Pflegeinterventionen bei Menschen mit Epidermolysis bullosa : zu den Schwerpunkten Schmerzmanagement, psychosoziale Aspekte und Wundmanagement im häuslichen Setting

Hintergrund: Epidermolysis bullosa (EB) ist ein Überbegriff von unheilbaren Hautkrankheiten, verursacht durch einen Gendefekt. In der fachlichen Beratung, Betreuung und Begleitung von Menschen mit EB besteht ein grosses Wissensdefizit, was zu einer unzureichend spezifisch pflegerischen Unterstützung von Betroffenen führt. Fragestellung: Die Fragestellung mit dem vorgängigen Ziel konkrete Bedürfnisse von Menschen mit EB zu erfassen, lautet: «Mit welchen Pflegeinterventionen können Pflegefachpersonen Patientinnen und Patienten im Alter ab 12 Jahren mit Epidermolysis bullosa zu Hause oder in Ambulatorien unterstützen?» Methode: Eine systematische Literaturrecherche wurde in drei verschiedenen medizinischen, pflegerischen Datenbanken durchgeführt. Die Literatur wurde zusammengefasst, gewürdigt und kritisch diskutiert. Zudem erfolgte ein Interview mit einer Fachexpertin, um die praxisrelevante Thematik zu untermauern. Ergebnisse: Drei Guidelines, eine qualitative und eine quantitative Studie sowie eine Expertenmeinung wurden zur Beantwortung der Fragestellung miteinbezogen. Es konnten Empfehlungen zu pflegerischen Interventionen mit den Schwerpunkten Schmerzmanagement, psychosoziale Aspekte und Wundmanagement erstellt werden. Schlussfolgerung: Fachkräfte, welche in der Pflege, Betreuung und Beratung von Menschen mit EB involviert sind, sollten eine gewisse Fachexpertise zum Thema mitbringen oder Informationsmöglichkeiten kennen. Die Bedürfnisse und Expertise von Betroffenen sollten in die Pflege miteinbezogen werden. Zudem ist ein multidisziplinäres Team mit einem interdisziplinären Ansatz empfehlenswert

Toxic plants-Detection of colchicine in a fast systematic clinical toxicology screening using liquid chromatography-mass spectrometry

Colchicum autumnale, which can be mistaken for Allium ursinum, contains the alkaloid colchicine potentially leading to life-threatening up to fatal intoxications. We report two cases of acute intoxications with unexplained circumstances. Using the authors' systematic screening approaches, colchicine could be detected in blood plasma and urine samples using liquid chromatography coupled to linear ion trap mass spectrometry (LC-ITMSn ) and high-resolution tandem mass spectrometry (LC-HRMS/MS). Metabolites of colchicine could be identified in urine for confirmation of screening results. Gas chromatography–mass spectrometry (GC-MS) analysis was also conducted, but colchicine could not be detected. Furthermore, colchicine concentration was estimated via LC-HRMS/MS in plasma samples. Results of the systematic screening indicated the ingestion of colchicine from both subjects. In both cases, the parent compound was detected in blood plasma and urine using the LC-HRMS/MS and LC-ITMSn system. An O-demethylation metabolite was identified in urine samples of both subjects using LC-HRMS/MS; the N-deacetylation product was also found in urine samples of both cases via LC-HRMS/MS and LC-ITMSn . The use of LC-ITMSn resulted only in the detection of the O-demethylation product in case 2. Plasma concentrations were estimated at 2.5 ng/ml and 4.7 ng/ml for cases 1 and 2, respectively. We demonstrated the detection of this highly toxic alkaloid in blood plasma and urine using a time-saving and reliable clinical systematic screening. Furthermore, we identified metabolites of colchicine being rarely discussed in literature, which can be used as additional screening targets

Contribuição de observações de sensoriamento remoto para a validação de modelos hidrológicos

Modelos hidrológicos são ferramentas essenciais para a gestão dos recursos hídricos, principalmente por servirem para a extrapolação ou interpolação do comportamento de processos hidrológicos no tempo e no espaço. O processo de calibração da modelagem hidrológica é iterativo, de forma que parâmetros são modificados para que o hidrograma simulado reproduza o hidrograma observado. No entanto, esta pode ser uma abordagem deficiente, já que diversas combinações de parâmetros podem resultar em um hidrograma satisfatório, mesmo que os processos hidrológicos intermediários não estejam sendo bem representados (e.g., evapotranspiração, interceptação, escoamento). O monitoramento in-situ desses processos hidrológicos apresenta limitações, principalmente em grandes bacias hidrográficas. Com o crescente desenvolvimento do sensoriamento remoto (SR), diversas variáveis hidrológicas vêm sendo remotamente monitoradas com larga cobertura espacial. Assim, o presente estudo tem como objetivo avaliar a contribuição de produtos de sensoriamento remoto de variáveis hidrológicas na representação dos processos intermediários em modelos hidrológicos, considerando que uma abordagem que contemple mais variáveis do que apenas a vazão traria maior confiabilidade para o modelo, principalmente para avaliação de cenários de alterações do sistema simulado, como o contexto de mudanças climáticas e de uso e ocupação do solo. O modelo utilizado foi o MGB-IPH, em um estudo de caso para a bacia hidrográfica do rio Purus, na Amazônia. A calibração foi realizada automaticamente (algoritmo multi-objetivo MOCOM-UA) a partir de observações de vazão. O modelo foi validado com as variáveis hidrológicas (e respectivas missões de SR) de altimetria espacial (Jason-2), armazenamento de água terrestre (GRACE), umidade do solo (SMOS), evapotranspiração (MODIS) e áreas inundadas (ALOS-PALSAR). O coeficiente de Kling-Gupta (KGE) foi utilizado para a avaliação da concordância entre as séries temporais simuladas e observadas. Resultados indicam que a calibração automática com vazão melhora as estimativas da própria vazão (KGE > 0.8), mas também de altimetria (KGE > 0.9) e de áreas inundadas (KGE > 0.7), mas não impacta significativamente as simulações de armazenamento de água terrestre, umidade do solo e evapotranspiração. Observações de sensoriamento remoto de umidade do solo e de evapotranspiração foram insuficientes para uma validação adequada do modelo hidrológico, ao passo que a própria variável de evapotranspiração, bem como as variáveis de armazenamento de água terrestre e de áreas inundadas foram relevantes a ponto de levantarem inconsistências e limitações do modelo, que não foram perceptíveis na análise da vazão apenas. São resultados promissores, pois apontam incertezas nas observações de sensoriamento remoto destas variáveis ou na representação de processos hidrológicos no modelo. Estudos futuros visam a ampliar o número de áreas de estudo, bem como avaliar o impacto da calibração do modelo a partir de observações de outras variáveis hidrológicas (além da vazão), a fim de que os processos hidrológicos sejam bem representados e de que o modelo resulte em simulações "certas pelos motivos certos".Hydrological models are important tools in water resources management, especially for extrapolating or interpolating the behavior of hydrological processes in time and space. The calibration, in hydrological modeling, is an iterative process, in which the parameters are modified with the view to the simulated hydrograph to reproduce the observed one. Nonetheless, this may be an incomplete approach, since there are many combinations of parameters that can result in a satisfactory hydrograph, even though the intermediary hydrological processes could be poorly represented (e.g., evapotranspiration, interception, flow). However, there are limitations to in-situ monitoring of hydrological processes, especially in large basins. With the rising development of remote sensing, many hydrological variables have been remotely monitored with large special coverage. Therefore, this study aims to evaluate the contribution of remote sensing products of hydrological variables to the representation of intermediary processes in hydrological models, considering that an approach that contemplates more variables than just the discharge would make the model more reliable, especially concerning scenarios of changes in the simulated system, such as in the context of climate change and land use change. The model used was MGB-IPH, in a study case for the Purus river watershed, in the Amazon basin. The calibration was set to an automatic mode (multi-objective algorithm MOCOM-UA), based on discharge observations. The model was validated with hydrological variables (and respective remote sensing missions) of spatial altimetry (Jason-2), terrestrial water storage (GRACE), soil moisture (SMOS), evapotranspiration (MODIS) and inundated areas (ALOS-PALSAR). The Kling-Gupta efficiency coefficient (KGE) was used to evaluate the agreement of the observed and the simulated time series. Results indicate that the automatic calibration with discharge improve the estimates of discharge itself (KGE > 0.8), altimetry (KGE > 0.9), and inundated areas (KGE > 0.7), but it does not significantly impact on simulations of terrestrial water storage, soil moisture and evapotranspiration. Remote sensing observations of soil moisture and evapotranspiration were insufficient to properly validate the hydrological model, even though evapotranspiration, as well as terrestrial water storage and inundated areas were relevant variables to be analyzed, since they were able to unmask inconsistencies and limitations in the model that were not observed when analyzing discharge time series only. These are promising results, because they either point out uncertainties in remote sensing observations of these variables, or in the representation of hydrological processes by the model. Future studies aim to evaluate the impact of model calibration with observations of other hydrological variables (besides discharge), in order for the hydrological processes to be well represented and in order for the model to generate "right results for the right reasons"

Contribuições de estimativas de sensoriamento remoto para a modelagem de múltiplas variáveis hidrológicas

Modelos hidrológicos são ferramentas importantes para diversas aplicações: sistemas de previsão, gestão de recursos hídricos, avaliação de impactos de mudanças climáticas, entre outras. Em geral, a calibração dos parâmetros de modelos é realizada com observações de vazão. No entanto, conjuntos de parâmetros resultantes da calibração com apenas uma variável (e.g., vazão) podem comprometer a correta representação de outras variáveis do ciclo hidrológico, em função de compensações nos parâmetros, de forma que o modelo estaria “acertando pelos motivos errados”. Uma alternativa é a calibração com múltiplas variáveis estimadas por sensoriamento remoto (SR). Estudos anteriores demonstraram o potencial desta técnica para melhorar estimativas de vazão, mas não analisaram em muita profundidade as outras variáveis do ciclo hidrológico. Neste estudo, um modelo de base física (MGB) é calibrado através do algoritmo de otimização multi-objetivo (MOCOM-UA), com vazão, mas também com estimativas de sensoriamento remoto de níveis (Jason-2), áreas inundadas (ALOS-PALSAR), anomalias no armazenamento de água (TWS, GRACE), evapotranspiração (ET, MOD16) e umidade do solo (SMOS). O método é testado para uma área de estudo no rio Purus, na Amazônia, e então replicado de forma simplificada para outras 3 áreas de estudo no Brasil, representativas de diferentes regimes hidro-climáticos. Resultados indicam que em alguns casos a calibração com variáveis de SR melhorou estimativas de vazão, e que evapotranspiração foi a variável que ofereceu mais contribuições às estimativas de vazão. Para a estimativa de outras variáveis do ciclo hidrológico, em alguns casos a calibração com uma variável melhorou a estimativa de outras, mas nem sempre, indicando que observações apresentam incertezas, ou que a estruturação ou parametrização do modelo está incorreta. Dentre as quatro regiões de estudo, o modelo mais consistente (i.e., calibração com uma variável resulta em melhora das outras) é o do rio Piquiri, no bioma Mata Atlântica, seguido pela bacia do bioma amazônico (Purus), Cerrado (Araguaia) e Caatinga (Pardo). Em geral, o modelo convergiu para diferentes conjuntos de parâmetros, dependendo da variável de calibração. Isto salienta que, a depender da variável de calibração, o modelo “acerta por motivos diferentes”. A abordagem de utilizar diferentes variáveis estimadas por SR se mostrou útil para fortalecer uma modelagem mais realística de variáveis hidrológicas (além de vazão).Hydrological models are important tools for many applications: forecasting systems, water resources management, climate change impact evaluation, among others. Usually, calibration of parameters in hydrological models is performed with streamflow observations. However, resulting parameter sets based on only one variable (e.g., streamflow) might compromise the correct representation of other variables in the water cycle, because of compensations between parameters, in a way that the model might be “getting the right answers for the wrong reasons”. One alternative is to calibrate the model parameters with multiple remote sensing (RS)-derived variables. Previous studies demonstrated its potential to improve discharge estimates, but few studies analyzed deeply the impacts on other variables in the water cycle. In this study, a physically based model (MGB) is calibrated with the multi-objective algorithm MOCOM-UA with streamflow, but also with RS estimates of water level (Jason-2), flood extent (ALOS-PALSAR), total water storage anomalies (TWS, GRACE), evapotranspiration (ET, MOD16), and soil moisture (SMOS). The method is tested for a study area in Purus river basin, in Amazon, and then replicated in a simpler framework to other 3 study areas in Brazil, in differing representative hydro-climatic regimes. Results indicate that in some cases, RS-based calibration improved streamflow estimates, and the variable that contributed the most to streamflow estimates was ET. For the other water cycle variables, in some cases calibration with a variable improved estimate of other variables, but not always, which indicates that observations have uncertainties, or that the model structure or parametrization might me incorrect. Within the four study regions, the most consistent model (i.e., calibration with a variable improved other variables) was in the “Mata Atlântica” biome (Piquiri), followed by Amazonian Purus, “Cerrado” (Araguaia), and semi-arid basin Pardo. In general, the model converged to different parameter sets, depending on the calibration variable. This highlights that, depending on the calibration variable, the model might “get the right results for differing reasons”. The approach of using multiple RS-variables proved to be useful towards a more realistic modelling of hydrological variables (besides streamflow)

Assessing Adherence to Antihypertensive Medication by Means of Dose-Dependent Reference Plasma Concentration Ranges and Ultra-High Performance Liquid Chromatography-Ion Trap Mass Spectrometry Analysis

Poor adherence to antihypertensive drug therapy is a well-recognized problem and can be assessed by mass spectrometry-based analyses of body fluids. However, contrary statements exist whether drug quantification in blood or qualitative screening in urine is more suitable. The present pilot study aimed to further elucidate the power of blood plasma drug concentrations for adherence monitoring by developing and validating a quantification procedure for nine antihypertensive drugs (amlodipine, bisoprolol, candesartan, canrenone, carvedilol, metoprolol, olmesartan, torasemide, and valsartan) in blood plasma using liquid–liquid extraction and an ultra-high-performance liquid chromatography-ion trap mass spectrometry analysis. The procedure should then be used for an adherence assessment and compared with the results of an established qualitative urine screening. Selectivity, carryover, matrix effect, accuracy, precision, dilution integrity, and stability were successfully validated, except for amlodipine. The applicability was demonstrated by analyzing 19 plasma samples containing 28 antihypertensive drugs and comparing the measured concentrations with calculated dose-dependent reference plasma concentration ranges. The interpretation of plasma concentrations was found to be more sophisticated and time-consuming than that of urine screening results, and adherence could not be assessed in two cases (10%) due to measured plasma concentrations below the lower limit of quantification. However, 14 out of 19 subjects were classified as adherent (75%) and three as nonadherent (15%), in contrast to 19 (100%) that were claimed to be adherent based on the results of the qualitative urine screening. Nevertheless, further data is needed to estimate whether plasma quantification is superior in terms of assessing adherence to antihypertensive medication

Assessment of the value of remotely sensed surface water extent data for the calibration of a lumped hydrological model

For many catchments, there is insufficient field data to calibrate the hydrological models that are needed to answer water resources management questions. One way to overcome this lack of data is to use remotely sensed data. In this study, we assess whether Landsat‐based surface water extent observations can inform the calibration of a lumped bucket‐type model for Brazilian catchments. We first performed synthetic experiments with daily, monthly, and limited monthly data (April–October), assuming a perfect monotonic relation between streamflow and stream width. The median relative performance was 0.35 for daily data and 0.17 for monthly data, where values above 0 imply an improvement in model performance compared to the lower benchmark. This indicates that the limited temporal resolution of remotely sensed data is not an impediment for model calibration. In a second step, we used real remotely sensed water extent data for calibration. For only 76 of the 671 sites the remotely sensed water extent was large and variable enough to be used for model calibration. For 30% of these sites, calibration with the actual remotely sensed water extent data led to a model fit that was better than the lower benchmark (i.e., relative performance >0). Model performance increased with river width and variation therein. This indicates that the coarse spatial resolution of the freely‐available, long time series of water extent used in this study hampered model calibration. We, therefore, expect that newer higher‐resolution imagery will be helpful for model calibration for more sites, especially when time series length increases

MONTAGEM CÊNICA

Oficina de Montagem Teatral com aulas de expressão corporal, expressão vocal e criação dramatúrgica, visando realizar apresentações dentro e fora da Instituição e a criação de um grupo permanente de Teatro no Campus Ibirama