MVZ Labor Ravensburg

Infections pose a major risk during long-term human spaceflight missions. By applying standard procedures in medical microbiology to a hypothetical urinary tract infection during a Mars-bound flight, important practical aspects become apparent. From infection diagnostics to antimicrobial treatment during spaceflight, issues include technological constraints, the lack of breakpoints, and epidemiological data. A potential solution is a combination of data acquisition, artificial intelligence, individualized medicine, novel diagnostic tools, and antimicrobial strategies. This work takes an exploratory approach to highlight challenges and potential directions in developing diagnostic strategies for long-term space missions.

ABSTRACT Water Safety Plans (WSPs) are internationally endorsed frameworks for ensuring the safety and sustainability of drinking water through proactive risk management. In some countries such as Zimbabwe, however, implementation remains sporadic due to perceived financial and operational barriers. This study presents a comprehensive economic and public health evaluation of WSPs in both urban and rural settings using advanced decision-making metrics. A 10-year cost-benefit analysis (CBA) revealed Benefit-Cost Ratios (BCRs) of 34.8:1 (urban) and 57.0:1 (rural), with Internal Rates of Return (IRR) exceeding 55%. The intervention was estimated to avert approximately 37,970 DALYs averted over 10 years (21,710 urban and 16,260 rural), reflecting morbidity reductions but excluding mortality impacts, and a Cost-Effectiveness Ratio (CER) as low as $43.68 per DALY. The WSPs achieved disease Incidence Rate Reductions (IRR%) of ∼42% (urban) and ∼61% (rural), with a break-even point within the first year of deployment. Sensitivity analysis confirmed that these results remained robust under variations in discount rate, incidence reduction and cost assumptions. Environmental benefits, including reductions in biochemical oxygen demand, chemical oxygen demand and improved compliance with Word Health Organization microbial standards, further enhance the return on investment.

Background:Progress in artificial intelligence-based analysis of surgical videos has been constrained by reliance on manual frame-level annotations rather than patient-level outcomes. In addition, concerns about data privacy restrict the exchange of laparoscopic video data and, thereby, multicenter collaboration.Methods:To address these limitations, we developed a pipeline that integrates weakly supervised deep learning with Swarm Learning, a decentralized machine learning approach that enables collaborative model training without data centralization. We evaluate our pipeline using a newly curated dataset of 397 laparoscopic appendectomy recordings from six international surgical centers. We identified optimal modelling configurations (frame sampling rates and model architectures) and subsequently compared Swarm Learning to single-center and centralized learning across three novel patient-level disease staging tasks: (i) binary detection of perforated appendicitis, (ii) laparoscopic grading of appendicitis, and (iii) histopathologic inflammation grading. In addition, we surveyed participating centers to identify real-world barriers to the clinical implementation of our decentralized learning pipeline for surgical video analysis.Results:For appendicitis grading tasks, frame sampling at 1.0 frames per second and use of the SurgTempoNet architecture resulted in reliable classification performance, outperforming SurgFrameNet and Multiple Instance Learning. Across all three disease staging tasks, Swarm Learning consistently outperformed single-center training and achieved performance comparable to centralized learning, with stable generalization in external validation. The user survey identified hardware failure and limited integration of the decentralized learning pipeline with electronic patient records as key barriers to the clinical implementation of our decentralized learning pipeline for collaborative surgical video analysis.Conclusions:Weakly supervised deep learning enables the prediction of patient-level endpoints directly from surgical video data. Swarm Learning facilitates privacy-preserving multicenter collaboration and achieves performance on par with centralized learning, highlighting its potential for advancing clinically relevant, collaborative AI development in surgical video analysis, especially when integrated with patients’ electronic health records.

Grounded theory (GT) is a research methodology that entails a systematic workflow for theory generation grounded on emergent data. In this article, we juxtapose GT workflows with typical workflows in visualization and visual analytics (VIS), unveiling the characteristics shared by these workflows. We explore the research landscape of VIS to study where GT is applied to generate VIS theories, explicitly as well as implicitly. We discuss "why" GT can potentially play a significant role in VIS. We outline a "how" methodology for conducting GT research in VIS, which addresses the need for theoretical advancement in VIS while benefiting from other methods and techniques in VIS. We illustrate this "how" methodology with a use case of adopting GT approaches in studying visualization guidelines.

Extracorporeal circulation (ECC) is frequently used in intensive care patients with impaired lung or cardiac function. Despite being a life-saving therapeutic option, ECC is associated with increased risk for both bleeding and thrombosis. The management of bleeding and thromboembolic events in ECC patients is still challenging partly due to the lack of information on the pathophysiological changes in hemostasis and platelet function during the procedure. Using a combination of an ex vivo model for shear stress and a sensitive and easy-to-use laboratory method, we analyzed platelet responsiveness during ECC. After shear stress simulation in an ex vivo closed-loop ECC model, we found a significantly decreased response of α-granules after activation with adenosine diphosphate and thrombin receptor activating peptide (TRAP-6) and CD63 expression after activation with TRAP-6. Mepacrine uptake was also significantly reduced in the ex vivo shear stress model.In the same line, platelets from patients under ECC with venovenous systems and venoarterial systems showed impaired CD62P degranulation after stimulation with ADP and TRAP-6 compared with healthy control on day 1, 6, and 10 after implantation of ECC. However, no correlation between platelet degranulation and the occurrence of bleeding or thromboembolic events was observed.The used whole blood flow cytometry with immediate fixation after drawing introduces a sensitive and easy-to-use method to determine platelet activation status and our data confirm that increased shear stress conditions under ECC can cause impaired degranulation of platelet.

Body condition is increasingly used to assess the status of populations and as a proxy for individual fitness. A common, quick and non-invasive approach is to estimate condition from the relation between body length and mass. Among the methods developed for this purpose, the Scaled Mass Index (SMI) appears best suited for comparisons among populations. We assembled data from 17 populations of European green toads (Bufotes viridis) with the aim of devising a standard formula applicable for monitoring this species. The mean value of the exponents describing length-mass allometry in these samples was 3.0047. Hence, we propose using 3 as a scaling coefficient for calculating the SMI in green toads. From the contrast of SMI values for both sexes within populations, estimated with either the population-specific or the standard coefficient, we conclude that applying the standard formula not only facilitates comparisons among populations but may also help to avoid misinterpretation of variation within populations.

Zielsetzung Rolle der Rezidiv-OP bei Patientinnen aus der AGO-DESKTOP III-Studie, die in den Standard-Therapiearm (Chemotherapie alleine) randomisiert wurden und bei nachfolgendem Rezidiv operiert wurden.

Abstract ObjectiveIndependent evaluation of the sensitivity of CE-marked SARS-CoV-2 antigen rapid diagnostic tests (Ag RDT) offered in Germany.MethodThe sensitivity of 122 Ag RDT was adressed using a common evaluation panel. Minimum sensitivity of 75% for panel members with CT<25 was used for differentiation of devices eligible for reimbursement in in the German healthcare system.ResultsThe sensitivity of different SARS-CoV-2 Ag RDT varied over a wide range. The sensitivity limit of 75% for panel members with CT <25 was met by 96 of the 122 tests evaluated; 26 tests exhibited lower sensitivity, few of which were completely failing. Some devices exhibited high sensitivity, e.g. 100% for CT<30.ConclusionThis comparative evaluation succeeded to distinguish less sensitive from better performing Ag RDT. Most of the Ag RDT evaluated appear to be suitable for fast identification of acute infections associated with high viral loads. Market access of SARS-CoV-2 Ag RDT should be based on minimal requirements for sensitivity and specificity.