Telangiectasia Mucosa e Periungual como Indícios de Doença Sistémica

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Measurement of healing area using planimetry after applying low-intensity ultrasound to the skin of rats

CONTEXTUALIZAÇÃO: A planimetria é um método utilizado para avaliar a evolução da cicatrização de feridas. A planimetria computacional é um método ainda em experimentação, mas cujas vantagens têm sido demonstradas em várias investigações. OBJETIVOS: Avaliar os efeitos do ultra-som pulsado de baixa intensidade sobre a cicatrização de lesão cutânea produzida na região dorsal de ratos, por meio da planimetria computacional. MATERIAIS E MÉTODOS: Utilizou-se 60 ratos machos Wistar (peso médio de 300g) divididos em dois grupos com 30 animais cada, de acordo com o tratamento: 1) irradiação simulada (controle); 2) irradiação efetiva (Freqüência fundamental de 1,5MHz, freqüência de repetição de pulsos de 1KHz, largura de pulso de 200µs, intensidade de 30mW/cm² SATA, dez minutos de aplicação em dias alternados). Cada grupo foi subdividido em três grupos, de acordo com o período de irradiação ultra-sônica, de três, sete e 14 dias, respectivamente, e a cicatrização foi avaliada por meio da planimetria, um decalque da lesão sendo obtido em papel especial, digitalizado e medido ao computador por meio de um programa gráfico. Análise estatística pelo método não-paramétrico de Mann-Whitney. RESULTADOS: Houve aumento significante (p<0,05) da área cicatrizada no grupo 2 (141,88±18,50mm²) em relação ao grupo 1 (117,38±15,14mm²), no 14º dia. Não houve diferenças significantes entre os grupos nos demais períodos. CONCLUSÕES: O ultra-som pulsado de baixa intensidade estimula a cicatrização cutânea por segunda intenção em condições experimentais. A planimetria computacional mostrou-se um recurso de baixo custo, fácil manuseio e de aplicabilidade clínica.BACKGROUND: Planimetry is a method used to evaluate the progression of skin wound healing. Computerized planimetry is still an experimental method, but its advantages have been demonstrated in several investigations. OBJECTIVE: To evaluate the effects of low-intensity pulsed ultrasound on the healing of a skin lesion produced on the dorsal region of rats, by means of computerized planimetry. METHODS: Sixty male Wistar rats of mean weight 300g were used. They were divided into two groups according to the treatment applied: 1) simulated irradiation (control); 2) effective irradiation (fundamental frequency 1.5MHz, pulse repetition frequency 1KHz, pulse width 200µs, SATA intensity 30mW/cm² and application for ten minutes on alternate days). Each group was divided into three subgroups according to the length of time for which ultrasound irradiation was applied of three, seven and 14 days, respectively, and healing was evaluated by means of planimetry; a tracing of the wound was obtained on special paper and this was digitized and measured by means of a graphing software. Statistical analysis was performed using the Mann-Whitney non-parametric method. RESULTS: The healed area was significantly greater (p<0.05) in group 2 (141.88±18.50mm²) than in group 1 (117.38±15.14mm²) on the 14th day. There were no significant differences between the subgroups for the other experimental periods. CONCLUSIONS: Low-intensity pulsed ultrasound irradiation stimulated secondary skin healing under these experimental conditions. Computerized planimetry was shown to be a low cost method that was easy to use and present clinical applicability

Event-based decision support algorithm for real-time flood forecasting in urban drainage systems using machine learning modelling

Urban flooding is a major problem for cities around the world, with significant socio-economic consequences. Conventional real-time flood forecasting models rely on continuous time-series data and often have limited accuracy, especially for longer lead times than 2 hrs. This study proposes a novel event-based decision support algorithm for real-time flood forecasting using event-based data identification, event-based dataset generation, and a real-time decision tree flowchart using machine learning models. The results of applying the framework to a real-world case study demonstrate higher accuracy in forecasting water level rise, especially for longer lead times (e.g., 2–3 hrs), compared to traditional models. The proposed framework reduces root mean square error by 50%, increases accuracy of flood forecasting by 50%, and improves normalised Nash–Sutcliffe error by 20%. The proposed event-based dataset framework can significantly enhance the accuracy of flood forecasting, reducing the occurrences of both false alarms and flood missing and improving emergency response systems

A critical review of digital technology innovations for early warning of water-related disease outbreaks associated with climatic hazards

Water-related climatic disasters pose a significant threat to human health due to the potential of disease outbreaks, which are exacerbated by climate change. Therefore, it is crucial to predict their occurrence with sufficient lead time to allow for contingency plans to reduce risks to the population. Opportunities to address this challenge can be found in the rapid evolution of digital technologies. This study conducted a critical analysis of recent publications investigating advanced technologies and digital innovations for forecasting, alerting, and responding to water-related extreme events, particularly flooding, which is often linked to disaster-related disease outbreaks. The results indicate that certain digital innovations, such as portable and local sensors integrated with web-based platforms are new era for predicting events, developing control strategies and establishing early warning systems. Other technologies, such as augmented reality, virtual reality, and social media, can be more effective for monitoring flood spread, disseminating before/during the event information, and issuing warnings or directing emergency responses. The study also identified that the collection and translation of reliable data into information can be a major challenge for effective early warning systems and the adoption of digital innovations in disaster management. Augmented reality, and digital twin technologies should be further explored as valuable tools for better providing of communicating complex information on disaster development and response strategies to a wider range of audiences, particularly non-experts. This can help to increase community engagement in designing and operating effective early warning systems that can reduce the health impact of climatic disasters

Enhancing community resilience in arid regions: A smart framework for flash flood risk assessment

This paper presents a novel framework for smart integrated risk management in arid regions. The framework combines flash flood modelling, statistical methods, artificial intelligence (AI), geographic evaluations, risk analysis, and decision-making modules to enhance community resilience. Flash flood is simulated by using Watershed Modelling System (WMS). Statistical methods are also used to trim outlier data from physical systems and climatic data. Furthermore, three AI methods, including Support Vector Machine (SVM), Artificial Neural Network (ANN), and Nearest Neighbours Classification (NNC), are used to predict and classify flash flood occurrences. Geographic Information System (GIS) is also utilised to assess potential risks in vulnerable regions, together with Failure Mode and Effects Analysis (FMEA) and Hazard and Operability Study (HAZOP) methods. The decision-making module employs the Classic Delphi technique to classify the appropriate solutions for flood risk control. The methodology is demonstrated by its application to the real case study of the Khosf region in Iran, which suffers from both drought and severe floods simultaneously, exacerbated by recent climate changes. The results show high Coefficient of determination (R2) scores for the three AI methods, with SVM at 0.88, ANN at 0.79, and NNC at 0.89. FMEA results indicate that over 50% of scenarios are at high flood risk, while HAZOP indicates 30% of scenarios with the same risk rate. Additionally, peak flows of over 24 m3/s are considered flood occurrences that can cause financial damage in all scenarios and risk techniques of the case study. Finally, our research findings indicate a practical decision support system that is compatible with sustainable development concepts and can enhance community resilience in arid regions

Crédito Rural: Sustentabilidade E O Paradoxo Do Desenvolvimento Econômico Social Do Campo

Considering that rural credit has an important role in the modernization and operation of the field by the promotion of agricultural activities, enabling the countryside social and economic development, the objective by this article, is to demonstrate that the institute in frank evolution of an earlier priority for the operation and modernization of farming techniques and pastoral seeking a higher yield, has gone up over time to have a priority for the sustainability of the field by the creation of aid programs for family agriculture and its foundations.So even if the means are temporally disjointed, it is understood that the objectives of sustainability and social economic development of the field would possess the common goal of benefiting the farmers, however, certainly there would be a paradox between them, since the rural credit is taken as the principal by agricultural expansion in 70s, as well by the fact that Brazil is now a world power in agribusiness, which also encompass the environment degradation that has occurred since then. Considerando que o crédito rural possui um papel relevante na modernização e operacionalização do campo mediante o fomento das atividades agropecuárias, possibilitando o desenvolvimento econômico social da zona rural, objetiva-se pelo presente artigo, demonstrar que o instituto em franca evolução, de uma anterior primazia pela operacionalização e modernização das técnicas de exploração agrícola e pastoril, visando um maior rendimento, passa-se com o tempo a se ter uma primazia pela sustentabilidade do campo ante a criação dos programas de auxílio a agricultura familiar e seus fundamentos. Assim, mesmo que temporalmente desconexos, entende-se que os objetivos da sustentabilidade e do desenvolvimento econômico social do campo possuiriam o objetivo comum de beneficiar o produtor rural. Entretanto, tem-se que haveria um paradoxo entre ambos, já que o crédito rural é tomado como o principal responsável pela expansão agrícola na década de 70, bem como pelo fato do Brasil ser hoje uma potência mundial no agronegócio, o que abarcaria também a degradação do meio ambiente ocorrida desde então

Transcriptome analysis of the oil-rich seed of the bioenergy crop Jatropha curcas L

<p>Abstract</p> <p>Background</p> <p>To date, oil-rich plants are the main source of biodiesel products. Because concerns have been voiced about the impact of oil-crop cultivation on the price of food commodities, the interest in oil plants not used for food production and amenable to cultivation on non-agricultural land has soared. As a non-food, drought-resistant and oil-rich crop, <it>Jatropha curcas </it>L. fulfils many of the requirements for biofuel production.</p> <p>Results</p> <p>We have generated 13,249 expressed sequence tags (ESTs) from developing and germinating <it>Jatropha </it>seeds. This strategy allowed us to detect most known genes related to lipid synthesis and degradation. We have also identified ESTs coding for proteins that may be involved in the toxicity of <it>Jatropha </it>seeds. Another unexpected finding is the high number of ESTs containing transposable element-related sequences in the developing seed library (800) when contrasted with those found in the germinating seed library (80).</p> <p>Conclusions</p> <p>The sequences generated in this work represent a considerable increase in the number of sequences deposited in public databases. These results can be used to produce genetically improved varieties of <it>Jatropha </it>with increased oil yields, different oil compositions and better agronomic characteristics.</p

Enhancing urban flood forecasting in drainage systems using dynamic ensemble-based data mining

This is the final version. Available on open access from Elsevier via the DOI in this recordData availability: Data will be made available on request.This study presents a novel approach for urban flood forecasting in drainage systems using a dynamic ensemble-based data mining model which has yet to be utilised properly in this context. The proposed method incorporates an event identification technique and rainfall feature extraction to develop weak learner data mining models. These models are then stacked to create a time-series ensemble model using a decision tree algorithm and confusion matrix-based blending method. The proposed model was compared to other commonly used ensemble models in a real-world urban drainage system in the UK. The results show that the proposed model achieves a higher hit rate compared to other benchmark models, with a hit rate of around 85% vs 70 % for the next 3 h of forecasting. Additionally, the proposed smart model can accurately classify various timesteps of flood or non-flood events without significant lag times, resulting in fewer false alarms, reduced unnecessary risk management actions, and lower costs in real-time early warning applications. The findings also demonstrate that two features, “antecedent precipitation history” and “seasonal time occurrence of rainfall,” significantly enhance the accuracy of flood forecasting with a hit rate accuracy ranging from 60 % to 10 % for a lead time of 15 min to 3 h.Devon Resilience Innovation Project (DRIP