Citations

Abstract Structural grey matter covariance networks provide an individual quantification of morphological patterns in the brain. The network integrity is disrupted in sporadic Alzheimer’s disease, and network properties show associations with the level of amyloid pathology and cognitive decline. Therefore, these network properties might be disease progression markers. However, it remains unclear when and how grey matter network integrity changes with disease progression. We investigated these questions in autosomal dominant Alzheimer’s disease mutation carriers, whose conserved age at dementia onset allows individual staging based upon their estimated years to symptom onset. From the Dominantly Inherited Alzheimer Network observational cohort, we selected T1-weighted MRI scans from 269 mutation carriers and 170 non-carriers (mean age 38 ± 15 years, mean estimated years to symptom onset −9 ± 11), of whom 237 had longitudinal scans with a mean follow-up of 3.0 years. Single-subject grey matter networks were extracted, and we calculated for each individual the network properties which describe the network topology, including the size, clustering, path length and small worldness. We determined at which time point mutation carriers and non-carriers diverged for global and regional grey matter network metrics, both cross-sectionally and for rate of change over time. Based on cross-sectional data, the earliest difference was observed in normalized path length, which was decreased for mutation carriers in the precuneus area at 13 years and on a global level 12 years before estimated symptom onset. Based on longitudinal data, we found the earliest difference between groups on a global level 6 years before symptom onset, with a greater rate of decline of network size for mutation carriers. We further compared grey matter network small worldness with established biomarkers for Alzheimer disease (i.e. amyloid accumulation, cortical thickness, brain metabolism and cognitive function). We found that greater amyloid accumulation at baseline was associated with faster decline of small worldness over time, and decline in grey matter network measures over time was accompanied by decline in brain metabolism, cortical thinning and cognitive decline. In summary, network measures decline in autosomal dominant Alzheimer’s disease, which is alike sporadic Alzheimer’s disease, and the properties show decline over time prior to estimated symptom onset. These data suggest that single-subject networks properties obtained from structural MRI scans form an additional non-invasive tool for understanding the substrate of cognitive decline and measuring progression from preclinical to severe clinical stages of Alzheimer’s disease.

AbstractBackgroundPersistent loss of skeletal muscle mass and function as well as altered fat metabolism are frequently observed in severe sepsis survivors. Studies examining sepsis‐associated tissue dysfunction from the perspective of the tissue microenvironment are scarce. In this study, we comprehensively assessed transcriptional changes in muscle and fat at single‐cell resolution following experimental sepsis induction.MethodsSkeletal muscle and visceral white adipose tissue from control mice or mice 1 day or 1 month following faecal slurry‐induced sepsis were used. Single cells were mechanically and enzymatically prepared from whole tissue, and viable cells were further isolated by fluorescence activated cell sorting. Droplet‐based single‐cell RNA‐sequencing (scRNA‐seq; 10× Genomics) was used to generate single‐cell gene expression profiles of thousands of muscle and fat‐resident cells. Bioinformatics analyses were performed to identify and compare individual cell populations in both tissues.ResultsIn skeletal muscle, scRNA‐seq analysis classified 1438 single cells into myocytes, endothelial cells, fibroblasts, mesenchymal stem cells, macrophages, neutrophils, T‐cells, B‐cells, and dendritic cells. In adipose tissue, scRNA‐seq analysis classified 2281 single cells into adipose stem cells, preadipocytes, endothelial cells, fibroblasts, macrophages, dendritic cells, B‐cells, T‐cells, NK cells, and gamma delta T‐cells. One day post‐sepsis, the proportion of most non‐immune cell populations was decreased, while immune cell populations, particularly neutrophils and macrophages, were highly enriched. Proportional changes of endothelial cells, neutrophils, and macrophages were validated using faecal slurry and cecal ligation and puncture models. At 1 month post‐sepsis, we observed persistent enrichment/depletion of cell populations and further uncovered a cell‐type and tissue‐specific ability to return to a baseline transcriptomic state. Differential gene expression analyses revealed key genes and pathways altered in post‐sepsis muscle and fat and highlighted the engagement of infection/inflammation and tissue damage signalling. Finally, regulator analysis identified gonadotropin‐releasing hormone and Bay 11‐7082 as targets/compounds that we show can reduce sepsis‐associated loss of lean or fat mass.ConclusionsThese data demonstrate persistent post‐sepsis muscle and adipose tissue disruption at the single‐cell level and highlight opportunities to combat long‐term post‐sepsis tissue wasting using bioinformatics‐guided therapeutic interventions.

Abstract Introduction: Even physicians who routinely work in complex, dynamic practices may be unprepared to optimally manage challenging critical events. High-fidelity simulation can realistically mimic critical clinically relevant events, however the reliability and validity of simulation-based assessment scores for practicing physicians has not been established.Methods: Standardized complex simulation scenarios were developed and administered to board-certified, practicing anesthesiologists who volunteered to participate in an assessment study during formative maintenance of certification activities. A subset of the study population agreed to participate as the primary responder in a second scenario for this study. The physicians were assessed independently by trained raters on both teamwork/behavioral and technical performance measures. Analysis using Generalizability and Decision studies were completed for the two scenarios with two raters.Results: The technical score was not more reliable than the behavioral score. With two raters > 20 scenarios would be required to achieve a reliability estimate of 0.7. Increasing the number of raters would have little effect on reliability.Discussion: The performance of practicing physicians on simulated critical events may be highly context-specific. Realistic simulation-based assessment for practicing physicians is resource-intensive and may be best-suited for individualized formative feedback. Moreover, aggregate data from a population of participants may yield even higher impact if used to identify skill or knowledge gaps to be addressed by training programs and continuing education improvements across the profession.