Recent studies demonstrated that primary immune responses can be induced within the brain depending on vessel-associated cells expressing markers of dendritic cells (DC). Using mice transcribing the green fluorescent protein (GFP) under the promoter of the DC marker CD11c, we determined the distribution, phenotype, and source of CD11c+ cells in non-diseased brains. Predilection areas of multiple sclerosis (MS) lesions (periventricular area, adjacent fibre tracts, and optical nerve) were preferentially populated by CD11c+ cells. Most CD11c+ cells were located within the juxtavascular parenchyma rather than the perivascular spaces. Virtually all CD11c+ cells co-expressed ionized calcium-binding adaptor molecule 1 (IBA-1), CD11b, while detectable levels of major histocompatibility complex II (MHC-II) in non-diseased mice was restricted to CD11c+ cells of the choroid plexus. Cellular processes project into the glia limitans which may allow transport and/or presentation of intraparenchymal antigens to extravasated T cells in perivascular spaces. In chimeric mice bearing CD11c-GFP bone marrow, fluorescent cells appeared in the CNS between 8 and 12 weeks after transplantation. In organotypic slice cultures from CD11c-GFP mice, the number of fluorescent cells strongly increased within 72 h. Strikingly, using anti-CD209, an established marker for human DC, a similar population was detected in human brains. Thus, we show for the first time that CD11c+ cells can not only be recruited from the blood into the parenchyma, but also develop from an intraneural precursor in situ. Dysbalance in their recruitment/development may be an initial step in the pathogenesis of chronic (autoimmune) neuroinflammatory diseases such as MS.
Background Due to demographic change and, more recently, coronavirus disease (COVID-19), the importance of modern intensive care units (ICU) is becoming apparent. One of the key components of an ICU is the continuous monitoring of patients' vital parameters. However, existing advances in informatics, signal processing, or engineering that could alleviate the burden on ICUs have not yet been applied. This could be due to the lack of user involvement in research and development. Objective This study focused on the satisfaction of ICU staff with current patient monitoring and their suggestions for future improvements. We aimed to identify aspects of monitoring that interrupt patient care, display devices for remote monitoring, use cases for artificial intelligence (AI), and whether ICU staff members are willing to improve their digital literacy or contribute to the improvement of patient monitoring. We further aimed to identify differences in the responses of different professional groups. Methods This survey study was performed with ICU staff from 4 ICUs of a German university hospital between November 2019 and January 2020. We developed a web-based 36-item survey questionnaire, by analyzing a preceding qualitative interview study with ICU staff, about the clinical requirements of future patient monitoring. Statistical analyses of questionnaire results included median values with their bootstrapped 95% confidence intervals, and chi-square tests to compare the distributions of item responses of the professional groups. Results In total, 86 of the 270 ICU physicians and nurses completed the survey questionnaire. The majority stated they felt confident using the patient monitoring equipment, but that high rates of false-positive alarms and the many sensor cables interrupted patient care. Regarding future improvements, respondents asked for wireless sensors, a reduction in the number of false-positive alarms, and hospital standard operating procedures for alarm management. Responses to the display devices proposed for remote patient monitoring were divided. Most respondents indicated it would be useful for earlier alerting or when they were responsible for multiple wards. AI for ICUs would be useful for early detection of complications and an increased risk of mortality; in addition, the AI could propose guidelines for therapy and diagnostics. Transparency, interoperability, usability, and staff training were essential to promote the use of AI. The majority wanted to learn more about new technologies for the ICU and required more time for learning. Physicians had fewer reservations than nurses about AI-based intelligent alarm management and using mobile phones for remote monitoring. Conclusions This survey study of ICU staff revealed key improvements for patient monitoring in intensive care medicine. Hospital providers and medical device manufacturers should focus on reducing false alarms, implementing hospital alarm standard operating procedures, introducing wireless sensors, preparing for the use of AI, and enhancing the digital literacy of ICU staff. Our results may contribute to the user-centered transfer of digital technologies into practice to alleviate challenges in intensive care medicine. Trial Registration ClinicalTrials.gov NCT03514173; https://clinicaltrials.gov/ct2/show/NCT03514173
Background Intensive Care Resources are heavily utilized during the COVID-19 pandemic. However, risk stratification and prediction of SARS-CoV-2 patient clinical outcomes upon ICU admission remain inadequate. This study aimed to develop a machine learning model, based on retrospective & prospective clinical data, to stratify patient risk and predict ICU survival and outcomes. Methods A Germany-wide electronic registry was established to pseudonymously collect admission, therapeutic and discharge information of SARS-CoV-2 ICU patients retrospectively and prospectively. Machine learning approaches were evaluated for the accuracy and interpretability of predictions. The Explainable Boosting Machine approach was selected as the most suitable method. Individual, non-linear shape functions for predictive parameters and parameter interactions are reported. Results 1039 patients were included in the Explainable Boosting Machine model, 596 patients retrospectively collected, and 443 patients prospectively collected. The model for prediction of general ICU outcome was shown to be more reliable to predict “survival”. Age, inflammatory and thrombotic activity, and severity of ARDS at ICU admission were shown to be predictive of ICU survival. Patients’ age, pulmonary dysfunction and transfer from an external institution were predictors for ECMO therapy. The interaction of patient age with D-dimer levels on admission and creatinine levels with SOFA score without GCS were predictors for renal replacement therapy. Conclusions Using Explainable Boosting Machine analysis, we confirmed and weighed previously reported and identified novel predictors for outcome in critically ill COVID-19 patients. Using this strategy, predictive modeling of COVID-19 ICU patient outcomes can be performed overcoming the limitations of linear regression models. Trial registration “ClinicalTrials” (clinicaltrials.gov) under NCT04455451.
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