护士长领导行为和医院护理安全文化相关性研究

目的 旨在探讨护士长的领导行为和医院护理安全文化的关系。方法 采用便利抽样的方法对黑龙江省3所三级甲等中医院的344名护士进行了多元化护士长领导行为、护理安全文化的调查。调查所得数据全部输入SPSS17.0软件进行分析,最后得出结论。结果 护士长领导行为得分最高为领导魅力,其次为被动例外管理、个性化关怀、主动例外管理、感召力、权变奖赏、智能激发。护理安全文化总体稍高于中等水平,其中团队氛围、对工作的满意度、单位安全的氛围、对管理的感受高于中等水平,对压力的认知得分低于中等水平。结论 护士感知的护士长领导行为各维度与护理安全文化各维度之间具有正相关。     

Identification of synthetic lethality based on a functional network by using machine learning algorithms

Synthetic lethality is the synthesis of mutations leading to cell death. Tumor-specific synthetic lethality has been targeted in research to improve cancer therapy. With the advances of techniques in molecular biology, such as RNAi and CRISPR/Cas9 gene editing, efforts have been made to systematically identify synthetic lethal interactions, especially for frequently mutated genes in cancers. However, elucidating the mechanism of synthetic lethality remains a challenge because of the complexity of its influencing conditions. In this study, we proposed a new computational method to identify critical functional features that can accurately predict synthetic lethal interactions. This method incorporates several machine learning algorithms and encodes protein-coding genes by an enrichment system derived from gene ontology terms and Kyoto Encyclopedia of Genes and Genomes pathways to represent their functional features. We built a random forest-based prediction engine by using 2120 selected features and obtained a Matthews correlation coefficient of 0.532. We examined the top 15 features and found that most of them have potential roles in synthetic lethality according to previous studies. These results demonstrate the ability of our proposed method to predict synthetic lethal interactions and provide a basis for further characterization of these particular genetic combinations.     

Grey matter damage in multiple sclerosis

Over the past decade, immunohistochemical studies have provided compelling evidence that gray matter (GM) pathology in multiple sclerosis (MS) is extensive. Until recently, this GM pathology was difficult to visualize using standard magnetic resonance imaging (MRI) techniques. However, with newly developed MRI sequences, it has become clear that GM damage is present from the earliest stages of the disease and accrues with disease progression. GM pathology is clinically relevant, as GM lesions and/or GM atrophy were shown to be associated with MS motor deficits and cognitive impairment. Recent autopsy studies demonstrated significant GM demyelination and microglia activation. However, extensive immune cell influx, complement activation and blood-brain barrier leakage, like in WM pathology, are far less prominent in the GM. Hence, so far, the cause of GM damage in MS remains unknown, although several plausible underlying pathogenic mechanisms have been proposed. This paper provides an overview of GM damage in MS with a focus on its topology and histopathology.     

BIOREL: the benchmark resource to estimate the relevance of the gene networks

The progress of high-throughput methodologies in functional genomics has lead to the development of statistical procedures to infer gene networks from various types of high-throughput data. However, due to the lack of common standards, the biological significance of the results of the different studies is hard to compare. To overcome this problem we propose a benchmark procedure and have developed a web resource (BIOREL), which is useful for estimating the biological relevance of any genetic network by integrating different sources of biological information. The associations of each gene from the network are classified as biologically relevant or not. The proportion of genes in the network classified as "relevant" is used as the overall network relevance score. Employing synthetic data we demonstrated that such a score ranks the networks fairly in respect to the relevance level. Using BIOREL as the benchmark resource we compared the quality of experimental and theoretically predicted protein interaction data.     

Traumatic Amputation: A Case of Laotian Indignation and Injustice

Culture is an essential variable of diagnosis and treatment. A cultural perspective draws attention to the social context within which symptoms arise, are given meaning, and are managed. Ethno-cultural work on illness narratives suggests that most people can provide culturally-based explanations for their symptoms. While these explanations are inconsistent with biomedical theory, they relieve patient distress by allowing the patient to create meaning for symptoms. Exploring the characteristics, context, and antecedents of the symptoms enables the patient to convey them to the clinician who may have a divergent explanation of sickness. This case study uses the Outline for Cultural Formulation of the DSM-IV created for clinicians to elicit a narrative account of the illness experience from the patient. Our study examines how the patient, a Laotian used social indignation (“Kwam khem keuang”) as an explanatory model for his ailment. He was diagnosed with post-traumatic stress disorder after having undergone a traumatic amputation. In the process of explaining his illness through a cultural idiom, the patient was able to reveal both personal and collective meaning of repressed anger and frustration, expressing them in a context that was acceptable to him. This cultural idiom allowed the patient to reflect upon the structure of the health care system and the specific context in which symptoms and their possible origins are recounted and explored. It also clarified to the treating clinicians some categories of experience and causal explanations that did not fit easily with western biomedical and psychiatric understanding. The case study illustrates how a cultural approach to illness from the patient’s perspective offers a reflexive stance on the clinician–patient interaction that allows for better patient care.     

Science‐Driven Restoration: A Square Grid on a Round Earth?

Is formal science necessarily an effective framework and methodology for designing and implementing ecological restoration programs? My experience as an ecologist in Hawaii suggests that even when scientific research programs are explicitly designed to guide and facilitate restoration, the culture of science, heterogeneity of nature, and real‐world complexities of implementing land management practices often limit the practical relevance of conventional scientific research. Although alternative models such as adaptive management and transdisciplinary science may facilitate research that more robustly models the real world, there is often little professional support or incentive to orient even these nonconventional research approaches toward actually solving on‐the‐ground problems. Thus, if one’s goal is to accomplish ecological restoration as quickly and efficiently as possible, a trial‐and‐error/intelligent tinkering–type approach might often be better than using more rigorous, data‐intensive scientific methodology. However, the sympatric implementation of ecological restoration and scientific research programs can lead to valuable synergies such as mutual logistical and financial support and the exchange of distinct forms of knowledge. The professional activities and mere presence of scientists can also greatly enhance a program’s prestige and visibility, which in turn can indirectly promote more and better ecological restoration. Improving our understanding of when formal science can directly assist restoration projects and when its value will more likely be synergistic and indirect could lead to better science, better ecological restoration, and better relationships between these two cultures.     

On responder analyses when a continuous variable is dichotomized and measurement error is present

In clinical studies results are often reported as proportions of responders, i.e. the proportion of subjects who fulfill a certain response criterion is reported, although the underlying variable of interest is continuous. In this paper, we consider the situation where a subject is defined as a responder if the (error-free) continuous measurements post-treatment are below a certain fraction of (error-free) continuous measurements obtained pre-treatment. Focus is on the one-sample case, but an extension to the two-sample case is also presented. The bias of different estimates for the proportion of responders is derived and compared. In addition, an asymptotically unbiased ML-type estimate for the proportion of responders is presented. The results are illustrated using data obtained in a clinical study investigating pre-menstrual dysphoric disorder (PMDD).     

Predict and analyze S-nitrosylation modification sites with the mRMR and IFS approaches

S-nitrosylation (SNO) is one of the most important and universal post-translational modifications (PTMs) which regulates various cellular functions and signaling events. Identification of the exact S-nitrosylation sites in proteins may facilitate the understanding of the molecular mechanisms and biological function of S-nitrosylation. Unfortunately, traditional experimental approaches used for detecting S-nitrosylation sites are often laborious and time-consuming. However, computational methods could overcome this demerit. In this work, we developed a novel predictor based on nearest neighbor algorithm (NNA) with the maximum relevance minimum redundancy (mRMR) method followed by incremental feature selection (IFS). The features of physicochemical/biochemical properties, sequence conservation, residual disorder, amino acid occurrence frequency, second structure and the solvent accessibility were utilized to represent the peptides concerned. Feature analysis showed that the features except residual disorder affected identification of the S-nitrosylation sites. It was also shown via the site-specific feature analysis that the features of sites away from the central cysteine might contribute to the S-nitrosylation site determination through a subtle manner. It is anticipated that our prediction method may become a useful tool for identifying the protein S-nitrosylation sites and that the features analysis described in this paper may provide useful insights for in-depth investigation into the mechanism of S-nitrosylation.     

Broad-scale adaptive genetic variation in alpine plants is driven by temperature and precipitation

Identifying adaptive genetic variation is a challenging task, in particular in non-model species for which genomic information is still limited or absent. Here, we studied distribution patterns of amplified fragment length polymorphisms (AFLPs) in response to environmental variation, in 13 alpine plant species consistently sampled across the entire European Alps. Multiple linear regressions were performed between AFLP allele frequencies per site as dependent variables and two categories of independent variables, namely Moran's eigenvector map MEM variables (to account for spatial and unaccounted environmental variation, and historical demographic processes) and environmental variables. These associations allowed the identification of 153 loci of ecological relevance. Univariate regressions between allele frequency and each environmental factor further showed that loci of ecological relevance were mainly correlated with MEM variables. We found that precipitation and temperature were the best environmental predictors, whereas topographic factors were rarely involved in environmental associations. Climatic factors, subject to rapid variation as a result of the current global warming, are known to strongly influence the fate of alpine plants. Our study shows, for the first time for a large number of species, that the same environmental variables are drivers of plant adaptation at the scale of a whole biome, here the European Alps.     

Ranking Enzyme Structures in the PDB by Bound Ligand Similarity to Biological Substrates

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