The development status of specialized nursing in ostomy care both in China and abroad

AbstractThe status of ostomy-specialized nurses' training and the specialized care for patients with permanent colostomy both in China and abroad are analyzed. Based on features and characteristics of the development of ostomy care outside China, problems in China's ostomy care are discussed to promote the sustainable development of this type of care

A two-stage framework for short-term wind power forecasting using different feature-learning models

With the growing dependence on wind power generation, improving the accuracy of short-term forecasting has become increasingly important for ensuring continued economical and reliable system operations. In the wind power forecasting field, ensemble-based forecasting models have been studied extensively; however, few of them considered learning the features from both historical wind data and NWP data. In addition, the exploration of the multiple-input and multiple-output learning structures is lacking in the wind power forecasting literature. Therefore, this study exploits the NWP and historical wind data as input and proposes a two-stage forecasting framework on the shelf of moving window algorithm. Specifically, at the first stage, four forecasting models are constructed with deep neural networks considering the multiple-input and multiple-output structures; at the second stage, an ensemble model is developed using ridge regression method for reducing the extrapolation error. The experiments are conducted on three existing wind farms for examining the 2-h ahead forecasting point. The results demonstrate that 1) the single-input-multiple-output (SIMO) structure leads to a better forecasting accuracy than the other threes; 2) ridge regression method results in a better ensemble model that is able to further improve the forecasting accuracy, than the other machine learning methods; 3) the proposed two-stage forecasting framework is likely to generate more accurate and stable results than the other existing algorithms

Signaling transduction regulated by 5-hydroxytryptamine 1A receptor and orexin receptor 2 heterodimers

As G-protein-coupled receptors (GPCRs), 5-hydroxytryptamine 1A receptor (5-HT1AR) and orexin receptor 2 (OX2R) regulate the levels of the cellular downstream molecules. The heterodimers of different GPCRs play important roles in various of neurological diseases. Moreover, 5-HT1AR and OX2R are involved in the pathogenesis of neurological diseases such as depression with deficiency of hippocampus plasticity. However, the direct interaction of the two receptors remains elusive. In the present study, we firstly demonstrated the heterodimer formation of 5-HT1AR and OX2R. Exchange protein directly activated by cAMP (Epac) cAMP bioluminescence resonance energy transfer (BRET) biosensor analysis revealed that the expression levels of cellular cAMP significantly increased in HEK293T cells transfected with the two receptors compared with the 5-HT1AR group. Additionally, the cellular level of calcium was upregulated robustly in HEK293T cells co-transfected with 5-HT1AR and OX2R group after agonist treatment. Furthermore, western blotting data showed that 5-HT1AR and OX2R heterodimer decreased the levels of phosphorylation of extracellular signal-regulated kinase (ERK) and cAMP-response element-binding protein (CREB). These results not only unraveled the formation of 5-HT1AR and OX2R heterodimer but also suggested that the heterodimer affected the downstream signaling pathway, which will provide new insights into the function of the two receptors in the brain