A estrutura do conhecimento em enfermagem

Propõe uma estruturação do conhecimento em enfermagem à luz de um quadro referencial teórico, caracterizando-se os conteúdos, seus limites e suas relações com as demais áreas da ciência. Identificam-se linhas e ciclos curriculares com vistas ao delineamento do perfil profissional centrado na demanda do sistema social. A estrutura do conhecimento visa fundamentar a organização curricular subseqüenteA structuring of knowledge in nursing is proposed, considering a theoretical reference framework, a characterization being made of its contents, its limits and its relation to other fields of science. Curricular lines and cycles are identified, to the effect of delineating the professional profile centered on social system demand. Knowledge structure aims to support ensuing curricular organization

Fatores condicionantes da eficácia de desinfetantes

São abordados os fatores que condicionam a efi- cácia dos desinfetantes qufmicos, como o tipo de agente causal, a dose infectante, o tempo de atuação, a concentração, a temperatu ra, a matéria orgân ica presente e o tipo de suporte ou do material de construção empregado.Factors which condition the efficacy of chemical disinfectants are analyzed, such as causal agent, infecting dosage, acting span, concentration, temperatura, organic matter present, and the kind of support or building material cmployed

Avaliação nutricional de farelo de escargot

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Triagem da atividade antibacteriana de condimentos vegetais sobre inóculos padronizados

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Concentrações inibitórias e bactericidas mínimas em filtrado de quefir artesanal

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Avaliação de atividade antimicrobiana de alho, manjerona e orégano em alimentos lácteos

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The ENIGMA-Epilepsy working group: Mapping disease from large data sets

Epilepsy is a common and serious neurological disorder, with many different constituent conditions characterized by their electro clinical, imaging, and genetic features. MRI has been fundamental in advancing our understanding of brain processes in the epilepsies. Smaller‐scale studies have identified many interesting imaging phenomena, with implications both for understanding pathophysiology and improving clinical care. Through the infrastructure and concepts now well‐established by the ENIGMA Consortium, ENIGMA‐Epilepsy was established to strengthen epilepsy neuroscience by greatly increasing sample sizes, leveraging ideas and methods established in other ENIGMA projects, and generating a body of collaborating scientists and clinicians to drive forward robust research. Here we review published, current, and future projects, that include structural MRI, diffusion tensor imaging (DTI), and resting state functional MRI (rsfMRI), and that employ advanced methods including structural covariance, and event‐based modeling analysis. We explore age of onset‐ and duration‐related features, as well as phenomena‐specific work focusing on particular epilepsy syndromes or phenotypes, multimodal analyses focused on understanding the biology of disease progression, and deep learning approaches. We encourage groups who may be interested in participating to make contact to further grow and develop ENIGMA‐Epilepsy

Structural brain abnormalities in the common epilepsies assessed in a worldwide ENIGMA study

Progressive functional decline in the epilepsies is largely unexplained. We formed the ENIGMA-Epilepsy consortium to understand factors that influence brain measures in epilepsy, pooling data from 24 research centres in 14 countries across Europe, North and South America, Asia, and Australia. Structural brain measures were extracted from MRI brain scans across 2149 individuals with epilepsy, divided into four epilepsy subgroups including idiopathic generalized epilepsies (n =367), mesial temporal lobe epilepsies with hippocampal sclerosis (MTLE; left, n = 415; right, n = 339), and all other epilepsies in aggregate (n = 1026), and compared to 1727 matched healthy controls. We ranked brain structures in order of greatest differences between patients and controls, by meta-Analysing effect sizes across 16 subcortical and 68 cortical brain regions. We also tested effects of duration of disease, age at onset, and age-by-diagnosis interactions on structural measures. We observed widespread patterns of altered subcortical volume and reduced cortical grey matter thickness. Compared to controls, all epilepsy groups showed lower volume in the right thalamus (Cohen's d = \uc3\ua2 '0.24 to \uc3\ua2 '0.73; P < 1.49 \uc3\u97 10 \uc3\ua2 '4), and lower thickness in the precentral gyri bilaterally (d = \uc3\ua2 '0.34 to \uc3\ua2 '0.52; P < 4.31 \uc3\u97 10 \uc3\ua2 '6). Both MTLE subgroups showed profound volume reduction in the ipsilateral hippocampus (d = \uc3\ua2 '1.73 to \uc3\ua2 '1.91, P < 1.4 \uc3\u97 10 \uc3\ua2 '19), and lower thickness in extrahippocampal cortical regions, including the precentral and paracentral gyri, compared to controls (d = \uc3\ua2 '0.36 to \uc3\ua2 '0.52; P < 1.49 \uc3\u97 10 \uc3\ua2 '4). Thickness differences of the ipsilateral temporopolar, parahippocampal, entorhinal, and fusiform gyri, contralateral pars triangularis, and bilateral precuneus, superior frontal and caudal middle frontal gyri were observed in left, but not right, MTLE (d = \uc3\ua2 '0.29 to \uc3\ua2 '0.54; P < 1.49 \uc3\u97 10 \uc3\ua2 '4). Contrastingly, thickness differences of the ipsilateral pars opercularis, and contralateral transverse temporal gyrus, were observed in right, but not left, MTLE (d = \uc3\ua2 '0.27 to \uc3\ua2 '0.51; P < 1.49 \uc3\u97 10 \uc3\ua2 '4). Lower subcortical volume and cortical thickness associated with a longer duration of epilepsy in the all-epilepsies, all-other-epilepsies, and right MTLE groups (beta, b < \uc3\ua2 '0.0018; P < 1.49 \uc3\u97 10 \uc3\ua2 '4). In the largest neuroimaging study of epilepsy to date, we provide information on the common epilepsies that could not be realistically acquired in any other way. Our study provides a robust ranking of brain measures that can be further targeted for study in genetic and neuropathological studies. This worldwide initiative identifies patterns of shared grey matter reduction across epilepsy syndromes, and distinctive abnormalities between epilepsy syndromes, which inform our understanding of epilepsy as a network disorder, and indicate that certain epilepsy syndromes involve more widespread structural compromise than previously assumed

Federated learning enables big data for rare cancer boundary detection.

Although machine learning (ML) has shown promise across disciplines, out-of-sample generalizability is concerning. This is currently addressed by sharing multi-site data, but such centralization is challenging/infeasible to scale due to various limitations. Federated ML (FL) provides an alternative paradigm for accurate and generalizable ML, by only sharing numerical model updates. Here we present the largest FL study to-date, involving data from 71 sites across 6 continents, to generate an automatic tumor boundary detector for the rare disease of glioblastoma, reporting the largest such dataset in the literature (n = 6, 314). We demonstrate a 33% delineation improvement for the surgically targetable tumor, and 23% for the complete tumor extent, over a publicly trained model. We anticipate our study to: 1) enable more healthcare studies informed by large diverse data, ensuring meaningful results for rare diseases and underrepresented populations, 2) facilitate further analyses for glioblastoma by releasing our consensus model, and 3) demonstrate the FL effectiveness at such scale and task-complexity as a paradigm shift for multi-site collaborations, alleviating the need for data-sharing

Author Correction: Federated learning enables big data for rare cancer boundary detection.

10.1038/s41467-023-36188-7NATURE COMMUNICATIONS14