Digital Forms of Commensality in the 21st Century: A Scoping Review.

The social act of eating together has been influenced and mediated by technologies in recent decades. This phenomenon has been investigated in different academic fields, but the topic is still in an incipient dimension, and there is a lack of consensus regarding terminology and definitions. The study aimed to characterize the main scientific findings regarding digital forms of commensality in the 21st century and to identify possible relationships between these practices and public health. A scoping review was conducted to identify papers published in different languages between 2001 and 2021. A total of 104 publications that combined commensality and technology in all contexts were included. Most studies were qualitative; from the Design and Technology field; used social media and video platforms or prototypes/augmented reality gadgets; and used different terms to refer to digital forms of commensality, allowing the analysis of the construction of field definitions over time. The intersections with health were observed from impacts on family/community engagement, culinary skills development, and mental health and eating habits. These practices also structured specific social interactions, such as virtual food communities and commensality, during the COVID-19 pandemic. This paper indicates the consistent growth of these practices and recommends the development of future research for theoretically and longitudinally deeper evaluations of the impacts of these new ways of eating together, especially regarding their effects on human health.

The spread of visceral leishmaniasis in Brazil: the first canine cases described in Ji-Paraná, Rondônia, Brazil.

Leishmaniases are zoonotic diseases caused by protozoa of the genus Leishmania. The disease has two clinical manifestations described in humans: visceral (VL) and cutaneous (CL) leishmaniasis. In Brazil, there has been an expansion of human VL. The participation of the dog as a reservoir of Leishmania infantum, the agent of VL, is important for the epidemiology of the disease since canine cases generally precede human cases. The present study aimed to evaluate the occurrence of Leishmania spp. infection in dogs in the municipality of Ji-Paraná by PCR assays using blood samples. Leishmania DNA was detected in two of the 105 studied dogs. The PCR products were sequenced and confirmed that the two samples (1.90%) correspond to L. infantum. The dogs had allochthonous history. Therefore, the positive results found here should serve as a warning to public health agencies. This is because Ji-Paraná is the third municipality to register cases of canine leishmaniasis (CanL) in Rondônia state. Thus, reinforcing the importance of expanding studies on the epidemiology and surveillance of VL in the region.

Combining unsupervised and supervised learning for predicting the final stroke lesion.

Predicting the final ischaemic stroke lesion provides crucial information regarding the volume of salvageable hypoperfused tissue, which helps physicians in the difficult decision-making process of treatment planning and intervention. Treatment selection is influenced by clinical diagnosis, which requires delineating the stroke lesion, as well as characterising cerebral blood flow dynamics using neuroimaging acquisitions. Nonetheless, predicting the final stroke lesion is an intricate task, due to the variability in lesion size, shape, location and the underlying cerebral haemodynamic processes that occur after the ischaemic stroke takes place. Moreover, since elapsed time between stroke and treatment is related to the loss of brain tissue, assessing and predicting the final stroke lesion needs to be performed in a short period of time, which makes the task even more complex. Therefore, there is a need for automatic methods that predict the final stroke lesion and support physicians in the treatment decision process. We propose a fully automatic deep learning method based on unsupervised and supervised learning to predict the final stroke lesion after 90 days. Our aim is to predict the final stroke lesion location and extent, taking into account the underlying cerebral blood flow dynamics that can influence the prediction. To achieve this, we propose a two-branch Restricted Boltzmann Machine, which provides specialized data-driven features from different sets of standard parametric Magnetic Resonance Imaging maps. These data-driven feature maps are then combined with the parametric Magnetic Resonance Imaging maps, and fed to a Convolutional and Recurrent Neural Network architecture. We evaluated our proposal on the publicly available ISLES 2017 testing dataset, reaching a Dice score of 0.38, Hausdorff Distance of 29.21 mm, and Average Symmetric Surface Distance of 5.52 mm.

The spread of visceral leishmaniasis in Brazil: the first canine cases described in Ji-Paraná, Rondônia, Brazil / A disseminação da leishmaniose visceral no Brasil: os primeiros casos caninos descritos em Ji-Paraná, Rondônia, Brasil

Abstract Leishmaniases are zoonotic diseases caused by protozoa of the genus Leishmania. The disease has two clinical manifestations described in humans: visceral (VL) and cutaneous (CL) leishmaniasis. In Brazil, there has been an expansion of human VL. The participation of the dog as a reservoir of Leishmania infantum, the agent of VL, is important for the epidemiology of the disease since canine cases generally precede human cases. The present study aimed to evaluate the occurrence of Leishmania spp. infection in dogs in the municipality of Ji-Paraná by PCR assays using blood samples. Leishmania DNA was detected in two of the 105 studied dogs. The PCR products were sequenced and confirmed that the two samples (1.90%) correspond to L. infantum. The dogs had allochthonous history. Therefore, the positive results found here should serve as a warning to public health agencies. This is because Ji-Paraná is the third municipality to register cases of canine leishmaniasis (CanL) in Rondônia state. Thus, reinforcing the importance of expanding studies on the epidemiology and surveillance of VL in the region.

Resumo As leishmanioses são doenças causadas por protozoários do gênero Leishmania. A doença apresenta duas manifestações clínicas: leishmaniose visceral (LV) e cutânea (LC). No Brasil, a LV está em expansão. A participação do cão como reservatório é importante para a epidemiologia da doença, pois os casos caninos geralmente precedem os humanos. O presente estudo avaliou a ocorrência de LV em cães (LVC) do município de Ji-Paraná por meio de ensaios de PCR, utilizando-se amostras de sangue. O DNA de Leishmania foi detectado em dois dos 105 cães estudados. Os produtos da PCR foram sequenciados e confirmaram que duas amostras (1,90%) eram Leishmania infantum. Os cães tinham histórico alóctone. Os resultados positivos encontrados servem de alerta aos órgãos públicos de saúde. Isso porque Ji-Paraná é o terceiro município a registrar casos de LVC no estado de Rondônia. Dessa forma, reforça-se a importância da ampliação dos estudos sobre a epidemiologia e vigilância da LV na região.

Adaptive Feature Recombination and Recalibration for Semantic Segmentation With Fully Convolutional Networks.

Fully convolutional networks have been achieving remarkable results in image semantic segmentation, while being efficient. Such efficiency results from the capability of segmenting several voxels in a single forward pass. So, there is a direct spatial correspondence between a unit in a feature map and the voxel in the same location. In a convolutional layer, the kernel spans over all channels and extracts information from them. We observe that linear recombination of feature maps by increasing the number of channels followed by compression may enhance their discriminative power. Moreover, not all feature maps have the same relevance for the classes being predicted. In order to learn the inter-channel relationships and recalibrate the channels to suppress the less relevant ones, squeeze and excitation blocks were proposed in the context of image classification with convolutional neural networks. However, this is not well adapted for segmentation with fully convolutional networks since they segment several objects simultaneously, hence a feature map may contain relevant information only in some locations. In this paper, we propose recombination of features and a spatially adaptive recalibration block that is adapted for semantic segmentation with fully convolutional networks- the SegSE block. Feature maps are recalibrated by considering the cross-channel information together with spatial relevance. The experimental results indicate that recombination and recalibration improve the results of a competitive baseline, and generalize across three different problems: brain tumor segmentation, stroke penumbra estimation, and ischemic stroke lesion outcome prediction. The obtained results are competitive or outperform the state of the art in the three applications.

Stroke Lesion Outcome Prediction Based on MRI Imaging Combined With Clinical Information.

In developed countries, the second leading cause of death is stroke, which has the ischemic stroke as the most common type. The preferred diagnosis procedure involves the acquisition of multi-modal Magnetic Resonance Imaging. Besides detecting and locating the stroke lesion, Magnetic Resonance Imaging captures blood flow dynamics that guides the physician in evaluating the risks and benefits of the reperfusion procedure. However, the decision process is an intricate task due to the variability of lesion size, shape, and location, as well as the complexity of the underlying cerebral hemodynamic process. Therefore, an automatic method that predicts the stroke lesion outcome, at a 3-month follow-up, would provide an important support to the physicians' decision process. In this work, we propose an automatic deep learning-based method for stroke lesion outcome prediction. Our main contribution resides in the combination of multi-modal Magnetic Resonance Imaging maps with non-imaging clinical meta-data: the thrombolysis in cerebral infarction scale, which categorizes the success of recanalization, achieved through mechanical thrombectomy. In our proposal, this clinical information is considered at two levels. First, at a population level by embedding the clinical information in a custom loss function used during training of our deep learning architecture. Second, at a patient-level through an extra input channel of the neural network used at testing time for a given patient case. By merging imaging with non-imaging clinical information, we aim to obtain a model aware of the principal and collateral blood flow dynamics for cases where there is no perfusion beyond the point of occlusion and for cases where the perfusion is complete after the occlusion point.

Automatic brain tissue segmentation in MR images using Random Forests and Conditional Random Fields.

BACKGROUND: The segmentation of brain tissue into cerebrospinal fluid, gray matter, and white matter in magnetic resonance imaging scans is an important procedure to extract regions of interest for quantitative analysis and disease assessment. Manual segmentation requires skilled experts, being a laborious and time-consuming task; therefore, reliable and robust automatic segmentation methods are necessary. NEW METHOD: We propose a segmentation framework based on a Conditional Random Field for brain tissue segmentation, with a Random Forest encoding the likelihood function. The features include intensities, gradients, probability maps, and locations. Additionally, skull stripping is critical for achieving an accurate segmentation; thus, after extracting the brain we propose to refine its boundary during segmentation. RESULTS: The proposed framework was evaluated on the MR Brain Image Segmentation Challenge and the Internet Brain Segmentation Repository databases. The segmentations of brain tissues obtained with the proposed algorithm were competitive both in normal and diseased subjects. The skull stripping refinement significantly improved the results, when comparing against no refinement. COMPARISON WITH EXISTING METHODS: In the MR Brain Image Segmentation Challenge database, the results were competitive when comparing with top methods. In the Internet Brain Segmentation Repository database, the proposed approach outperformed other well-established algorithms. CONCLUSIONS: The combination of a Random Forest and Conditional Random Field for brain tissue segmentation performed well for normal and diseased subjects. Additionally, refinement of the skull stripping at segmentation time is feasible in learning-based methods and significantly improves the segmentation of cerebrospinal fluid and intracranial volume.

Brain Tumor Segmentation Using Convolutional Neural Networks in MRI Images.

Among brain tumors, gliomas are the most common and aggressive, leading to a very short life expectancy in their highest grade. Thus, treatment planning is a key stage to improve the quality of life of oncological patients. Magnetic resonance imaging (MRI) is a widely used imaging technique to assess these tumors, but the large amount of data produced by MRI prevents manual segmentation in a reasonable time, limiting the use of precise quantitative measurements in the clinical practice. So, automatic and reliable segmentation methods are required; however, the large spatial and structural variability among brain tumors make automatic segmentation a challenging problem. In this paper, we propose an automatic segmentation method based on Convolutional Neural Networks (CNN), exploring small 3 ×3 kernels. The use of small kernels allows designing a deeper architecture, besides having a positive effect against overfitting, given the fewer number of weights in the network. We also investigated the use of intensity normalization as a pre-processing step, which though not common in CNN-based segmentation methods, proved together with data augmentation to be very effective for brain tumor segmentation in MRI images. Our proposal was validated in the Brain Tumor Segmentation Challenge 2013 database (BRATS 2013), obtaining simultaneously the first position for the complete, core, and enhancing regions in Dice Similarity Coefficient metric (0.88, 0.83, 0.77) for the Challenge data set. Also, it obtained the overall first position by the online evaluation platform. We also participated in the on-site BRATS 2015 Challenge using the same model, obtaining the second place, with Dice Similarity Coefficient metric of 0.78, 0.65, and 0.75 for the complete, core, and enhancing regions, respectively.

Brain Tumour Segmentation based on Extremely Randomized Forest with high-level features.

Gliomas are among the most common and aggressive brain tumours. Segmentation of these tumours is important for surgery and treatment planning, but also for follow-up evaluations. However, it is a difficult task, given that its size and locations are variable, and the delineation of all tumour tissue is not trivial, even with all the different modalities of the Magnetic Resonance Imaging (MRI). We propose a discriminative and fully automatic method for the segmentation of gliomas, using appearance- and context-based features to feed an Extremely Randomized Forest (Extra-Trees). Some of these features are computed over a non-linear transformation of the image. The proposed method was evaluated using the publicly available Challenge database from BraTS 2013, having obtained a Dice score of 0.83, 0.78 and 0.73 for the complete tumour, and the core and the enhanced regions, respectively. Our results are competitive, when compared against other results reported using the same database.

Utilização de indicadores de qualidade para avaliação da assistência de enfermagem: opinião dos enfermeiros / Use of quality indicators for evaluation of nursing care: nurses' opinions

Objetivo - Os indicadores de qualidade são utilizados nos processos de trabalho nas instituições de saúde, que visam à assistência de enfermagem livre de riscos para o paciente, colaborador e instituição. Este estudo tem como objetivo verificar a opinião dos enfermeiros sobre a utilização dos indicadores de qualidade como metodologia de avaliação da assistência de enfermagem. Métodos - Este estudo foi realizado em um hospital do interior do Estado de São Paulo, com 30 enfermeiros do período diurno e noturno que atuam na assistência direta ao paciente. Os dados foram coletados pelos pesquisadores no período de junho e julho de 2009. Resultados - Evidenciou-se, neste estudo que os enfermeiros têm conhecimento sobre o tema sugerido e todos acreditam que a utilização dos indicadores pode contribuir para a melhoria de assistência de enfermagem prestada aos pacientes. Foram citados como principais indicadores: Controle da infecção hospitalar, Prevenção de erros na administração de medicamentos, Índice de satisfação do cliente, Cuidados na prevenção de flebites, Prevenção de quedas. Conclusão - Os enfermeiros têm a opinião correta sobre a adequação da utilização dos indicadores de qualidade no processo de trabalho e conhecem os indicadores que podem ser utilizados nos processos.

Objective - The quality indicators are used in work processes in health care institutions, aimed at nursing care free of risks for the patient, employee and institution. This study aims to determine the views of nurses on the use of quality indicators and assessment methodology of nursing care. Methods - This study was performed in a hospital in the State of São Paulo, with 30 nurses during the day and night working in direct patient care, data were collected by researchers during June and July 2009. Results - It was evidenced in this study that nurses have knowledge about the issue and suggested everyone believes that the use of indicators can contribute to the improvement of nursing care provided to patients, were cited as key indicators; Control of hospital infection, Prevention of errors in medication administration, Customer satisfaction index, Care in the prevention of phlebitis, Prevention of falls. Conclusion - Nurses have the right opinion on the appropriateness of the use of quality indicators in the work process and they know the indicators that can be used in processes.

Rotura de angiomiolipoma: relato de caso / Renal angiomyolipoma rupture: case report

Os angiomiolipomas são lesões geralmente benignas. Caracterizamsepela presença de tecido adiposo maduro, músculo liso e vasos sangüíneos com parede espessada. Os angiomiolipomas são lesões assintomáticas, porém em 10% dos casos, geralmente em tumoresmaiores, pode ocorrer sangramento intenso, acompanhado de choquehipovolêmico. Relata-se o caso de uma mulher de 33 anos, com rotura espontânea de um angiomiolipoma, tratada inicialmente por embolização da lesão, que estabilizou o quadro clínico da paciente, sendo posteriormente realizada nefrectomia parcial.