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ObjectiveTo assess salt intake and its dietary sources using biochemical and self-report methods and to characterize salt intake according to sociodemographic and disease-related variables in a sample of the Brazilian population.DesignPopulation-based cross-sectional survey.SettingSalt intake was assessed by biochemical (24 h urinary Na excretion) and self-report methods (sodium FFQ, 24 h dietary recall, seasoned-salt questionnaire, discretionary-salt questionnaire and total reported salt intake).ParticipantsAdults and older people (n 517) aged 20–80 years, living in Artur Nogueira, São Paulo, Brazil.ResultsMean salt intake based on 24 h urinary Na excretion and total reported salt intake was 10·5 and 11·0 g/d, respectively; both measures were significantly correlated. Discretionary salt and seasoned salt were the most important sources of salt intake (68·2 %). Men in the study consumed more salt than women as estimated by 24 h urinary Na excretion (11·7 v. 9·6 g salt/d; P<0·0001). Participants known to be hypertensive added more salt to their meals but consumed less salty ultra-processed foods. Waist circumference in both sexes and BMI were positively correlated with salt intake estimated by 24 h urinary Na excretion. In addition, regression analysis revealed that being a young male or having a high waist circumference was a predictor of higher salt intake.ConclusionsSalt intake in this population was well above the recommended amount. The main source of salt intake came from salt added during cooking. Salt intake varied according to sex and waist circumference.

RESUMOO manejo inadequado e a baixa disponibilidade de nutrientes nos solos da Zona da Mata de Minas Gerais, têm resultado em baixas produtividades de pastagens, aparecimento de solos descobertos e perdas de solo por erosão. O objetivo deste trabalho foi avaliar a capacidade das imagens do sensor ASTER em identificar diferentes níveis de degradação de pastagens. A área de estudo inclui parte dos municípios de Viçosa, Teixeiras e São Miguel do Anta, perfazendo cerca de 3.314 ha. Devido às características das pastagens da região, foram utilizados quatro níveis de degradação: leve, moderada, forte e muito forte. A classe que apresentou maior erro de classificação foi a pastagem com nível de degradação muito forte (Pastagem 4), com 53,91% dos pixels classificados, confundindo-se com as demais classes. A pastagem com nível de degradação moderada (Pastagem 2) apresentou a melhor classificação. Da área avaliada, aproximadamente 70% correspondem a pastagens, sendo 56,46% classificadas como fortemente degradadas; 28,73% Mata/Capoeira e 1,54% plantações de café. Os resultados permitiram concluir que as imagens do sensor ASTER apresentaram um potencial satisfatório para separar os diferentes níveis de degradação de pastagens. Palavras-chave: pastagens degradadas, sensoriamento remoto, classificaçãoUse of ASTER sensor images for the identification of levels of pasture degradation ABSTRACTThe improper management and the low availability of nutrients of soils in "Zona da Mata" in Minas Gerais State, Brazil, have led to low productivity of natural pasture, emergence of bare soils and soil losses by erosion. The objective of this work was to evaluate the capacity of ASTER sensor images to identify different levels of degradation in pasture lands. The studied area includes part of Viçosa, Teixeiras, and São Miguel do Anta municipalities, forming a total area around 3,314 ha. Due to natural characteristics of the pasture in this region, four levels of degradation were used: light, moderate, strong, and very strong. The class that showed the highest error in the classification was the very strong, degraded with 53.91% of the classified pixels, not distinguishable from the other classes. The moderate degradation class showed the best classification. From the total evaluated area, approximately 70% corresponded to pasture, 56.46% of which was classified in the strong degradation level, 28.73% to 'Mata/Capoeira' and only 1.54% to coffee plantations.The results permit to conclude that the use of sensor ASTER images was satisfactory to separate degradation levels of pasture lands in the studied area.

Patients undergoing hematopoietic stem cell transplantation (HSCT) are at risk of developing potential drug-drug interactions (PDDIs). The aim of this study was to assess the prevalence of PDDIs that occur in HSCT patients on the day of hematopoietic stem cell infusion. We performed a cross-sectional study based on the evaluation of prescriptions to HSCT patients on the day of infusion (day 0). The PDDIs were analyzed using the DRUG-REAX(®) system and classified according to the severity level, available scientific evidence, time of onset, and potential clinical impact. Forty patients undergoing HSCT were included in this study; 33 patients (82.5%) were exposed to at least one major and one contraindicated PDDI in a concomitant manner. All patients exposed to PDDIs had an increased risk of cardiotoxicity. Most cases of PDDIs were classified as being of major severity (80.9%), with time of onset not specified (61.9%), and with good or excellent scientific evidence (52.4%). HSCT patients have a high prevalence of clinically significant PDDIs. The management of PDDIs requires an approach that includes biochemical tests, installation of cardiac monitors, periodic electrocardiograms, implementation of electronic prescriptions with a PDDI alert system, and availability of the PDDI databases.

Aim To test a theoretical model aiming to understand which characteristics of the professional nursing practice environment most affect patients, professionals and institution outcomes. Design A cross‐sectional and correlational study, using a structural equation model. Methods One thousand seven hundred and seventy‐three staff nurses were recruited using convenience sampling in five Brazilian hospitals from November 2017 to July 2018. Structural equation modelling was used to assess the relationship between the characteristics of the nursing work environment and patients (climate of safety and quality of care), nursing professionals (job satisfaction and emotional exhaustion) and institutions (intention to leave the job) outcomes. The model was tested using the partial least squares method, considering the bootstrapping technique to estimate the results. The path coefficients and their respective 95% confidence intervals were calculated. The quality of fit of the structural model was assessed by calculating the coefficient of determination (R2), the predictive validity coefficient (Q2) and the effect size (f2). Results The characteristics that most affected the outcomes for patients were Nurse manager ability, leadership and support of nurses (λ=0.27), and Staffing and resource adequacy (λ=0.26); for nursing professionals, Staffing and resource adequacy (λ=−0.19), and Collegial nurse–physician relations (λ=0.19); and for institutions, Nurse manager ability, leadership and support of nurses (λ=−0.10), and Collegial nurse–physician relations (λ=−0.10). Conclusion The characteristics of the professional nursing practice environment that most contribute to achieving better outcomes include nurse manager ability, leadership and support of nurses, staffing and resource adequacy, and collegial nurse–physician relations. Impact This study allowed us to assess which strategies should be prioritized in the professional nursing practice environment to achieve better results. Thus, investment in the training of leadership, in the adequacy of resources, and in physician–nurse relations will bring better results for patients, nursing professionals, and institutions.

Background and Objective: To automatically identify patients with diabetes mellitus (DM) who have high risk of developing diabetic foot, via an unsupervised machine learning technique. Methods: We collected a new database containing 54 known risk factors from 250 patients diagnosed with diabetes mellitus. The database also contained a separate validation cohort composed of 73 subjects, where the perceived risk was annotated by expert nurses. A competitive neuron layer-based method was used to automatically split training data into two risk groups. Results: We found that one of the groups was composed of patients with higher risk of developing diabetic foot. The dominant variables that described group membership via our method agreed with the findings from other studies, and indicated a greater risk for developing such a condition. Our method was validated on the available test data, reaching 71% sensitivity, 100% specificity, and 90% accuracy. Conclusions: Unsupervised learning may be deployed to screen patients with diabetes mellitus, pointing out high-risk individuals who require priority follow-up in the prevention of diabetic foot with very high accuracy. The proposed method is automatic and does not require clinical examinations to perform risk assessment, being solely based on the information of a questionnaire answered by patients. 2 Our study found that discriminant variables for predicting risk group membership are highly correlated with expert opinion.

AIM: The aim of this study was to investigate the accuracy of the self-reported measure of adherence and the relation between adherence to warfarin use, demographic and clinical variables, and the satisfaction with the treatment in patients affected by stroke. METHODS: This is a correlational, quantitative, and cross-sectional study, carried out in the outpatient clinics of a public university hospital from October 2017 to April 2018. Sociodemographic and clinical data were collected through interviews and hospital charts, as well as by applying the Measurement of Treatment Adherence (MTA) and the Duke Anticoagulation Satisfaction Scale, in their Brazilian versions. Results of the international normalized ratio (INR) were collected. Measurements of accuracy of the MTA scale were calculated in relation to the INR classification. RESULTS: Of 99 patients (55.6% male with a mean age of 58.6 years), 57.6% presented with therapeutic INR values and 75.8% of the patients were adherent to the oral anticoagulant therapy according to the MTA. The accuracy analysis of the measurement provided by the MTA scale in relation to the INR classification showed a sensitivity of 77.2% and a specificity of 26.2%. The patients’ satisfaction with the treatment was high. The Duke Anticoagulation Satisfaction Scale had an average total score of 46.4, with the dimension impact in the field having the highest score (20.3). CONCLUSION: Stroke patients were adherent and satisfied with the oral anticoagulant therapy. The MTA had good sensitivity and poor specificity. Sociodemographic and clinical characteristics identified were not associated with adherence and satisfaction with treatment.

OBJECTIVES: to assess the construct validity and reliability of the Pediatric Patient Classification Instrument. METHODS: correlation study developed at a teaching hospital. The classification involved 227 patients, using the pediatric patient classification instrument. The construct validity was assessed through the factor analysis approach and reliability through internal consistency. RESULTS: the Exploratory Factor Analysis identified three constructs with 67.5% of variance explanation and, in the reliability assessment, the following Cronbach's alpha coefficients were found: 0.92 for the instrument as a whole; 0.88 for the Patient domain; 0.81 for the Family domain; 0.44 for the Therapeutic procedures domain. CONCLUSIONS: the instrument evidenced its construct validity and reliability, and these analyses indicate the feasibility of the instrument. The validation of the Pediatric Patient Classification Instrument still represents a challenge, due to its relevance for a closer look at pediatric nursing care and management. Further research should be considered to explore its dimensionality and content validity.