated with sodium benzoate 250 mg/kg i.v., sodium phenylbutyrate/sodium phenylacetate 250 mg/kg i.v., L-arginine 250 mg/kg i.v., and received continuous hemofiltration. Under simultaneous medical treatment and haemodialysis, ammonia levels dropped to normal within 24 h and symptomatic encephalopathy ceased completely. OTC deficiency is rare in adults, and the majority of patients are diagnosed in childhood. It can lead to death if not diagnosed and treated properly. Our case underlines the importance of considering causes of HE other than liver cirrhosis.

Abstract Immune checkpoint inhibitors are increasingly used in advanced malignant diseases and are well-known for their good results. With the blockade of immune checkpoints, the probability of immune-related adverse events is also increased.We present a 54-year-old female patient with advanced NSCLC. She was treated with pembrolizumab and developed a stable disease under therapy. After six cycles, she presented with massive epigastric pain to our emergency department. Gastroscopy showed severe erosive-fibrinous pangastritis without the involvement of the esophagus, duodenum, or other immune-related adverse effects. Histology showed the complete destruction of the gastric mucosa. We concluded an immune-mediated gastritis by pembrolizumab, after the exclusion of other differential diagnoses.Despite treatment with prednisolone and marked improvement of her symptoms, the mucosa was never fully reconstituted into a healthy mucosa.Furthermore, we collected published reports of similar cases and conducted a comparison with features of a typical, endogenous type A gastritis to highlight similarities and differences.

Verschwörungstheorien lassen sich mit Hilfe von drei Suchscheinwerfern näher ausleuchten: Um welche Art von Wissen geht es bei den Verschwörungstheorien, welche Rolle spielt ihre Medialität und inwieweit kommen in ihnen gerade in Krisenzeiten virulente Emotionen zum Ausdruck? Verschwörungstheorien verbreiten sich nahezu epidemisch und gedeihen auf dem Nährboden einer Kultur des Misstrauens und reklamieren für sich die Logik des Verdachts. Eine solche Verschwörungsmentalität unterminiert die notwendige Vertrauensgrundlage demokratischer Herrschaft. Die beobachtbare, durch die Funktionslogik der neuen Medien begünstige Erosion der Kommunikationskultur gefährdet tendenziell die digitale Demokratie. Die Krise der digitalen Demokratie lässt sich so als Krise ihrer Kommunikationskultur analysieren.

The COVID-19 pandemic shed light on the need for quick diagnosis tools in healthcare, leading to the development of several algorithmic models for disease detection. Though these models are relatively easy to build, their training requires a lot of data, storage, and resources, which may not be available for use by medical institutions or could be beyond the skillset of the people who most need these tools. This paper describes a data analysis and machine learning platform that takes advantage of high-performance computing infrastructure for medical diagnosis support applications. This platform is validated by re-training a previously published deep learning model (COVID-Net) on new data, where it is shown that the performance of the model is improved through large-scale hyperparameter optimisation that uncovered optimal training parameter combinations. The per-class accuracy of the model, especially for COVID-19 and pneumonia, is higher when using the tuned hyperparameters (healthy: 96.5%; pneumonia: 61.5%; COVID-19: 78.9%) as opposed to parameters chosen through traditional methods (healthy: 93.6%; pneumonia: 46.1%; COVID-19: 76.3%). Furthermore, training speed-up analysis shows a major decrease in training time as resources increase, from 207 min using 1 node to 54 min when distributed over 32 nodes, but highlights the presence of a cut-off point where the communication overhead begins to affect performance. The developed platform is intended to provide the medical field with a technical environment for developing novel portable artificial-intelligence-based tools for diagnosis support.