Immunoprofiling of oral squamous cell carcinomas reveals high p63 and survivin expression

Peer Reviewedhttp://deepblue.lib.umich.edu/bitstream/2027.42/106715/1/odi12136.pd

Who is in charge? A review and a research agenda on the 'human side' of the circular economy

The adoption of the circular economy (CE) at the firm level has rarely intersected with human resource management (HRM) – here called 'the human side of organizations' – and these two fields remain largely separate areas of knowledge. While the literature on the CE is expanding, discussion of its implementation in organizations is, so far, rare, along with exploration of the necessary alignment of the CE with green human resource management (GHRM). In this article, we extend the state-of-the-art literature on CE business models through the inclusion of the ‘human side’ of such issues. This goal is met by offering an original integrative GHRM framework for organizations developing CE. The theoretical lenses of stakeholders' theory and the resource based view (RBV) form the foundation of this framework, which represents a 'middle range theory'. We underline the practices and dimensions of the links between GHRM and the 'ReSOLVE' CE model. Through an exploration of this integrative framework, we propose a future research agenda along with original research propositions. Furthermore, the middle-range integrated theoretical framework we propose can serve both academics and practitioners in developing understanding of the human resource management (HRM) and change management aspects of the CE

GESTAÇÃO DE RISCO: PERCEPÇÃO E SENTIMENTOS DAS GESTANTES COM AMNIORREXE PREMATURA

Premature amniorrhexis, risky pregnancy, became a global concern because of the harm to the mother and fetus. Aim to identify the knowledge of women of premature amniorrhexis and get to know their perceptions and their feelings about this pathology. Descriptive study with a qualitative approach, developed in a public maternity ward. 11 women participated while hospitalized with a diagnosis of premature amniorrhexis, in the months of September and October 2007. The collection of semi-structured data was used, from which emerged the categories: Women's knowledge in relation to premature amniorrhexis, requiring information, and their feelings experienced, and reaction before their water broke. The results showed that women know little of the disease, or fear for the life of their children, and do not know what to do before that. Additionally health care professionals should regard women as human beings that need support and understanding.Amniorrexe prematuro, embarazo de riesgo, se convirtió en preocupación mundial debido a los agravios en la gestante y en el feto. Objetivamos identificar el conocimiento de las gestantes sobre amniorrexe prematura y conocer sus percepciones y sus sentimientos ante esta patología. Estudio descriptivo con un enfoque cualitativo, desarrollado en una maternidad pública. Participaron 11 gestantes hospitalizadas con diagnóstico de amniorrexe prematura, en los meses de septiembre y octubre de 2007. Se utilizó en la recogida de datos entrevista semi-estructurada a partir de la cual surgieron las categorías: conocimiento de las gestantes en relación con amniorrexe prematura, que requieren la información, los sentimientos experimentados, la reacción ante la ruptura de la bolsa de aguas. Los resultados mostraron que las mujeres saben poco de la enfermedad, el temor por la vida de sus hijos y no saben qué hacer ante esta situación. De ahí la necesidad de los profesionales de la salud de mirar a las gestantes como seres que necesitan de apoyo y comprensión.Amniorrexe prematura, gestação de risco, tornou-se preocupação mundial devido os agravos na gestante e no feto. Objetivamos identificar o conhecimento de gestantes sobre amniorrexe prematura e conhecer suas percepções e seus sentimentos diante desta patologia. Estudo descritivo com abordagem qualitativa, desenvolvido em uma maternidade pública. Participaram 11 gestantes internadas com o diagnóstico de amniorrexe prematura, nos meses de setembro e outubro de 2007. Utilizou-se na coleta de dados entrevista semi-estruturada de onde emergiram as categorias: conhecimento das gestantes em relação à amniorrexe prematura, necessitando de informações, sentimentos vivenciados, reação diante do rompimento da bolsa das águas. Os resultados revelaram que as gestantes pouco conhecem da patologia, temem pela vida dos filhos e não sabem o que fazer diante dessa situação. Consideramos então a necessidade dos profissionais de saúde olhar para as gestantes como seres que necessitam de apoio e compreensão

imPlatelet classifier: image-converted RNA biomarker profiles enable blood-based cancer diagnostics

Liquid biopsies offer a minimally invasive sample collection, outperforming traditional biopsies employed for cancer evaluation. The widely used material is blood, which is the source of tumor-educated platelets. Here, we developed the imPlatelet classifier, which converts RNA-sequenced platelet data into images in which each pixel corresponds to the expression level of a certain gene. Biological knowledge from the Kyoto Encyclopedia of Genes and Genomes was also implemented to improve accuracy. Images obtained from samples can then be compared against standard images for specific cancers to determine a diagnosis. We tested imPlatelet on a cohort of 401 non-small cell lung cancer patients, 62 sarcoma patients, and 28 ovarian cancer patients. imPlatelet provided excellent discrimination between lung cancer cases and healthy controls, with accuracy equal to 1 in the independent dataset. When discriminating between noncancer cases and sarcoma or ovarian cancer patients, accuracy equaled 0.91 or 0.95, respectively, in the independent datasets. According to our knowledge, this is the first study implementing an image-based deep-learning approach combined with biological knowledge to classify human samples. The performance of imPlatelet considerably exceeds previously published methods and our own alternative attempts of sample discrimination. We show that the deep-learning image-based classifier accurately identifies cancer, even when a limited number of samples are available.publishedVersio