Cerebral abscess due to Listeria monocytogenes infection in silent diabetes mellitus: Case presentation, treatment and patient outcome.

Introduction: Invasive listeriosis most often presents as bacteremia or neurolisteriosis. Cerebral infection mostly manifests as meningitis or meningoencephalitis, but cerebral abscesses are a rare manifestation. Case presentation: We present the rare case of a 51-year old patient with progressive right sided hemiparesis caused by a cerebral abscess due to Listeria monocytogenes infection. The initially suspected cerebral ischemia or bleeding was ruled out. Magnetic resonance imaging led to the suspected diagnosis of an angiocentric lymphoma. An open cerebral biopsy revealed an intracranial abscess formation. After abscess evacuation and identification of Listeria monocytogenes, anti-infective treatment with ampicillin and gentamicin was started. After repeated cerebral imaging with signs of ongoing tissue inflammation after 6 weeks we chose to prolong the therapy with oral amoxicillin until resolution of signs of intracerebral inflammation after 12 weeks, documented by repeated cerebral magnetic resonance imaging. During hospitalization, the patient was diagnosed with diabetes mellitus type II and treatment was initiated. The patient was discharged without any persistent neurologic deficits. Discussion: For the treatment of bacterial brain abscesses, 4-6 weeks of intravenous antimicrobial treatment after surgical drainage are recommended. However, first line therapy of invasive cerebral listeriosis is not well established. We decided to use a combined treatment using ampicillin and gentamicin, followed by prolonged oral treatment due to ongoing tissue inflammation. Conclusion: No evidence-based treatment recommendations are available for brain abscess caused by Listeria monocytogenes. We report a case with favorable outcome after anti-infective ampicillin- and gentamicin-based therapy. Systematic assessment of treatment would be desirable.

Covid-19 triage in the emergency department 2.0: how analytics and AI transform a human-made algorithm for the prediction of clinical pathways.

The Covid-19 pandemic has pushed many hospitals to their capacity limits. Therefore, a triage of patients has been discussed controversially primarily through an ethical perspective. The term triage contains many aspects such as urgency of treatment, severity of the disease and pre-existing conditions, access to critical care, or the classification of patients regarding subsequent clinical pathways starting from the emergency department. The determination of the pathways is important not only for patient care, but also for capacity planning in hospitals. We examine the performance of a human-made triage algorithm for clinical pathways which is considered a guideline for emergency departments in Germany based on a large multicenter dataset with over 4,000 European Covid-19 patients from the LEOSS registry. We find an accuracy of 28 percent and approximately 15 percent sensitivity for the ward class. The results serve as a benchmark for our extensions including an additional category of palliative care as a new label, analytics, AI, XAI, and interactive techniques. We find significant potential of analytics and AI in Covid-19 triage regarding accuracy, sensitivity, and other performance metrics whilst our interactive human-AI algorithm shows superior performance with approximately 73 percent accuracy and up to 76 percent sensitivity. The results are independent of the data preparation process regarding the imputation of missing values or grouping of comorbidities. In addition, we find that the consideration of an additional label palliative care does not improve the results.

Channels of participation: Political participant types and personality.

This article employs a person-centred approach to test the relationship between personality traits and empirically defined political participant types. We argue that it is more appropriate to focus on types of participants to test the relationship between personality and political participation than on individual modes or latent dimensions of political participation. Our reasoning is that the person-centred approach allows us to learn more about how and why citizens combine different modes of participation from a tool kit of available political activities to achieve a goal as a function of their personality. We rely on data collected by the German Longitudinal Election Study 2017 (GLES, ZA6801). On the basis of a set of survey questions enquiring on political activities that people take part in, Latent Class Analysis allows us to identify three political participant types (inactives, voting specialists, and complete activists). The 10-item Big Five Inventory (BFI-10) measures respondents' personality traits. Our findings suggest that conscientious people are more likely to affiliate with the voting specialists and extroverts with the more active participant types in Germany.