Objective: To identify clinical signs and symptoms (ie, "terms") that accurately predict laboratory-confirmed influenza cases and thereafter generate and evaluate various influenza-like illness (ILI) case definitions for detecting influenza. A secondary objective explored whether surveillance of data beyond the chief complaint improves the accuracy of predicting influenza.
Design: Retrospective, cross-sectional study.
Background: A highly sensitive real-time syndrome surveillance system is critical to detect, monitor, and control infectious disease outbreaks, such as influenza. Direct comparisons of diagnostic accuracy of various surveillance systems are scarce.
Objective: To statistically compare sensitivity and specificity of multiple proprietary and open source syndrome surveillance systems to detect influenza-like illness (ILI).
Objective: To investigate the impact of excluding cases with alternative diagnoses on the sensitivity and specificity of the Centers for Disease Control and Prevention's (CDC) influenza-like illness (ILI) case definition in detecting the 2009 H1N1 influenza, using Geographic Utilization of Artificial Intelligence in Real-Time for Disease Identification and Alert Notification, a disease surveillance system.
Design: Retrospective cross-sectional study design.
Setting: Emergency department of an urban tertiary care academic medical center.
Collagen-like peptides of the type (Pro-Pro-Gly)(10) fold into stable triple helices. An electron-withdrawing substituent at the H(gamma)(3) ring position of the second proline residue stabilizes these triple helices. The aim of this study was to reveal the structural and energetic origins of this effect.
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