40 research outputs found

    Multi-drug resistant Acinetobacter infections in critically injured Canadian forces soldiers

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    <p>Abstract</p> <p>Background</p> <p>Military members, injured in Afghanistan or Iraq, have returned home with multi-drug resistant <it>Acinetobacter baumannii </it>infections. The source of these infections is unknown.</p> <p>Methods</p> <p>Retrospective study of all Canadian soldiers who were injured in Afghanistan and who required mechanical ventilation from January 1 2006 to September 1 2006. Patients who developed <it>A. baumannii </it>ventilator associated pneumonia (VAP) were identified. All <it>A. baumannii </it>isolates were retrieved for study patients and compared with <it>A. baumannii </it>isolates from environmental sources from the Kandahar military hospital using pulsed-field gel electrophoresis (PFGE).</p> <p>Results</p> <p>During the study period, six Canadian Forces (CF) soldiers were injured in Afghanistan, required mechanical ventilation and were repatriated to Canadian hospitals. Four of these patients developed <it>A. baumannii </it>VAP. <it>A. baumannii </it>was also isolated from one environmental source in Kandahar – a ventilator air intake filter. Patient isolates were genetically indistinguishable from each other and from the isolates cultured from the ventilator filter. These isolates were resistant to numerous classes of antimicrobials including the carbapenems.</p> <p>Conclusion</p> <p>These results suggest that the source of <it>A. baumannii </it>infection for these four patients was an environmental source in the military field hospital in Kandahar. A causal linkage, however, was not established with the ventilator. This study suggests that infection control efforts and further research should be focused on the military field hospital environment to prevent further multi-drug resistant <it>A. baumannii </it>infections in injured soldiers.</p

    The SUN Protein Mps3 Is Required for Spindle Pole Body Insertion into the Nuclear Membrane and Nuclear Envelope Homeostasis

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    The budding yeast spindle pole body (SPB) is anchored in the nuclear envelope so that it can simultaneously nucleate both nuclear and cytoplasmic microtubules. During SPB duplication, the newly formed SPB is inserted into the nuclear membrane. The mechanism of SPB insertion is poorly understood but likely involves the action of integral membrane proteins to mediate changes in the nuclear envelope itself, such as fusion of the inner and outer nuclear membranes. Analysis of the functional domains of the budding yeast SUN protein and SPB component Mps3 revealed that most regions are not essential for growth or SPB duplication under wild-type conditions. However, a novel dominant allele in the P-loop region, MPS3-G186K, displays defects in multiple steps in SPB duplication, including SPB insertion, indicating a previously unknown role for Mps3 in this step of SPB assembly. Characterization of the MPS3-G186K mutant by electron microscopy revealed severe over-proliferation of the inner nuclear membrane, which could be rescued by altering the characteristics of the nuclear envelope using both chemical and genetic methods. Lipid profiling revealed that cells lacking MPS3 contain abnormal amounts of certain types of polar and neutral lipids, and deletion or mutation of MPS3 can suppress growth defects associated with inhibition of sterol biosynthesis, suggesting that Mps3 directly affects lipid homeostasis. Therefore, we propose that Mps3 facilitates insertion of SPBs in the nuclear membrane by modulating nuclear envelope composition

    Principles of Orthopaedic Medicine and Surgery

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    A border health crisis at the United States-Mexico border: an urgent call to action

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    Summary: In this Viewpoint, we provide an overview of the worsening trend of traumatic injuries across the United States–Mexico border after its recent fortification and height extension to 30-feet. We further characterize the international factors driving migration and the current U.S. policies and political climate that will allow this public health crisis to progress. Finally, we provide recommendations involving prevention efforts, effective resource allocation, and advocacy that will start addressing the humanitarian and economic consequences of current U.S. border policies and infrastructure

    Electronic health record machine learning model predicts trauma inpatient mortality in real time: A validation study

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    IntroductionPatient outcome prediction models are underused in clinical practice because of lack of integration with real-time patient data. The electronic health record (EHR) has the ability to use machine learning (ML) to develop predictive models. While an EHR ML model has been developed to predict clinical deterioration, it has yet to be validated for use in trauma. We hypothesized that the Epic Deterioration Index (EDI) would predict mortality and unplanned intensive care unit (ICU) admission in trauma patients.MethodsA retrospective analysis of a trauma registry was used to identify patients admitted to a level 1 trauma center for &gt;24 hours from October 2019 to July 2020. We evaluated the performance of the EDI, which is constructed from 125 objective patient measures within the EHR, in predicting mortality and unplanned ICU admissions. We performed a 5 to 1 match on age because it is a major component of EDI, then examined the area under the receiver operating characteristic curve (AUROC), and benchmarked it against Injury Severity Score (ISS) and new injury severity score (NISS).ResultsThe study cohort consisted of 1,325 patients admitted with a mean age of 52.5 years and 91% following blunt injury. The in-hospital mortality rate was 2%, and unplanned ICU admission rate was 2.6%. In predicting mortality, the maximum EDI within 24 hours of admission had an AUROC of 0.98 compared with 0.89 of ISS and 0.91 of NISS. For unplanned ICU admission, the EDI slope within 24 hours of ICU admission had a modest performance with an AUROC of 0.66.ConclusionEpic Deterioration Index appears to perform strongly in predicting in-patient mortality similarly to ISS and NISS. In addition, it can be used to predict unplanned ICU admissions. This study helps validate the use of this real-time EHR ML-based tool, suggesting that EDI should be incorporated into the daily care of trauma patients.Level of evidencePrognostic, level III
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