Bacterial inter-species communication mediated by the autoinducer-2 signal

Dissertation presented to obtain the Ph.D degree in Biology by Universidade Nova de Lisboa, Instituto de Tecnologia Química e Biológica, Instituto Gulbenkian de Ciência.During the last few decades, scientists have come to appreciate the immense complexity in bacterial signaling interactions that sustain microbial communities. Quorum-sensing (QS) is a cell-cell communication process whereby single cell bacteria regulate gene expression synchronously in a population in response to self-produced extracellular signal molecules, called autoinducers. Autoinducer-2 (AI-2), the synthase of which, LuxS, is present in both Gram-negative and Gram-positive bacteria, was proposed to represent a non-species-specific signal that mediates inter-species communication. In enteric bacteria, extracellular AI-2 levels peak in late exponential phase and rapidly decline as bacteria continue to grow. This depletion occurs because AI-2 activates the expression of an operon, lsr (for LuxS Regulated), encoding the Lsr transporter and enzymes that degrade the signal. As the Lsr system imports self and non-self AI-2, lsr-containing bacteria can interfere with AI-2 signaling of other species and shut off group behaviors regulated by this molecule: this system represents the first example of interference with a bacterial inter-species QS signal.(...)Fundação para a Ciência e Tecnologia financial support with the grant SFRH / BD / 28543 / 2006

Risk prediction analysis for post-surgical complications in cardiothoracic surgery

Cardiothoracic surgery patients have the risk of developing surgical site infections (SSIs), which causes hospital readmissions, increases healthcare costs and may lead to mortality. The first 30 days after hospital discharge are crucial for preventing these kind of infections. As an alternative to a hospital-based diagnosis, an automatic digital monitoring system can help with the early detection of SSIs by analyzing daily images of patient’s wounds. However, analyzing a wound automatically is one of the biggest challenges in medical image analysis. The proposed system is integrated into a research project called CardioFollowAI, which developed a digital telemonitoring service to follow-up the recovery of cardiothoracic surgery patients. This present work aims to tackle the problem of SSIs by predicting the existence of worrying alterations in wound images taken by patients, with the help of machine learning and deep learning algorithms. The developed system is divided into a segmentation model which detects the wound region area and categorizes the wound type, and a classification model which predicts the occurrence of alterations in the wounds. The dataset consists of 1337 images with chest wounds (WC), drainage wounds (WD) and leg wounds (WL) from 34 cardiothoracic surgery patients. For segmenting the images, an architecture with a Mobilenet encoder and an Unet decoder was used to obtain the regions of interest (ROI) and attribute the wound class. The following model was divided into three sub-classifiers for each wound type, in order to improve the model’s performance. Color and textural features were extracted from the wound’s ROIs to feed one of the three machine learning classifiers (random Forest, support vector machine and K-nearest neighbors), that predict the final output. The segmentation model achieved a final mean IoU of 89.9%, a dice coefficient of 94.6% and a mean average precision of 90.1%, showing good results. As for the algorithms that performed classification, the WL classifier exhibited the best results with a 87.6% recall and 52.6% precision, while WC classifier achieved a 71.4% recall and 36.0% precision. The WD had the worst performance with a 68.4% recall and 33.2% precision. The obtained results demonstrate the feasibility of this solution, which can be a start for preventing SSIs through image analysis with artificial intelligence.Os pacientes submetidos a uma cirurgia cardiotorácica tem o risco de desenvolver infeções no local da ferida cirúrgica, o que pode consequentemente levar a readmissões hospitalares, ao aumento dos custos na saúde e à mortalidade. Os primeiros 30 dias após a alta hospitalar são cruciais na prevenção destas infecções. Assim, como alternativa ao diagnóstico no hospital, a utilização diária de um sistema digital e automático de monotorização em imagens de feridas cirúrgicas pode ajudar na precoce deteção destas infeções. No entanto, a análise automática de feridas é um dos grandes desafios em análise de imagens médicas. O sistema proposto integra um projeto de investigação designado CardioFollow.AI, que desenvolveu um serviço digital de telemonitorização para realizar o follow-up da recuperação dos pacientes de cirurgia cardiotorácica. Neste trabalho, o problema da infeção de feridas cirúrgicas é abordado, através da deteção de alterações preocupantes na ferida com ajuda de algoritmos de aprendizagem automática. O sistema desenvolvido divide-se num modelo de segmentação, que deteta a região da ferida e a categoriza consoante o seu tipo, e num modelo de classificação que prevê a existência de alterações na ferida. O conjunto de dados consistiu em 1337 imagens de feridas do peito (WC), feridas dos tubos de drenagem (WD) e feridas da perna (WL), provenientes de 34 pacientes de cirurgia cardiotorácica. A segmentação de imagem foi realizada através da combinação de Mobilenet como codificador e Unet como decodificador, de forma a obter-se as regiões de interesse e atribuir a classe da ferida. O modelo seguinte foi dividido em três subclassificadores para cada tipo de ferida, de forma a melhorar a performance do modelo. Caraterísticas de cor e textura foram extraídas da região da ferida para serem introduzidas num dos modelos de aprendizagem automática de forma a prever a classificação final (Random Forest, Support Vector Machine and K-Nearest Neighbors). O modelo de segmentação demonstrou bons resultados ao obter um IoU médio final de 89.9%, um dice de 94.6% e uma média de precisão de 90.1%. Relativamente aos algoritmos que realizaram a classificação, o classificador WL exibiu os melhores resultados com 87.6% de recall e 62.6% de precisão, enquanto o classificador das WC conseguiu um recall de 71.4% e 36.0% de precisão. Por fim, o classificador das WD teve a pior performance com um recall de 68.4% e 33.2% de precisão. Os resultados obtidos demonstram a viabilidade desta solução, que constitui o início da prevenção de infeções em feridas cirúrgica a partir da análise de imagem, com recurso a inteligência artificial

Diving into the depth of primary motor cortex: a high-resolution investigation of the motor system using 7Tesla fMRI

Dissertação para a obtenção do Grau de Mestre em Engenharia BiomédicaHuman behaviour is grounded in our ability to perform complex tasks. While human motor function has been studied for over a century the cortical processes underlying motor behaviour are still under debate. Central to the execution of action is the primary motor cortex (M1), which has previously been considered to be responsible for the execution of movements planned in the premotor cortex, yet recent studies point to more complex roles for M1 in orchestrating motor-related information. The purpose of this project is to study the functional properties of primary motor cortex using ultra-high fMRI. The spatial resolution made possible by using a high field magnet allows us to investigate novel questions such as the existence of cortical columns, the functional organization pattern for single fingers and functional involvement of M1 in motor imagery and observation. Thirteen young healthy subjects participated in this study. Functional and anatomical high resolution images were acquired. Four functional scans were acquired for the different tasks: motor execution; motor imagery; movement observation and rest. The paradigm used was a randomized finger tapping. The images analysis was performed with the Brainvoyager QX program. Using the novel high resolution cortical grid sampling analysis tools, different cortical laminas of human M1 were examined. Our results reveal a distributed pattern (intermingled with somatotopic “hot spots”) for single fingers activity in M1. Furthermore we show novel evidence of columnar structures in M1 and show that non motor tasks such as motor imagery and action observation also activate this region. We conclude that the primary motor cortex has much more un-expected complex roles regarding the processing of movement related information, not only due to their involvement in tasks that do not imply muscle movement, but also due to their intriguing organization pattern