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AbstractObjectiveTo determine whether electronic health record alerts for acute kidney injury would improve patient outcomes of mortality, dialysis, and progression of acute kidney injury.DesignDouble blinded, multicenter, parallel, randomized controlled trial.SettingSix hospitals (four teaching and two non-teaching) in the Yale New Haven Health System in Connecticut and Rhode Island, US, ranging from small community hospitals to large tertiary care centers.Participants6030 adult inpatients with acute kidney injury, as defined by the Kidney Disease: Improving Global Outcomes (KDIGO) creatinine criteria.InterventionsAn electronic health record based “pop-up” alert for acute kidney injury with an associated acute kidney injury order set upon provider opening of the patient’s medical record.Main outcome measuresA composite of progression of acute kidney injury, receipt of dialysis, or death within 14 days of randomization. Prespecified secondary outcomes included outcomes at each hospital and frequency of various care practices for acute kidney injury.Results6030 patients were randomized over 22 months. The primary outcome occurred in 653 (21.3%) of 3059 patients with an alert and in 622 (20.9%) of 2971 patients receiving usual care (relative risk 1.02, 95% confidence interval 0.93 to 1.13, P=0.67). Analysis by each hospital showed worse outcomes in the two non-teaching hospitals (n=765, 13%), where alerts were associated with a higher risk of the primary outcome (relative risk 1.49, 95% confidence interval 1.12 to 1.98, P=0.006). More deaths occurred at these centers (15.6% in the alert group v 8.6% in the usual care group, P=0.003). Certain acute kidney injury care practices were increased in the alert group but did not appear to mediate these outcomes.ConclusionsAlerts did not reduce the risk of our primary outcome among patients in hospital with acute kidney injury. The heterogeneity of effect across clinical centers should lead to a re-evaluation of existing alerting systems for acute kidney injury.Trial registrationClinicalTrials.gov NCT02753751.

Acute kidney injury (AKI) is an adverse event that carries significant morbidity. Given that interventions after AKI occurrence have poor performance, there is substantial interest in prediction of AKI prior to its diagnosis. However, integration of real-time prognostic modeling into the electronic health record (EHR) has been challenging, as complex models increase the risk of error and complicate deployment. Our goal in this study was to create an implementable predictive model to accurately predict AKI in hospitalized patients and could be easily integrated within an existing EHR system. We performed a retrospective analysis looking at data of 169,859 hospitalized adults admitted to one of three study hospitals in the United States (in New Haven and Bridgeport, Connecticut) from December 2012 to February 2016. Demographics, medical comorbidities, hospital procedures, medications, and laboratory data were used to develop a model to predict AKI within 24 hours of a given observation. Outcomes of AKI severity, requirement for renal replacement therapy, and mortality were also measured and predicted. Models were trained using discrete-time logistic regression in a subset of Hospital 1, internally validated in the remainder of Hospital 1, and externally validated in Hospital 2 and Hospital 3. Model performance was assessed via the area under the receiver-operator characteristic (ROC) curve (AUC). The training set cohort contained 60,701 patients, and the internal validation set contained 30,599 patients. External validation data sets contained 43,534 and 35,025 patients. Patients in the overall cohort were generally older (median age ranging from 61 to 68 across hospitals); 44%-49% were male, 16%-20% were black, and 23%-29% were admitted to surgical wards. In the training set and external validation set, 19.1% and 18.9% of patients, respectively, developed AKI. The full model, including all covariates, had good ability to predict imminent AKI for the validation set, sustained AKI, dialysis, and death with AUCs of 0.74 (95% CI 0.73-0.74), 0.77 (95% CI 0.76-0.78), 0.79 (95% CI 0.73-0.85), and 0.69 (95% CI 0.67-0.72), respectively. A simple model using only readily available, time-updated laboratory values had very similar predictive performance to the complete model. The main limitation of this study is that it is observational in nature; thus, we are unable to conclude a causal relationship between covariates and AKI and do not provide an optimal treatment strategy for those predicted to develop AKI. In this study, we observed that a simple model using readily available laboratory data could be developed to predict imminent AKI with good discrimination. This model may lend itself well to integration into the EHR without sacrificing the performance seen in more complex models.

IntroductionAcute kidney injury (AKI) is common among hospitalised patients and under-recognised by providers and yet carries a significant risk of morbidity and mortality. Electronic alerts for AKI have become more common despite a lack of strong evidence of their benefits. We designed a multicentre, randomised, controlled trial to evaluate the effectiveness of AKI alerts. Our aim is to highlight several challenges faced in the design of this trial, which uses electronic screening, enrolment, randomisation, intervention and data collection.Methods and analysisThe design and implementation of an electronic alert system for AKI was a reiterative process involving several challenges and limitations set by the confines of the electronic medical record system. The trial will electronically identify and randomise 6030 adults with AKI at six hospitals over a 1.5–2 year period to usual care versus an electronic alert containing an AKI-specific order set. Our primary outcome will be a composite of AKI progression, inpatient dialysis and inpatient death within 14 days of randomisation. During a 1-month pilot in the medical intensive care unit of Yale New Haven Hospital, we have demonstrated feasibility of automating enrolment and data collection. Feedback from providers exposed to the alerts was used to continually improve alert clarity, user friendliness and alert specificity through refined inclusion and exclusion criteria.Ethics and disseminationThis study has been approved by the appropriate ethics committees for each of our study sites. Our study qualified for a waiver of informed consent as it presents no more than minimal risk and cannot be feasibly conducted in the absence of a waiver. We are committed to open dissemination of our data through clinicaltrials.gov and submission of results to the NIH data sharing repository. Results of our trial will be submitted for publication in a peer-reviewed journal.Trial registration number NCT02753751; Pre-results.

Objetivou-se com o presente trabalho avaliar o crescimento inicial de eucalipto consorciado em diferentes arranjos espaciais de feijão-caupi, no estado do Tocantins. O experimento foi conduzido na Fazenda Experimental da Universidade Federal do Tocantins ? UFT Campus Universitário de Gurupi - TO. Foram utilizadas mudas do clone VM-01 (Eucalyptus urophylla x Eucalyptus camaldulensis) e a implantação foi em janeiro de 2013, utilizando o espaçamento de 6 x 1,5 m. O feijoeiro foi instalado no espaçamento 20 x 50 cm, 15 dias após o plantio do eucalipto. O consórcio foi realizado com 08, 06, 04 e 0 linhas de feijão-caupi variedade fradinho. Foram realizadas avaliações de altura (cm) e diâmetro (mm) do eucalipto aos 60, 90, 120 e 150 dias após o plantio do eucalipto. O delineamento experimental utilizado foi em blocos ao acaso com nove repetições. Os dados coletados foram submetidos à análise de variância e, quando significativos pelo teste F, foi aplicada a análise de regressão com auxílio do programa SigmaPlot 10.0. Observou-se que a maior altura e o maior diâmetro foram obtidos com o consórcio do eucalipto com 8 linhas de feijão-caupi. Pode-se concluir que o consórcio foi benéfico para a cultura do eucalipto, que teve maior crescimento inicial em altura e diâmetro de caule, com efeito linear para as épocas de avaliação.

O pinhão manso (Jatropha curcas L.) é uma planta que tem sementes com alto teor de óleo e se destaca na produção de biodiesel. Como ainda está em processo de melhoramento, uma solução para o problema da desuniformidade genética é a reprodução assexuada. Esse método facilita o trabalho do melhorista, pois, uma vez identificada uma planta considerada superior, ela pode ser propagada, mantendo a sua característica genética. Desta forma, objetivou-se com este trabalho verificar a época de coleta, tipos de estaca e substratos, na propagação do pinhão manso. O delineamento experimental utilizado foi inteiramente casualizado em esquema fatorial 3 x 3 (substrato x estaca) totalizando 9 tratamentos com 3 repetições, sendo que cada unidade experimental foi composta por 10 estacas. Para efeito de comparação das épocas foi realizada análise conjunta. Foram utilizadas como estacas: ponteiro, parte mediana e basal, com aproximadamente 20 cm de comprimento. O estaqueamento foi feito em jardineiras plásticas pretas, contendo como substratos: composto orgânico comercial (Bioplant®), vermiculita média expandida e areia grossa lavada. As características avaliadas foram: porcentagem de estacas vivas e enraizadas, número de brotos e número de folhas por estaca, massa fresa e massa seca da parte aérea e das raízes. Os resultados permitiram concluir que a melhor época para a propagação do pinhão manso é agosto; e o uso de estacas da parte basal e dos substratos: vermiculita e composto orgânico comercial (Bioplant®) foram eficientes para produção de mudas de pinhão manso.

Cada espécie tem exigências próprias para o seu desenvolvimento. Luz, água, temperatura são alguns dos elementos abióticos que influenciam no desenvolvimento das plantas. A luz é importante no desenvolvimento vegetativo por influir, entre outros processos, na taxa de fotossíntese; a intensidade, qualidade, duração e periodicidade da luz agem tanto quantitativa como qualitativamente no incremento da planta. Objetivou-se testar diferentes níveis de sombreamento no desenvolvimento de mudas de jatobá (Hymenaea courbaril var. Stilbocarpa). O experimento foi realizado em área aberta sendo as mudas produzidas em casa de vegetação do tipo Pad & Fan e transplantadas para sacos pretos de 5 litros aos 20 dias após a semeadura usando-se mistura de solo e composto orgânico (1:1) como substrato. O delineamento experimental foi inteiramente casualizado com quatro tratamentos e 13 repetições cada, uma planta por parcela. Os tratamentos foram: T1 = Pleno sol; T2 = Tela de sombreamento 30%; T3 = Tela de sombreamento 50%; T4 = Tela de manipulação de espectro de luz ChromatiNet® vermelho 30%. As telas foram fixadas em telados de madeira de dimensões 1x1x1 m (altura x largura x comprimento). As características analisadas foram: altura das plantas, diâmetro médio do caule das plantas, teor de clorofila das folhas, relação altura de planta e diâmetro de caule, área foliar e massa fresca e seca de raiz e parte aérea. As melhores mudas de jatobá (Hymenaea courbaril var. Stilbocarpa) foram produzidas em sombreamento de 30 e 50% em 84 dias após o transplante.

Plasmids specifying bacteriocin production and immunity to its action were found in three clinical isolates of Staphylococcus aureus obtained in different hospitals located in Rio de Janeiro. These plasmids (pRJ28, pRJ29 and pRJ30) of 8.0 kb were found to generate identical restriction fragment patterns upon digestion with several enzymes, although the range of strains susceptible to the respective bacteriocin varied among the producer strains, when different Gram-positive bacteria were used as indicators, pRJ29 was then chosen for further characterization in order to compare it with pRJ6 and pRJ9, two small bacteriocin-encoding plasmids previously described in strains isolated from food. pRJ29 was found to code for a bacteriocin with chemical properties (sensitivity to proteases, heat resistance, activity under anaerobiosis, and estimated molecular weight) similar to those of pRJ6-encoded bacteriocin, conferring cross-immunity to it. However, its restriction map differed from those of pRJ6 and pRJ9. These studies together with hybridization, incompatibility, and mobilization analyses using a derivative of pRJ29 tagged with Tn917-lac suggest that pRJ29 is a mosaic composed of genetic determinants found on pRJ6 and pRJ9, and that IS257 was not involved in the recombination events which gave rise to pRJ29.

Objetivo: Descrever a implementação de atividades de promoção da saúde e prevenção de agravos para portadores de doenças crônicas não-transmissíveis, além de estimar a associação entre hábitos de saúde e de vida e os sexos. Método: Relato de experiência e estudo quantitativo transversal, realizado com portadores de doenças crônicas não-transmissíveis. Os dados sociodemográficos, de estado de saúde e hábitos de vida foram submetidos à análise estatística por meio dos testes T-student e Qui-quadrado. Resultados: Houve diferença estatisticamente significativa entre os valores de pressão arterial sistólica segundo o sexo. Nas atividades, os participantes foram participativos, apresentando avaliações positivas. Conclusão: As ações educativas e interventivas contribuem para a troca de saberes científicos e populares. As ações em saúde devem ser integrais e direcionadas, também, ao público masculino.

We introduce PaLM 2, a new state-of-the-art language model that has better multilingual and reasoning capabilities and is more compute-efficient than its predecessor PaLM. PaLM 2 is a Transformer-based model trained using a mixture of objectives. Through extensive evaluations on English and multilingual language, and reasoning tasks, we demonstrate that PaLM 2 has significantly improved quality on downstream tasks across different model sizes, while simultaneously exhibiting faster and more efficient inference compared to PaLM. This improved efficiency enables broader deployment while also allowing the model to respond faster, for a more natural pace of interaction. PaLM 2 demonstrates robust reasoning capabilities exemplified by large improvements over PaLM on BIG-Bench and other reasoning tasks. PaLM 2 exhibits stable performance on a suite of responsible AI evaluations, and enables inference-time control over toxicity without additional overhead or impact on other capabilities. Overall, PaLM 2 achieves state-of-the-art performance across a diverse set of tasks and capabilities. When discussing the PaLM 2 family, it is important to distinguish between pre-trained models (of various sizes), fine-tuned variants of these models, and the user-facing products that use these models. In particular, user-facing products typically include additional pre- and post-processing steps. Additionally, the underlying models may evolve over time. Therefore, one should not expect the performance of user-facing products to exactly match the results reported in this report.

Large language models have been shown to achieve remarkable performance across a variety of natural language tasks using few-shot learning, which drastically reduces the number of task-specific training examples needed to adapt the model to a particular application. To further our understanding of the impact of scale on few-shot learning, we trained a 540-billion parameter, densely activated, Transformer language model, which we call Pathways Language Model PaLM. We trained PaLM on 6144 TPU v4 chips using Pathways, a new ML system which enables highly efficient training across multiple TPU Pods. We demonstrate continued benefits of scaling by achieving state-of-the-art few-shot learning results on hundreds of language understanding and generation benchmarks. On a number of these tasks, PaLM 540B achieves breakthrough performance, outperforming the finetuned state-of-the-art on a suite of multi-step reasoning tasks, and outperforming average human performance on the recently released BIG-bench benchmark. A significant number of BIG-bench tasks showed discontinuous improvements from model scale, meaning that performance steeply increased as we scaled to our largest model. PaLM also has strong capabilities in multilingual tasks and source code generation, which we demonstrate on a wide array of benchmarks. We additionally provide a comprehensive analysis on bias and toxicity, and study the extent of training data memorization with respect to model scale. Finally, we discuss the ethical considerations related to large language models and discuss potential mitigation strategies.