O processo de cuidar em Unidade de Terapia Intensiva

The environment of Intensive Care Unit (ICU) is commonly referred to as a place where caring is inextricably linked to high technology. The care in ICU often changes the patient into a taxpayer being left apart from its complexity and sometimes seen through a reductionist perspective. Thus, studies circa the care process are needed oriented from a historical ransom, raising the prospect of a more centralized human care. Hence, this study aimed to analyze the care process in a nursing intensive care unit from the perspective of the professional, family and patients. The study is characterized from a qualitative, descriptive and exploratory methodological approach. The actors were participating nursing professionals, patients and family members of an intensive care unit of Mossoró / RN. Data were collected in the period of May-June 2011, through interviews and observation of activities performed by nursing professionals, and their records in the chart. Data analysis was divided into topics and subtopics representing the phases and shapes that formed the collection. The analysis and discussion of the interviews were based on Bardin's proposal, when we created categories from a process of sorting and grouping criteria adequately defined. The observation of nursing records intended to observe the emphasis which is described in those notes as well as their consistency with practice of FCN and resolution 358/2009. The analysis showed that the nursing staff also performs work focused on mechanized activities and technical-bureaucratic institution that seem to override the needs of patients. In an overview, the care provided by professionals occurs either fragmented or insipient, however there is a service that involves other aspects beyond technical-curative practice, considering that major attention is given to the family and patient, focused on the concern of Nursing guiding their actions in not only the performance of procedures. However, the process of humanizing not always ends with an engagement between professional and patient, which mischaracterizes the true meaning of human care. The records also showed a tendency to focus on caring in a positivist line, where, in most cases, the factors of the disease and the obligation to meet the productivity have overshadowed other relevant aspects to a holistic understanding of caring. Regarding FCN Resolution No. 358/2009, which guides a systematization of nursing care, it is confirmed a technical view, a fragmented and superficial view of the patient, as well as a weakness of care, caused by ignorance and unpreparedness of the entire team. The perspective of caring demonstrates a reality with dialectic between what is proposed in a humane nursing and what happens in this performance space. Besides, it was shown a daily full of important considerations that arise in professional practice, in their views and also those people who were participants in the process

Emprego de redes neurais artificiais supervisionadas e não supervisionadas no estudo de parâmetros reológicos de excipientes farmacêuticos sólidos

In this paper artificial neural network (ANN) based on supervised and unsupervised algorithms were investigated for use in the study of rheological parameters of solid pharmaceutical excipients, in order to develop computational tools for manufacturing solid dosage forms. Among four supervised neural networks investigated, the best learning performance was achieved by a feedfoward multilayer perceptron whose architectures was composed by eight neurons in the input layer, sixteen neurons in the hidden layer and one neuron in the output layer. Learning and predictive performance relative to repose angle was poor while to Carr index and Hausner ratio (CI and HR, respectively) showed very good fitting capacity and learning, therefore HR and CI were considered suitable descriptors for the next stage of development of supervised ANNs. Clustering capacity was evaluated for five unsupervised strategies. Network based on purely unsupervised competitive strategies, classic "Winner-Take-All", "Frequency-Sensitive Competitive Learning" and "Rival-Penalize Competitive Learning" (WTA, FSCL and RPCL, respectively) were able to perform clustering from database, however this classification was very poor, showing severe classification errors by grouping data with conflicting properties into the same cluster or even the same neuron. On the other hand it could not be established what was the criteria adopted by the neural network for those clustering. Self-Organizing Maps (SOM) and Neural Gas (NG) networks showed better clustering capacity. Both have recognized the two major groupings of data corresponding to lactose (LAC) and cellulose (CEL). However, SOM showed some errors in classify data from minority excipients, magnesium stearate (EMG) , talc (TLC) and attapulgite (ATP). NG network in turn performed a very consistent classification of data and solve the misclassification of SOM, being the most appropriate network for classifying data of the study. The use of NG network in pharmaceutical technology was still unpublished. NG therefore has great potential for use in the development of software for use in automated classification systems of pharmaceutical powders and as a new tool for mining and clustering data in drug development