The Mega Code is a simulated cardiac arrest during which students practice as members of a team and learn to integrate the knowledge and skills of advanced cardiac life support (ACLS). This study used the Mega Code and American Heart Association (AHA) standards to evaluate 32 medical residents (MDs) and nine critical care nurses (RNs) in the role of ACLS team leader. All had been previously trained in ACLS. The testing sequence included ventricular fibrillation (VF) refractory to initial countershock (defib), asystole after second defib, recurrent VF after drug therapy, and finally sinus rhythm after third defib. A blood gas report indicated respiratory acidosis and hypoxemia. Assessment of patient status was poor in both groups, although MDs did significantly (p = .001) better than RNs. Other problem areas were drug therapy and trouble-shooting are not adequately stressed in the AHA ACLS curriculum; moreover, there is no lecture that specifically addresses the team approach to resuscitation and the role of team leader. We found that the Mega Code effectively evaluated individual and group performance. Results of objective-based Mega Code testing can be used both to improve ACLS curriculum and to indicate areas to be stressed during refresher training.
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http://dx.doi.org/10.1097/00003246-198602000-00005 | DOI Listing |
Front Plant Sci
January 2025
Guangdong University of Technology, Guangzhou Higher Education Mega Centre, Guangzhou, China.
Precise segmentation of unmanned aerial vehicle (UAV)-captured images plays a vital role in tasks such as crop yield estimation and plant health assessment in banana plantations. By identifying and classifying planted areas, crop areas can be calculated, which is indispensable for accurate yield predictions. However, segmenting banana plantation scenes requires a substantial amount of annotated data, and manual labeling of these images is both timeconsuming and labor-intensive, limiting the development of large-scale datasets.
View Article and Find Full Text PDFBioinformatics
January 2025
Guangdong Provincial Key Laboratory IRADS, Beijing Normal University-Hong Kong Baptist University United International College, Zhuhai, China.
Motivation: The increasing accessibility of large-scale protein sequences through advanced sequencing technologies has necessitated the development of efficient and accurate methods for predicting protein function. Computational prediction models have emerged as a promising solution to expedite the annotation process. However, despite making significant progress in protein research, graph neural networks face challenges in capturing long-range structural correlations and identifying critical residues in protein graphs.
View Article and Find Full Text PDFClin Oral Investig
January 2025
Department of Endodontics, Guangdong Engineering Research Center of Oral Restoration and Reconstruction, Guangzhou Key Laboratory of Basic and Applied Research of Oral Regenerative Medicine, Affiliated Stomatology Hospital of Guangzhou Medical University, Guangzhou, China.
Objectives: We investigated the recently generated RNA-sequencing dataset of pulpitis to identify the potential pain-related lncRNAs for pulpitis prediction.
Materials And Methods: Differential analysis was performed on the gene expression profile between normal and pulpitis samples to obtain pulpitis-related genes. The co-expressed gene modules were identified by weighted gene coexpression network analysis (WGCNA).
Sci Rep
January 2025
National Institutes for Quantum Science and Technology, Kamikita, 039-3212, Japan.
The Alfvén instability nonlinearly excited the energetic-particle-driven geodesic acoustic mode on the ASDEX-Upgrade tokamak, as demonstrated experimentally. The mechanism of the energetic-particle-driven geodesic acoustic mode excitation and the mode nonlinear evolution is not yet fully understood. In the present work, a first-principles simulation using the MEGA code investigated the mode properties in both the linear growth and nonlinear saturated phases.
View Article and Find Full Text PDFRev Sci Instrum
December 2024
UKAEA, Culham Centre for Fusion Energy, Culham OX14 3EB, United Kingdom.
Understanding the confinement of fast ions is crucial for plasma heating and non-inductive current drive, i.e., for the operation of a fusion reactor.
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