Predicting death and morbidity in perforated peptic ulcer

Peptic ulcers are defined as defects in the gastrointestinal mucosa that extend through the muscularis mucosae. Although not being the most common complication, perforations stand out as being the complication with the highest mortality rate. To predict the probability of mortality, several scoring systems based on clinical and biochemical parameters, such as the Boey and PULP scoring system have been developed. This article explores, using data mining in the medical data available, how the scoring systems perform when trying to predict mortality and patients’ state complication. We also try to conclude, from the two scoring systems presented, which predicts better the situations described. Regarding the results, we concluded that the PULP scoring allows a better mortality prediction achieving, in this case, above 90% accuracy, however, the results may be inconclusive due to the lack of patients who died in the dataset used. Regarding the complications, we concluded that, on the other hand, the Boey system achieves better results leading to a better prediction when it comes to predicting patients’ state complication.FCT - Fundação para a Ciência e a Tecnologia (UID/CEC/00319/2013

Pervasive gaps in Amazonian ecological research

Biodiversity loss is one of the main challenges of our time,1,2 and attempts to address it require a clear un derstanding of how ecological communities respond to environmental change across time and space.3,4 While the increasing availability of global databases on ecological communities has advanced our knowledge of biodiversity sensitivity to environmental changes,5–7 vast areas of the tropics remain understudied.8–11 In the American tropics, Amazonia stands out as the world’s most diverse rainforest and the primary source of Neotropical biodiversity,12 but it remains among the least known forests in America and is often underrepre sented in biodiversity databases.13–15 To worsen this situation, human-induced modifications16,17 may elim inate pieces of the Amazon’s biodiversity puzzle before we can use them to understand how ecological com munities are responding. To increase generalization and applicability of biodiversity knowledge,18,19 it is thus crucial to reduce biases in ecological research, particularly in regions projected to face the most pronounced environmental changes. We integrate ecological community metadata of 7,694 sampling sites for multiple or ganism groups in a machine learning model framework to map the research probability across the Brazilian Amazonia, while identifying the region’s vulnerability to environmental change. 15%–18% of the most ne glected areas in ecological research are expected to experience severe climate or land use changes by 2050. This means that unless we take immediate action, we will not be able to establish their current status, much less monitor how it is changing and what is being lostinfo:eu-repo/semantics/publishedVersio

Pervasive gaps in Amazonian ecological research

Biodiversity loss is one of the main challenges of our time,1,2 and attempts to address it require a clear understanding of how ecological communities respond to environmental change across time and space.3,4 While the increasing availability of global databases on ecological communities has advanced our knowledge of biodiversity sensitivity to environmental changes,5,6,7 vast areas of the tropics remain understudied.8,9,10,11 In the American tropics, Amazonia stands out as the world's most diverse rainforest and the primary source of Neotropical biodiversity,12 but it remains among the least known forests in America and is often underrepresented in biodiversity databases.13,14,15 To worsen this situation, human-induced modifications16,17 may eliminate pieces of the Amazon's biodiversity puzzle before we can use them to understand how ecological communities are responding. To increase generalization and applicability of biodiversity knowledge,18,19 it is thus crucial to reduce biases in ecological research, particularly in regions projected to face the most pronounced environmental changes. We integrate ecological community metadata of 7,694 sampling sites for multiple organism groups in a machine learning model framework to map the research probability across the Brazilian Amazonia, while identifying the region's vulnerability to environmental change. 15%–18% of the most neglected areas in ecological research are expected to experience severe climate or land use changes by 2050. This means that unless we take immediate action, we will not be able to establish their current status, much less monitor how it is changing and what is being lost

Rationale, study design, and analysis plan of the Alveolar Recruitment for ARDS Trial (ART): Study protocol for a randomized controlled trial

Background: Acute respiratory distress syndrome (ARDS) is associated with high in-hospital mortality. Alveolar recruitment followed by ventilation at optimal titrated PEEP may reduce ventilator-induced lung injury and improve oxygenation in patients with ARDS, but the effects on mortality and other clinical outcomes remain unknown. This article reports the rationale, study design, and analysis plan of the Alveolar Recruitment for ARDS Trial (ART). Methods/Design: ART is a pragmatic, multicenter, randomized (concealed), controlled trial, which aims to determine if maximum stepwise alveolar recruitment associated with PEEP titration is able to increase 28-day survival in patients with ARDS compared to conventional treatment (ARDSNet strategy). We will enroll adult patients with ARDS of less than 72 h duration. The intervention group will receive an alveolar recruitment maneuver, with stepwise increases of PEEP achieving 45 cmH(2)O and peak pressure of 60 cmH2O, followed by ventilation with optimal PEEP titrated according to the static compliance of the respiratory system. In the control group, mechanical ventilation will follow a conventional protocol (ARDSNet). In both groups, we will use controlled volume mode with low tidal volumes (4 to 6 mL/kg of predicted body weight) and targeting plateau pressure <= 30 cmH2O. The primary outcome is 28-day survival, and the secondary outcomes are: length of ICU stay; length of hospital stay; pneumothorax requiring chest tube during first 7 days; barotrauma during first 7 days; mechanical ventilation-free days from days 1 to 28; ICU, in-hospital, and 6-month survival. ART is an event-guided trial planned to last until 520 events (deaths within 28 days) are observed. These events allow detection of a hazard ratio of 0.75, with 90% power and two-tailed type I error of 5%. All analysis will follow the intention-to-treat principle. Discussion: If the ART strategy with maximum recruitment and PEEP titration improves 28-day survival, this will represent a notable advance to the care of ARDS patients. Conversely, if the ART strategy is similar or inferior to the current evidence-based strategy (ARDSNet), this should also change current practice as many institutions routinely employ recruitment maneuvers and set PEEP levels according to some titration method.Hospital do Coracao (HCor) as part of the Program 'Hospitais de Excelencia a Servico do SUS (PROADI-SUS)'Brazilian Ministry of Healt

Normalização espacial e análise de imagens cerebrais de ressonância magnética – uma abordagem com deep learning

Dissertação de mestrado integrado em Engenharia Biomédica (especialização em Informática Médica)Throughout the years, Deep Learning has proven to be an excellent technology to solve problems that would otherwise be too complex. Furthermore, it has shown great success in the area of medical imaging, especially when applied to segmentation of brain tissues. As such, this dissertation explores a possible new approach, using Deep Artificial Neural Networks to perform spatial normalization on brain MRI studies as well as classify using Brain MRI studies regarding their state of brain atrophy. Spatial normalization of Magnetic Resonance images by tools like the FSL, or SPM turned out to be inefficient for researches as they need too many resources to achieve good results. These resources include, for example, wasted human and computer time when executing the commands to normalize and waiting for the process to finish, this can take up to several hours just for one study. Therefore, a new approach was needed, a faster and easier way to normalize the MRI studies. To do so, Deep Artificial Neural Networks were used by creating a python program to deal with said studies in much less time. This program should free the researchers’ time for other more relevant tasks and help reach conclusions faster in their studies when trying to find patterns between the analysed brains. Several architectures were tried, having better results with U-Net based architecture as well as GAN architecture. At the end, the model couldn’t learn correctly all the brain features to be changed in any of the approaches but showed great potential. Even though the final model did achieve the correct shape it could not yet achieve the final normalization. With some more time invested in perfecting the models, these could, in the future, learn to correctly perform the final normalization and allow the researchers to perform it in less than 10 seconds per exam instead of hours. Regarding the Brain Atrophy models, the models showed some potential too as the predictions were partially correct. With more data, and less unbalanced, the model could probably learn correctly and output the expected results for all classes.Ao longo dos anos, abordagens Deep Learning têm provado ser uma excelente tecnologia para resolver problemas que seriam complexos demais. Além disso, demonstrou grande sucesso na área da imagem médica, principalmente quando aplicada em segmentação de imagens. Como tal, esta dissertação explora uma possível nova abordagem, usando as Redes Neurais Artificiais Profundas para realizar a normalização espacial em estudos de RM do cérebro, bem como classificá las usando estudos cerebrais de RM em relação ao seu estado de atrofia cerebral. A normalização espacial dos estudos de ressonância magnética através de ferramentas como a biblioteca FSL acabou sendo pouco eficiente para uso na investigação, pois estas ferramentas precisam de muitos recursos para obter bons resultados. Esses recursos incluem, por exemplo, desperdício de tempo humano e de computador ao executar os comandos para normalizar e aguardar a conclusão do processo; o que pode demorar várias horas, apenas para um estudo. Portanto, uma nova abordagem é necessária, uma maneira mais rápida e fácil de normalizar os estudos de RM. Para isso, foram utilizadas Redes Neurais Artificiais Profundas, criando um programa em python para lidar com os estudos em muito menos tempo. Esse programa deve liberar o tempo dos investigadores para outras tarefas mais exigentes e ajudar a chegar a conclusões mais rapidamente nos seus estudos, ao tentar encontrar padrões entre os cérebros analisados. Várias arquiteturas para o modelo foram testadas, obtendo melhores resultados com a arquitetura baseada em U-Net e com a arquitetura GAN. No final, o modelo não conseguiu aprender corretamente todos os detalhes do cérebro a serem alterados em nenhuma das abordagens, mas mostrou grande potencial. Apesar de o modelo final ter atingido a forma correta, ainda não conseguiu a normalização final. Com mais tempo investido no aperfeiçoamento dos modelos, estes poderiam, no futuro, aprender a executar corretamente a normalização final e permitir que os pesquisadores realizassem este processo em menos de 10 segundos por exame, em vez de horas. Em relação aos modelos de atrofia cerebral, estes também mostraram algum potencial, pois as previsões estavam parcialmente corretas. Com mais dados e menos desequilíbrio nos mesmos, o modelo provavelmente poderia aprender corretamente e gerar os resultados esperados para todas as classes

Trends in the use of 3D printing with medical imaging

A major problem affecting the health systems of almost all countries today is the need to strike a balance between providing effective health services and controlling expenditure to ensure the overall sustainability of the system. The use of 3D printing technology has proved incredibly useful in several areas over the last few years, including the medical sector in a wide range of tasks. The main objective of this review is to provide a starting point for encouraging the development of new tools based on 3D printing using medical imaging to help professionals and researchers balance efficiency and effectiveness in health management and achieve sustainable healthcare. Therefore, this paper presents a typical workflow for using 3D printing based on medical imaging volumetric data to help both researchers and professionals use this technology to treat their patients more cost-effectively and effectively. Healthcare providers and health education institutions equipped with 3D printing are an innovation that will support progress towards a smarter and more sustainable healthcare system.This work is supported by the National Council for Scientific and Technological Development (CNPq), Brazil. This work has been supported by FCT e Fundação para a Ciência e Tecnologia within the R&D Units Project Scope: UIDB/00319/202

Contactless human-computer interaction using a Deep Neural Network Pipeline for real-time video interpretation and classification

Nowadays, all applications are developed with the user’s comfort in mind. Regardless of the application’s objective, it should be as simple as possible so that it is easily accepted by its users. With the evolution of technology, simplicity has evolved and has become intrinsically related to the automation of tasks. Therefore, many researchers have focused their investigations on the interaction between humans and computing devices. However, this interaction is usually still carried out via a keyboard and/or a mouse. We present an essemble of deep neural networks for the detection and interpretation of gestural movement, in various environments. Its purpose is to introduce a new form of interaction between the human and computing devices in order to evolve this paradigm. The use case focused on detecting the movement of the user’s hands in real time and automatically interpreting the movement.This research has been supported by FCT - Fundação para a Ciência e Tecnologia whithin the R&D Units Project Scope: UIDB/00319/2020

Characterisation of microbial attack on archaeological bone

As part of an EU funded project to investigate the factors influencing bone preservation in the archaeological record, more than 250 bones from 41 archaeological sites in five countries spanning four climatic regions were studied for diagenetic alteration. Sites were selected to cover a range of environmental conditions and archaeological contexts. Microscopic and physical (mercury intrusion porosimetry) analyses of these bones revealed that the majority (68%) had suffered microbial attack. Furthermore, significant differences were found between animal and human bone in both the state of preservation and the type of microbial attack present. These differences in preservation might result from differences in early taphonomy of the bones. © 2003 Elsevier Science Ltd. All rights reserved