Miotomia laparoscopica secondo Heller per acalasia esofagea. C’è bisogno di una fundoplicatio?

The last decade has witnessed radical changes in the treatment of esophageal achalasia due to the development of minimally invasive techniques. Because of the high success rate of the laparoscopic Heller myotomy, a radical shift in the treatment algorithm of these patients has occurred, and today this is the preferred treatment modality for achalasia. This remarkable change is due to the recognition by gastroenterologists and patients that a laparoscopic Heller myotomy outperforms pneumatic dilatation and intra-sphincteric injection of botulinum toxin injection. While there is agreement about the technique of the myotomy per se, some questions still linger about the need for a fundoplication after the myotomy. The following review describes the data present in the literature in order to identify the best procedure that can achieve relief of dysphagia while avoiding development of gastroesophageal reflux

Riparazione laparoscopica delle ernie paraesofagee: un approccio “evidence-based”

The approach to paraesophageal hernias has drastically changed over the last decade. The goal of this paper is to describe in detail our surgical technique of laparoscopic repair of paraesophageal hernias and to provide an evidence-based approach to the most controversial aspects of this type of repair

Primary Esophageal Motility Disorders: Beyond Achalasia

The best-defined primary esophageal motor disorder is achalasia. However, symptoms such as dysphagia, regurgitation and chest pain can be caused by other esophageal motility disorders. The Chicago classification introduced new manometric parameters and better defined esophageal motility disorders. Motility disorders beyond achalasia with the current classification are: esophagogastric junction outflow obstruction, major disorders of peristalsis (distal esophageal spasm, hypercontractile esophagus, absent contractility) and minor disorders of peristalsis (ineffective esophageal motility, fragmented peristalsis). The aim of this study was to review the current diagnosis and management of esophageal motility disorders other than achalasia

Designing a Smart City Internet of Things Platform with Microservice Architecture

The Internet of Things (IoT) is being adopted in different application domains and is recognized as one of the key enablers of the Smart City vision. Despite the standard-ization efforts and wide adoption of Web standards and cloud computing technologies, however, building large-scale Smart City IoT platforms in practice remains challenging. The dynamically changing IoT environment requires these systems to be able to scale and evolve over time adopting new technologies and requirements. In response to the similar challenges in building large-scale distributed applications and platforms on the Web, microservice architecture style has emerged and gained a lot of popularity in the industry in recent years. In this work, we share our early experience of applying the microservice architecture style to design a Smart City IoT platform. Our experience suggests significant benefits provided by this architectural style compared to the more generic Service-Oriented Architecture (SOA) approaches, as well as highlights some of the challenges it introduces

Understanding the Chicago Classification: From Tracings to Patients

Current parameters of the Chicago classification include assessment of the esophageal body (contraction vigour and peristalsis), lower esophageal sphincter relaxation pressure, and intra-bolus pressure pattern. Esophageal disorders include achalasia, esophagogastric junction outflow obstruction, major disorders of peristalsis, and minor disorders of peristalsis. Sub-classification of achalasia in types I, II, and III seems to be useful to predict outcomes and choose the optimal treatment approach. The real clinical significance of other new parameters and disorders is still under investigation

Training Nonintrusive Load Monitoring Algorithms Without Supervision From Submeters

Non-intrusive load monitoring allows to estimate the energy consumption of major household appliances by just analyzing the aggregated power consumption collected at the main meter of the house. Recent disaggregation algorithms based on deep learning techniques showed superior performance with respect to previous methods. However, they require large amount of sub-meter data to be trained. In this work, we present a new solution for training non-intrusive load monitoring algorithms without any supervision from sub-meters. To achieve this goal, we divided the disaggregation algorithm into two stages named appliance detection and state-based disaggregation. In the first stage, we aim at identifying the start and stop times of the individual appliance operations within the whole-house power signal. In the second stage, we reconstruct the power signature of the target device by exploiting appliance-specific power states learned in the house. We tested our methodology on fridges, washing machines and dishwashers of a public dataset, showing double-digit improvements with respect to previous methods trained with sub-meter data. Most importantly, the proposed solution allows to collect a large number of appliance power signatures with minor costs, thus helping to achieve the generalization capabilities required by a real-world disaggregation system