Organizing principles of astrocytic nanoarchitecture in the mouse cerebral cortex.

Astrocytes are increasingly understood to be important regulators of central nervous system (CNS) function in health and disease; yet, we have little quantitative understanding of their complex architecture. While broad categories of astrocytic structures are known, the discrete building blocks that compose them, along with their geometry and organizing principles, are poorly understood. Quantitative investigation of astrocytic complexity is impeded by the absence of high-resolution datasets and robust computational approaches to analyze these intricate cells. To address this, we produced four ultra-high-resolution datasets of mouse cerebral cortex using serial electron microscopy and developed astrocyte-tailored computer vision methods for accurate structural analysis. We unearthed specific anatomical building blocks, structural motifs, connectivity hubs, and hierarchical organizations of astrocytes. Furthermore, we found that astrocytes interact with discrete clusters of synapses and that astrocytic mitochondria are distributed to lie closer to larger clusters of synapses. Our findings provide a geometrically principled, quantitative understanding of astrocytic nanoarchitecture and point to an unexpected level of complexity in how astrocytes interact with CNS microanatomy.

Characteristics and Outcomes for Low-Risk Hospital Admissions Admitted to the ICU: A Multisite Cohort Study.

IMPORTANCE: Prognostication following ICU admission can often be determined based on known risk factors, including demographics and illness severity; however, little is known about outcomes of patients deemed to be "low-risk" at the time of hospital admission who subsequently are admitted to the ICU. OBJECTIVES: The objectives of this study were to determine the characteristics, outcomes, and costs for patients requiring ICU admission despite having lower predicted mortality when they were admitted to the hospital. DESIGN SETTING AND PARTICIPANTS: In this historical cohort study, we used a prospectively maintained ICU registry that included all ICU admissions to The Ottawa Hospital for patients 18 years or older from January 2011 to December 2016. We classified patients as low-risk using the Hospital-patient 1-year Mortality Risk at admission score, a hospital admission score validated to predict 1-year mortality. MAIN OUTCOMES AND MEASURES: The primary outcome was inhospital mortality. Secondary outcomes included adverse events, resource utilization, and costs. RESULTS: Of the 17,173 total ICU patients, 3,445 (20.1%) were classified as low-risk at hospital admission. Low-risk patients were younger (48.7 vs 67.5 yr; p < 0.001) and had a lower Multiple Organ Dysfunction Score (2.37 vs 4.14; p < 0.001). Mortality for low-risk patients was significantly lower than for non-low-risk patients (4.1% vs 25.4%; p < 0.001). For low-risk patients, multivariable logistic regression showed mortality was independently associated with older age (odds ratio, 1.02 per 1 yr; 95% CI, 1.00-1.03 per 1 yr), Multiple Organ Dysfunction Score (odds ratio, 1.42 per 1 point; 95% CI, 1.31-1.54 per 1 point), fluid management adverse events (odds ratio, 2.84; 95% CI, 1.29-6.25), hospital-acquired infections (odds ratio, 1.60; 95% CI, 1.02-2.51), and mechanical ventilation (odds ratio, 1.98; 95% CI, 1.20-3.26). CONCLUSIONS AND RELEVANCE: Despite their robust premorbid status, low-risk patients admitted to the ICU had significant inhospital mortality. Fluid management adverse events, hospital-associated infections, multiple organ dysfunction, and mechanical ventilation are important prognostic factors for low-risk patients.

Content Analysis and Characterization of Medical Tweets During the Early Covid-19 Pandemic.

Objective The novel coronavirus disease 2019 (Covid-19) has infected millions worldwide and impacted the lives of many folds more. Many clinicians share new Covid-19-related resources, research, and ideas within the online Free Open Access to Medical Education (FOAM) community of practice. This study provides a detailed content and contributor analysis of Covid-19-related tweets among the FOAM community during the first months of the pandemic. Design, Setting, and Participants In this social media content analysis study, Twitter was searched from November 1, 2019, to March 21, 2020, for English tweets discussing Covid-19 in the FOAM community. Tweets were classified into one of 13 pre-specified content categories: original research, editorials, FOAM resource, public health, podcast or video, learned experience, refuting false information, policy discussion, emotional impact, blatantly false information, other Covid-19, and non-Covid-19. Further analysis of linked original research and FOAM resources was performed. One-thousand (1000) randomly selected contributor profiles and those deemed to have contributed false information were analyzed. Results The search yielded 8541 original tweets from 4104 contributors. The number of tweets in each content category were: 1557 other Covid-19 (18.2%), 1190 emotional impact (13.9%), 1122 FOAM resources (13.1%), 1111 policy discussion (13.0%), 928 advice (10.9%), 873 learned experience (10.2%), 424 non-Covid-19 (5.0%), 410 podcast or video (4.8%), 304 editorials (3.6%), 275 original research (3.2%), 245 public health (2.9%), 83 refuting false information (1.0%), and 19 blatantly false (0.2%). Conclusions Early in the Covid-19 pandemic, the FOAM community used Twitter to share Covid-19 learned experiences, online resources, crowd-sourced advice, and research and to discuss the emotional impact of Covid-19. Twitter also provided a forum for post-publication peer review of new research. Sharing blatantly false information within this community was infrequent. This study highlights several potential benefits from engaging with the FOAM community on Twitter.