Military medicine
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Basic military trainee (BMT) gas mask training poses a potential mechanism of severe acute respiratory syndrome coronavirus-2 (SARS-CoV-2) transmission. After training, gas masks are decontaminated. Insufficient decontamination can lead to viral transmission in the next training class. To our knowledge, the decontamination process has not been validated for efficacy in removing respiratory pathogens such as SARS-CoV-2. ⋯ BMT gas masks can be monitored for the presence of respiratory pathogens using RT-PCR. The decontamination process removed all viable respiratory pathogens tested from the gas masks. This study demonstrates that RT-PCR can be used to conduct pathogen surveillance on BMT gas masks after training and that the current decontamination process is effective to eliminate respiratory viruses including SARS-CoV-2.
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During mechanical ventilation, cyclic recruitment and derecruitment (R/D) of alveoli result in focal points of heterogeneous stress throughout the lung. In the acutely injured lung, the rates at which alveoli can be recruited or derecruited may also be altered, requiring longer times at higher pressure levels to be recruited during inspiration, but shorter times at lower pressure levels to minimize collapse during exhalation. In this study, we used a computational model to simulate the effects of airway pressure release ventilation (APRV) on acinar recruitment, with varying inspiratory pressure levels and durations of exhalation. ⋯ Our computational model demonstrates the confounding effects of cyclic R/D, sustained recruitment, and parenchymal strain stiffening on estimates of total lung elastance during APRV. Increasing inspiratory pressures leads to not only more sustained recruitment of unstable acini but also more intratidal R/D. Our model indicates that higher inspiratory pressures should be used in conjunction with shorter exhalation times, to avoid increasing intratidal R/D.
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Service members experience unique circumstances when providing medical care in austere environments. Some challenges include supply shortages and the need to perform surgery in extreme temperatures. As such, methods for the sanitization of medical tools are sought and efficacy of existing materiel sourced to austere medical facilities should be examined for this purpose. This study tested the efficacy of commercially available, FDA-approved wound cleansers for alternative use as a potential sanitizer of stainless-steel medical devices and instruments found at improvised medical facilities. ⋯ Wound cleansers cleared for use in surgical settings demonstrated antimicrobial effects against bacteria deposited on metal surfaces. These cleansers decreased bacterial viability when challenged against extreme temperatures and few bacteria were harvested from treated surfaces even after 7 days. FDA-approved wound cleaners show promise as a potential sanitizer in resource limited environments.
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For behind armor blunt trauma (BABT), recent prominent BABT standards for chest plate define a maximum deformation distance of 44 mm in clay. It was developed for soft body armor applications with limited animal, gelatin, and clay tests. The legacy criterion does not account for differing regional thoracoabdominal tolerances to behind armor-induced injury. This study examines the rationale and approaches used in the legacy BABT clay criterion and presents a novel paradigm to develop thoracoabdominal regional injury risk curves. ⋯ While the original authors stressed limitations and the importance of additional tests for refining the 44 mm recommendation, they were not pursued. As live swine tests are effective in developing injury criteria and the responses of different areas of the thoracoabdominal regions are different because of anatomy, structure, and function, a new set of swine and human cadaver tests are necessary to develop scaling relationships. Live swine tests are needed to develop incapacitation/lethal injury risk functions; using scaling relationships, human injury criteria can be developed.
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Foot and ankle fractures are the most common military health problem. Automated diagnosis can save time and personnel. It is crucial to distinguish fractures not only from normal healthy cases, but also robust against the presence of other orthopedic pathologies. Artificial intelligence (AI) deep learning has been shown to be promising. Previously, we have developed HAMIL-Net to automatically detect orthopedic injuries for upper extremity injuries. In this research, we investigated the performance of HAMIL-Net for detecting foot and ankle fractures in the presence of other abnormalities. ⋯ Automated fracture detection is promising but to be deployed in clinical use, presence of other abnormalities must be considered to deliver its full benefit. Our results with HAMIL-Net showed that considering other abnormalities improved fracture detection and allowed for incidental findings of other musculoskeletal abnormalities pertinent or superimposed on fractures.