Nurses in the hospital setting must be knowledgeable about resuscitation procedures and proficient in the delivery of care during an emergency. They must be ready to implement their knowledge and skills at a moment's notice. A common dilemma for many nurses is that cardiopulmonary emergencies (Code Blues) are infrequent occurrences. Therefore, how do nurses remain competent and confident in their implementation of emergency skills while having limited exposure to the equipment and minimal experience in emergency situations? A team of nurse educators at a regional medical center in Washington State applied adult learning theory and accelerated learning techniques to develop and present a series of learning activities to enhance the staff's familiarity with emergency equipment and procedures. The series began with a carnival venue that provided hands-on practice and review of emergency skills and was reinforced with subsequent random unannounced code drills led by both educators and charge nurses.
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http://dx.doi.org/10.3928/00220124-20091119-03 | DOI Listing |
Natl Sci Rev
January 2025
Key Laboratory for Thermal Science and Power Engineering of Ministry of Education, Department of Engineering Mechanics, Tsinghua University, Beijing 100084, China.
The high thermopower of ionic thermoelectric (-TE) materials holds promise for miniaturized waste-heat recovery devices and thermal sensors. However, progress is hampered by laborious trial-and-error experimentations, which lack theoretical underpinning. Herein, by introducing the simplified molecular-input line-entry system, we have addressed the challenge posed by the inconsistency of -TE material types, and present a machine learning model that evaluates the Seebeck coefficient with an of 0.
View Article and Find Full Text PDFEClinicalMedicine
August 2024
Department of Psychology, University of Cambridge, Cambridge, CB2 3EB, United Kingdom.
Background: Predicting dementia early has major implications for clinical management and patient outcomes. Yet, we still lack sensitive tools for stratifying patients early, resulting in patients being undiagnosed or wrongly diagnosed. Despite rapid expansion in machine learning models for dementia prediction, limited model interpretability and generalizability impede translation to the clinic.
View Article and Find Full Text PDFDespite the sequencing revolution, large swaths of the genomes sequenced to date lack any information about the arrangement of transcription factor binding sites on regulatory DNA. Massively Parallel Reporter Assays (MPRAs) have the potential to dramatically accelerate our genomic annotations by making it possible to measure the gene expression levels driven by thousands of mutational variants of a regulatory region. However, the interpretation of such data often assumes that each base pair in a regulatory sequence contributes independently to gene expression.
View Article and Find Full Text PDFIt is now possible to generate large volumes of high-quality images of biomolecules at near-atomic resolution and in near-native states using cryogenic electron microscopy/electron tomography (Cryo-EM/ET). However, the precise annotation of structures like filaments and membranes remains a major barrier towards applying these methods in high-throughput. To address this, we present TARDIS ( ransformer-b sed apid imensionless nstance egmentation), a machine-learning framework for fast and accurate annotation of micrographs and tomograms.
View Article and Find Full Text PDFBMC Bioinformatics
January 2025
International Institute "Solution Chemistry of Advanced Materials and Technologies", ITMO University, Saint-Petersburg, Russian Federation, 191002.
Background: Deoxyribozymes or DNAzymes represent artificial short DNA sequences bearing many catalytic properties. In particular, DNAzymes able to cleave RNA sequences have a huge potential in gene therapy and sequence-specific analytic detection of disease markers. This activity is provided by catalytic cores able to perform site-specific hydrolysis of the phosphodiester bond of an RNA substrate.
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