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https://www.selleckchem.com/ Mean specialist response times decreased from 5.9±4.9 min (2008-2012, mean±SD) to 4.3±2.2 min (2016-2019, p=0.12), and the number of incidents with response times >5 min decreased from 28.8±17.8% (2008-2012) to 9.3±11.4% (2016-2019, p=0.04 by linear regression). As our program became more standardized, we noted better equipment availability and subspecialist communication. Few emergency situations (n=9, 6%) required operating room management. There were 3 patient deaths (2%). Conclusions Our airway safety program, including readily available specialists and equipment, facilitated effective resolution of airway emergencies in our NICU and multidisciplinary involvement enabled rapid and effective changes in response to COVID-19 regulations. A similar program could be implemented in other centers.Artificial intelligence (AI) researchers and radiologists have recently reported AI systems that accurately detect COVID-19 in chest radiographs. However, the robustness of these systems remains unclear. Using state-of-the-art techniques in explainable AI, we demonstrate that recent deep learning systems to detect COVID-19 from chest radiographs rely on confounding factors rather than medical pathology, creating an alarming situation in which the systems appear accurate, but fail when tested in new hospitals. Every year, Puerto Rico faces a hurricane season fraught with potentially catastrophic structural, emotional and health consequences. In 2017, Puerto Rico was hit by Hurricane Maria, the largest natural disaster to ever affect the island. Several studies have estimated the excess morbidity and mortality following Hurricane Maria in Puerto Rico, yet no study has comprehensively examined the underlying health system weaknesses contributing to the deleterious health outcomes. A qualitative case study was conducted to assess the ability of the UPR health system to provide patient care in response to Hurricane Maria. An established five key resilie

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