Acute cholangitis due to afferent loop syndrome after a Whipple procedure: a case report

This is an Open Access article distributed under the terms of the Creative Commons Attribution Licens

Wound dehiscence: is still a problem in the 21th century: a retrospective study

<p>Abstract</p> <p>Background</p> <p>The aim of this study was to evaluate the risk factors of wound dehiscence and determine which of them can be reverted.</p> <p>Methods</p> <p>We retrospectively analyzed 3500 laparotomies. Age over 75 years, diagnosis of cancer, chronic obstructive pulmonary disease, malnutrition, sepsis, obesity, anemia, diabetes, use of steroids, tobacco use and previous administration of chemotherapy or radiotherapy were identified as risk factors</p> <p>Results</p> <p>Fifteen of these patients developed wound dehiscence. Emergency laparotomy was performed in 9 of these patients. Patients who had more than 7 risk factors died.</p> <p>Conclusion</p> <p>It is important for the surgeon to know that wound healing demands oxygen consumption, normoglycemia and absence of toxic or septic factors, which reduces collagen synthesis and oxidative killing mechanisms of neutrophils. Also the type of abdominal closure may plays an important role. The tension free closure is recommended and a continuous closure is preferable. Preoperative assessment so as to identify and remove, if possible, these risk factors is essential, in order to minimize the incidence of wound dehiscence, which has a high death rate.</p

Forecasting: theory and practice

Forecasting has always been in the forefront of decision making and planning. The uncertainty that surrounds the future is both exciting and challenging, with individuals and organisations seeking to minimise risks and maximise utilities. The lack of a free-lunch theorem implies the need for a diverse set of forecasting methods to tackle an array of applications. This unique article provides a non-systematic review of the theory and the practice of forecasting. We offer a wide range of theoretical, state-of-the-art models, methods, principles, and approaches to prepare, produce, organise, and evaluate forecasts. We then demonstrate how such theoretical concepts are applied in a variety of real-life contexts, including operations, economics, finance, energy, environment, and social good. We do not claim that this review is an exhaustive list of methods and applications. The list was compiled based on the expertise and interests of the authors. However, we wish that our encyclopedic presentation will offer a point of reference for the rich work that has been undertaken over the last decades, with some key insights for the future of the forecasting theory and practice

Forecasting: theory and practice

peer reviewedForecasting has always been at the forefront of decision making and planning. The uncertainty that surrounds the future is both exciting and challenging, with individuals and organisations seeking to minimise risks and maximise utilities. The large number of forecasting applications calls for a diverse set of forecasting methods to tackle real-life challenges. This article provides a non-systematic review of the theory and the practice of forecasting. We provide an overview of a wide range of theoretical, state-of-the-art models, methods, principles, and approaches to prepare, produce, organise, and evaluate forecasts. We then demonstrate how such theoretical concepts are applied in a variety of real-life contexts. We do not claim that this review is an exhaustive list of methods and applications. However, we wish that our encyclopedic presentation will offer a point of reference for the rich work that has been undertaken over the last decades, with some key insights for the future of forecasting theory and practice. Given its encyclopedic nature, the intended mode of reading is non-linear. We offer cross-references to allow the readers to navigate through the various topics. We complement the theoretical concepts and applications covered by large lists of free or open-source software implementations and publicly-available databases

Εφαρμογή και αξιολόγηση μεθόδων τεχνητής νοημοσύνης σε προβλήματα ιατρικής διάγνωσης

Summarization: Magnetic Resonance Imaging (MRI) is a widely used medical imaging modality that provides accurate information about the human tissue, anatomy and pathology, of a non-invasive form. If used to scan a human brain it can provide images of high contrast, therefore distinguishing between the three major brain tissues: Cerebro-Spinal Fluid (CSF), Grey Matter (GM) and White Matter (WM). In this way MRI can greatly assist radiologists and doctors in providing a more precise diagnosis and therapy. Because of their unpredictable appearance and shape, segmenting brain tumors from multi-modal imaging data is one of the most challenging tasks in medical image analysis. Manual detection and classification of brain tumor by an expert is still considered the most acceptable method, but it is too time-consuming, especially because of the large amount of data that have to be analysed manually. In this thesis we examine, optimize and finally combine specific state-of-the-art methods comprising of four consistent methods for Computer-Aided Diagnosis (CAD) processes for detection of a brain tumor from MRI, of T2 weighted modality, from the axial plane (T2 MRI). We denote the four proposed methodologies as “Method 1” to “Method 4”. These methodologies are based on image pre-processing and classification by utilizing neural networks (NN) or a hybrid combination of neural networks and fuzzy logic (ANFIS). In order to gauge the current innovation status in automated brain tumor segmentation and to compare various proposed methods in bibliography, we use a large dataset of brain tumor MR scans, in which the relevant tumor structures have been delineated. These are provided freely from Multimodal Brain Tumor Image Segmentation (BRATS) MICCAI 2015. Our dataset of the training and testing data set, referred to male and female adult persons, includes 24 non-tumorous cases and 202 tumorous cases that all have been segmented visually by our experienced neurosurgeon partner, Dr. A. Krasoudakis. The healthy MRI scans are from “St. George” general hospital of Chania, Crete and from Harvard General Hospital database. Our data set contains about 5% high-grade, 82% low-grade glioma cases, 3% unhealthy but not recognizable cases and 10% healthy cases. At the pre-processing stage we apply a skull-stripping algorithm to isolate the brain region. Subsequently, we use a high-pass Gaussian filter for sharpening and a median filter for noise reduction. In post-processing stage, for image segmentation we use Otsu’s threshold as well as we implement morphological operators for region of interest (ROI) definition. In our proposed CAD Method 1, feature extraction is made using Grey-level co-occurrence matrix (GLCM) and 13 statistical features are calculated. In Method 2, feature extraction is made by using Discrete Wavelet Transform (DWT) and dimensionality reduction is implemented using Principal Components Analysis (PCA). In Method 3, all the above methods are combined and GLCM matrix is applied after the DWT and PCA stages, so to provide the necessary statistical features. In Method 4, the Mean-Shift algorithm is implemented at the post-processing stage for better segmentation results and features extraction is made according to Method 3. The features extracted from every proposed CAD method are processed at first with a feed-forward artificial neural network (ANN) with back-propagation training algorithm and then with an adaptive neuro-fuzzy inference system (ANFIS) for the methods with GLCM matrix. The experimental results of the proposed methods have been validated and evaluated for performance over a testing set of images based on sensitivity, specificity and accuracy with the best results reaching 98.8% sensitivity, 62.5% specificity and 95.6% accuracy

Environmental Assessment of Alternative Strategies for the Management of Construction and Demolition Waste: A Life Cycle Approach

The management of solid waste is currently seen as one of the most important concerns that national authorities, particularly in south Europe, must address. In recent years, emphasis has begun to be paid to Construction and Demolition Waste (CDW) being the largest waste stream in the European Union that is produced by renovation and repair work on buildings, roads, bridges, and other constructions made of bulky materials such as asphalt, bricks, wood, and plastic. Many EU countries responded quickly as a result of the large amounts of such waste and the presence of hazardous substances in their composition. This study illustrates the anticipated outcomes of several CDW management strategies other than final disposal, such as recycling, reuse, and incineration, for a public-school building in Greece. In order to assess how well the chosen schemes performed in terms of various environmental criteria, the SimaPro software suite and the Ecoinvent v.3 Life Cycle Inventory database were used. In order to enhance the quality of the outcomes, inventory data from earlier studies were also employed as input data for the Life Cycle Assessment tool