Plos One
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Critical care nurses need a high level of medical competence, especially with regard to patient safety. There are several tools to measure general and critical care nursing competence, but the usability of these tools is inadequate because they include large numbers of questions. To maintain quality and safety in intensive care units (ICUs), it is necessary to be able to easily measure and evaluate critical care nursing competence. The purpose of this study was to develop an easy-to-use questionnaire assessing critical care nursing competence related to patient safety. ⋯ We developed an easy-to-use questionnaire to assess critical care nursing competence related to patient safety. The C3Q-safety was able to detect four areas of competence. The C3Q-safety will make it possible to easily measure critical care nursing competence and can be utilized for efficient education.
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Widely-prescribed prodrug opioids (e.g., hydrocodone) require conversion by liver enzyme CYP-2D6 to exert their analgesic effects. The most commonly prescribed antidepressant, selective serotonin reuptake inhibitors (SSRIs), inhibits CYP-2D6 activity and therefore may reduce the effectiveness of prodrug opioids. We used a machine learning approach to identify patients prescribed a combination of SSRIs and prodrug opioids postoperatively and to examine the effect of this combination on postoperative pain control. ⋯ We provide the first direct clinical evidence that the known ability of SSRIs to inhibit prodrug opioid effectiveness is associated with worse pain control among depressed patients. Current prescribing patterns indicate that prescribers may not account for this interaction when choosing an opioid. The study results imply that prescribers might instead choose direct acting opioids (e.g. oxycodone or morphine) in depressed patients on SSRIs.
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Vascular smooth muscle cells from the pulmonary arteries (HPASMC) of subjects with pulmonary arterial hypertension (PAH) exhibit hyperplastic growth. The PAH HPASMC display an increased sensitivity to fetal bovine serum (FBS) and undergo growth at a very low, 0.2%, FBS concentration. On the other hand, normal HPASMC (obtained from non-PAH donors) do not proliferate at low FBS (0.2%). ⋯ Furthermore, the knockdown of PLK1 expression lowers the protein level of FOXM1. On the other hand, inhibiting the action of FOXO1, a growth inhibitor, further increases the expression of FOXM1 in PAH HPASMC. Although PLK1 and FOXM1 clearly participate in PAH HPASMC hyperplasia, at this time it is not clear whether their increased activity is the primary driver of the hyperplastic behavior of the PAH HPASMC or merely a component of the pathway(s) leading to this response.
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Measuring whole-brain functional connectivity patterns based on task-free ('resting-state') spontaneous fluctuations in the functional MRI (fMRI) signal is a standard approach to probing habitual brain states, independent of task-specific context. This view is supported by spatial correspondence between task- and rest-derived connectivity networks. Yet, it remains unclear whether intrinsic connectivity observed in a resting-state acquisition is persistent during task. ⋯ Changes in functional connectivity were detected locally, within the active networks. But to identify these local changes, the contributions of different FC networks to the global intrinsic connectivity pattern had to be isolated. Together, we show that intrinsic connectivity underlying the canonical resting-state networks is relatively stable even when participants are engaged in different tasks and is not limited to the resting-state.
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Degenerative cervical myelopathy (DCM) is a spinal cord condition that results in progressive non-traumatic compression of the cervical spinal cord. Spine surgeons must consider a large quantity of information relating to disease presentation, imaging features, and patient characteristics to determine if a patient will benefit from surgery for DCM. We applied a supervised machine learning approach to develop a classification model to predict individual patient outcome after surgery for DCM. ⋯ Worse pre-operative disease severity, longer duration of DCM symptoms, older age, higher body weight, and current smoking status were associated with worse surgical outcomes. We developed a model that predicted positive surgical outcome for DCM with good accuracy at the individual patient level on an independent testing cohort. Our analysis demonstrates the applicability of machine-learning to predictive modeling in spine surgery.