Objective: Based on histopathology, Edinburgh diagnostic criteria were proposed to consider a nontraumatic intracerebral lobar hemorrhage (ICH) as related to cerebral amyloid angiopathy (CAA) using the initial computed tomography (CT) scan and the APOE genetic status. We aimed to externally validate the Edinburgh prediction model, excluding the APOE genotyping and based on the modified Boston criteria on the MRI for CAA diagnosis METHODS: We included patients admitted for spontaneous lobar ICH in the emergency department between 2016 and 2019 who underwent noncontrast CT scan and MRI. According to the MRI, patients were classified into the CAA group or into the non-CAA group in the case of other causes of ICH. Two neuroradiologists, blinded to the final retained diagnosis, rated each radiological feature on initial CT scan described in the Edinburgh study on initial CT scan RESULTS: A total of 102 patients were included, of whom 36 were classified in the CAA group, 46 in the non-CAA causes group and 20 of undetermined cause (excluded from the primary analysis). The Edinburgh prediction model, including finger-like projections and subarachnoid extension showed an area under receiver operating characteristic curves (AUC) of 0.760 (95% confidence interval, CI: 0.660-0.859) for the diagnosis of CAA. The AUC reached 0.808 (95% CI: 0.714-0.901) in a new prediction model integrating a third radiologic variable: the ICH cortical involvement.
Conclusion: Using the Boston MRI criteria as a final assessment, we provided a new external confirmation of the radiological Edinburgh CT criteria, which are directly applicable in acute settings of spontaneous lobar ICH and further proposed an original 3‑set model considering finger-like projections, subarachnoid extension, and cortical involvement that may achieve a high discrimination performance.
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http://dx.doi.org/10.1007/s00062-022-01230-6 | DOI Listing |
Am J Emerg Med
January 2025
Department of Emergency Medicine, Yale University School of Medicine, New Haven, CT, USA; Center for Outcomes Research and Evaluation, Yale University, New Haven, CT, USA.
Background: This study aimed to examine how physician performance metrics are affected by the speed of other attendings (co-attendings) concurrently staffing the ED.
Methods: A retrospective study was conducted using patient data from two EDs between January-2018 and February-2020. Machine learning was used to predict patient length of stay (LOS) conditional on being assigned a physician of average speed, using patient- and departmental-level variables.
Biomed Phys Eng Express
January 2025
Shandong University of Traditional Chinese Medicine, Qingdao Academy of Chinese Medical Sciences, Jinan, Shandong, 250355, CHINA.
Mild cognitive impairment (MCI) is a significant predictor of the early progression of Alzheimer's disease, and it can be used as an important indicator of disease progression. However, many existing methods focus mainly on the image itself when processing brain imaging data, ignoring other non-imaging data (e.g.
View Article and Find Full Text PDFJ Nurs Adm
December 2024
Author Affiliations: Research Associate (Dr Keys), The Center for Health Design, Concord, California; National Senior Director (Dr Fineout-Overholt), Evidence-Based Practice and Implementation Science, at Ascension in St. Louis, MO.
Objective: Relationships among coworker and patient visibility, reactions to physical work environment, and work stress in ICU nurses are explored.
Background: Millions of dollars are invested annually in the building or remodeling of ICUs, yet there is a gap in understanding relationships between the physical layout of nursing units and work stress.
Methods: Using a cross-sectional, correlational, exploratory, predictive design, relationships among variables were studied in a diverse sample of ICU nurses.
Proc Natl Acad Sci U S A
January 2025
Computer Science and Artificial Intelligence Laboratory, Massachusetts Institute of Technology, Cambridge, MA 02139.
Protein language models (PLMs) have demonstrated impressive success in modeling proteins. However, general-purpose "foundational" PLMs have limited performance in modeling antibodies due to the latter's hypervariable regions, which do not conform to the evolutionary conservation principles that such models rely on. In this study, we propose a transfer learning framework called Antibody Mutagenesis-Augmented Processing (AbMAP), which fine-tunes foundational models for antibody-sequence inputs by supervising on antibody structure and binding specificity examples.
View Article and Find Full Text PDFProc Natl Acad Sci U S A
January 2025
Applied Mathematics Laboratory, Courant Institute of Mathematical Sciences, Department of Mathematics, New York University, New York, NY 10012.
Mechanical systems with moving points of contact-including rolling, sliding, and impacts-are common in engineering applications and everyday experiences. The challenges in analyzing such systems are compounded when an object dynamically explores the complex surface shape of a moving structure, as arises in familiar but poorly understood contexts such as hula hooping. We study this activity as a unique form of mechanical levitation against gravity and identify the conditions required for the stable suspension of an object rolling around a gyrating body.
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