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tional cohort study suggests that early SDCT scanning is safe, can expedite the diagnosis of potential causes, and can meaningfully change clinical management after idiopathic OHCA.

To determine the impact of workforce engagement factors on nurses' intention to leave their hospital.

Nurse retention is important for safe patient care. It is unknown whether meaning and joy in work, occupational fatigue, job satisfaction and unprofessional behaviour experiences predict hospital nurse turnover intentions.

This cross-sectional study involved responses from 747 nurses from two south-western hospitals. selleck inhibitor Measures included surveys to capture meaning and joy in work, job satisfaction, occupational fatigue and unprofessional behaviour exposure/impact.

Following correlational analyses, manifest variables significantly correlated with related latent factors. In structural equation modelling, greater chronic occupational fatigue was the strongest and meaning and joy at work (negative direction) the next strongest predictor of turnover intention. Although significant, job satisfaction and acute fatigue were weak predictors. Inter-shift recovery did not predict intent to leave.

This is the first study to identify Chronic Fatigue and meaning and joy in work as significant predictors of hospital nurse turnover intentions.

Employing practices that decrease chronic fatigue and increase meaning/joy in work are recommended to improve nurse retention.

Employing practices that decrease chronic fatigue and increase meaning/joy in work are recommended to improve nurse retention.

Canagliflozin, a sodium-glucose cotransporter 2 inhibitor indicated for lowering glucose, has been increasingly used in diabetes patients because of its beneficial effects on cardiovascular and renal outcomes. However, clinical trials have documented an increased risk of lower extremity amputations (LEA) associated with canagliflozin. We applied machine learning methods to predict LEA among diabetes patients treated with canagliflozin.

Using claims data from a 5% random sample of Medicare beneficiaries, we identified 13 904 diabetes individuals initiating canagliflozin between April 2013 and December 2016. The samples were randomly and equally split into training and testing sets. We identified 41 predictor candidates using information from the year prior to canagliflozin initiation, and applied four machine learning approaches (elastic net, least absolute shrinkage and selection operator [LASSO], gradient boosting machine and random forests) to predict LEA risk after canagliflozin initiation.

The incidence rate of LEA was 0.57% over a median 1.5 years follow-up. LASSO produced the best prediction, yielding a C-statistic of 0.81 (95% CI 0.76, 0.86). Among individuals categorized in the top 5% of the risk score, the actual incidence rate of LEA was 3.74%. Among the 16 factors selected by LASSO, history of LEA [adjusted odds ratio (aOR) 33.6 (13.8, 81.9)] and loop diuretic use [aOR 3.6 (1.8,7.3)] had the strongest associations with LEA incidence.

Our machine learning model efficiently predicted the risk of LEA among diabetes patients undergoing canagliflozin treatment. The risk score may support optimized treatment decisions and thus improve health outcomes of diabetes patients.

Our machine learning model efficiently predicted the risk of LEA among diabetes patients undergoing canagliflozin treatment. The risk score may support optimized treatment decisions and thus improve health outcomes of diabetes patients.Compared to modern fossil-fuel-based refineries, the emerging electrocatalytic refinery (e-refinery) is a more sustainable and environmentally benign strategy to convert renewable feedstocks and energy sources into transportable fuels and value-added chemicals. A crucial step in conducting e-refinery processes is the development of appropriate reactions and optimal electrocatalysts for efficient cleavage and formation of chemical bonds. However, compared to well-studied primary reactions (e.g., O2 reduction, water splitting), the mechanistic aspects and materials design for emerging complex reactions are yet to be settled. To address this challenge, herein, we first present fundamentals of heterogeneous electrocatalysis and some primary reactions, and then implement these to establish the framework of e-refinery by coupling in situ generated intermediates (integrated reactions) or products (tandem reactions). We also present a set of materials design principles and strategies to efficiently manipulate the reaction intermediates and pathways.Mnemonic similarity task performance, in which a known target stimulus must be distinguished from similar lures, is supported by the hippocampus and perirhinal cortex. Impairments on this task are known to manifest with advancing age. Interestingly, disrupting hippocampal activity leads to mnemonic discrimination impairments when lures are novel, but not when they are familiar. This observation suggests that other brain structures support discrimination abilities as stimuli are learned. The prefrontal cortex (PFC) is critical for retrieval of remote events and executive functions, such as working memory, and is also particularly vulnerable to dysfunction in aging. Importantly, the medial PFC is reciprocally connected to the perirhinal cortex and neuron firing in this region coordinates communication between lateral entorhinal and perirhinal cortices to presumably modulate hippocampal activity. This anatomical organization and function of the medial PFC suggests that it contributes to mnemonic discrimination; k performance, but the time course of PFC involvement is dissociable from that of the hippocampus.Ezrin-Radixin-Moesin (ERM) proteins play an essential role in the cytoplasm by cross-linking actin filaments with plasma membrane proteins. Research has identified the nuclear localization of ERMs, as well as the involvement of a single Drosophila ERM protein, Moesin, in nuclear mRNA exports. However, the question of how important the nuclear activity of ERM proteins are for the life of an organism has so far not been explored. Here, we present the first attempt to reveal the in vivo relevance of nuclear localization of Moesin in Drosophila. With the help of a nuclear export signal, we decreased the amount of Moesin in the nuclei of the animals. Furthermore, we observed various developmental defects, demonstrating the importance of ERM function in the nucleus for the first time. Transcriptome analysis of the mutant flies revealed that the lack of nuclear Moesin function leads to expression changes in nearly 700 genes, among them heat-shock genes. This result together with additional findings revealed that in Drosophila the expression of protein chaperones requires the nuclear functions of Moesin.