Evaluating Nursing Students' Medical Efficiency.

7%) and specificity (95.6%). Receiver operating characteristic curves indicated that combined pGSN and sFasL levels further augmented this sensitivity (90.6%) for early disease detection. Moreover, higher pGSN levels predicted improved prognosis at both 5-year overall survival and progression-free survival. In conclusion, circulating pGSN could be an independent predictor of favorable clinical outcomes and a novel biomarker for the early HNC detection in combination with sFasL.In this work, the compositional optimization in copper oxide/tungsten trioxide (CuO/WO3) composites was systematically studied for hydrogen sulfide (H2S) sensing. The response of CuO/WO3 composites changes from p-type to n-type as the CuO content decreases. Furthermore, the p-type response weakens while the n-type response strengthens as the Cu/W molar ratio decreases from 10 to 110. The optimal Cu/W molar ratio is 110, at which the sensor presents the ultrahigh n-type response of 1.19 × 105 to 20 ppm H2S gas at 40 °C. Once the temperature rises from 40 °C to 250 °C, the CuO/WO3 (11) sensor presents the p-n response transformation, and the CuO/WO3 (11.5) sensor changes from no response to n-type response, because the increased temperature facilitates the Cu-S bonds break and weakens the p-type CuO contribution to the total response, such that the CuS bond decomposition by a thermal effect was verified by a Raman analysis. In addition, with a decrease in CuO content, the CuO is transformed from partly to completely converting to CuS, causing the resistance of CuO to decrease from increasing and, hence, a weakening mode of p-CuO and n-WO3 to the total response turns to a synergistic mode to it.Professional and academic legislation relating to nursing skills reflects conceptual and professional developments. In this sense, conceptual and methodological analyses are required to describe the concept of nursing competencies, the individual or group self-perception of competencies, to identify training needs, and to specify the nursing professional profile within the health organization. A sequential mixed methodology was proposed combining qualitative and quantitative approaches. The qualitative methodology involves the Focus Group and the Delphi technique. TA 7284 The quantitative methodology involves surveying and analyzing self-perception (descriptive and analytical in relation to personal and professional variables and levels of excellence). The methodology was piloted among primary care nurses. Competencies were analyzed and distributed across the training program. The combination of qualitative and quantitative methods showed that obtaining a deep insight into the nurses' competencies would be a good process. This proposal is applicable as an approach to global nursing competencies or to a particular specialty.Lung cancer is the leading cause of death in the world, and the most common type of lung cancer is non-small-cell lung cancer (NSCLC), accounting for 85% of lung cancer. Patients with NSCLC, when detected, are mostly in a metastatic stage, and over half of patients diagnosed with NSCLC die within one year after diagnosis; the 5-year survival rate is 24%. However, in patients with metastatic NSCLC, the 5-year survival rate is 6%. Therefore, development of a new therapeutic agent or strategy is urgent for NSCLCs. Berberine has been illustrated to be a therapeutic agent of NSCLC. In the present study, we synthesized six derivatives of berberine, and the anti-NSCLC activity of these agents was examined. Some of them exert increasing proliferation inhibition comparing with berberine. Further studies demonstrated that two of the most effective agents, 9-O-decylberberrubine bromide (B6) and 9-O-dodecylberberrubine bromide (B7), performed cell cycle regulation, in-vitro tumorigenesis inhibition and autophagic flux blocking, but not induction of cellular apoptosis in NSCLC cells. Moreover, B6 and B7 were determined to be green fluorescent and could be penetrated and localized in cellular mitochondria. Herein, B6 and B7, the berberine derivatives we synthesized, revealed better anti-NSCLC activity with berberine and may be used as therapeutic candidates for the treatment of NSCLCs.The technology of microbial conversion provides a potential way to exploit compounds of biotechnological potential. The red pigment prodigiosin (PG) and other PG-like pigments from bacteria, majorly from Serratia marcescens, have been reported as bioactive secondary metabolites that can be used in the broad fields of agriculture, fine chemicals, and pharmacy. Increasing PG productivity by investigating the culture conditions especially the inexpensive carbon and nitrogen (C/N) sources has become an important factor for large-scale production. Investigations into the bioactivities and applications of PG and its related compounds have also been given increased attention. To save production cost, chitin and protein-containing fishery byproducts have recently been investigated as the sole C/N source for the production of PG and chitinolytic/proteolytic enzymes. This strategy provides an environmentally-friendly selection using inexpensive C/N sources to produce a high yield of PG together with chitinolytic and proteolytic enzymes by S. marcescens. The review article will provide effective references for production, bioactivity, and application of S. marcescens PG in various fields such as biocontrol agents and potential pharmaceutical drugs.The metabolome of an organism depends on environmental factors and intracellular regulation and provides information about the physiological conditions. Metabolomics helps to understand disease progression in clinical settings or estimate metabolite overproduction for metabolic engineering. The most popular analytical metabolomics platform is mass spectrometry (MS). However, MS metabolome data analysis is complicated, since metabolites interact nonlinearly, and the data structures themselves are complex. Machine learning methods have become immensely popular for statistical analysis due to the inherent nonlinear data representation and the ability to process large and heterogeneous data rapidly. In this review, we address recent developments in using machine learning for processing MS spectra and show how machine learning generates new biological insights. In particular, supervised machine learning has great potential in metabolomics research because of the ability to supply quantitative predictions. We review here commonly used tools, such as random forest, support vector machines, artificial neural networks, and genetic algorithms.TA 7284