Splenic hamartoma associated with abdominal discomfort and pain. Case report

Hamartomas are benign splenic neoplasms asymptomatic in most of the cases. Symptoms, when present, may either be related to the growth of the mass with abdominal discomfort and pain or be related to a hypersplenism syndrome. Certain preoperative diagnosis cannot be made with current diagnostic imaging. Splenectomy is therefore indicated in order to obtain histological diagnosis, rule out malignancy or achieve regression of symptoms. We report the case of a 39-year-old woman referred for a splenic hamartoma causing pain located on the upper abdominal quadrant. She underwent splenectomy through a left subcostal access followed by complete resolution of symptoms. Resection of splenic masses is indicated to complete diagnosis, achieve cure and, when present, relieve symptoms

Severe intestinal bleeding due to left-sided portal hypertension after pancreatoduodenectomy with portal resection and splenic vein ligation

Pancreatoduodenectomy (PD) with portal vein (PV)/superior mesenteric vein (SMV) resection is well accepted for pancreatic head cancer because of the improvement in margin-negative resection and survival rates, without increasing postoperative morbidity and mortality in high volume centers. There is controversy in the surgical literature regarding the safety of splenic vein (SV) ligation during a PD with PV-SMV resection. Simple SV ligation has been associated with the development of left-sided portal hypertension, gastrointestinal bleeding and hypersplenism over the long term. We report a rare case of severe intestinal bleeding due to left-sided portal hypertension in patient who underwent a PD with PV-SMV confluence segmental resection and splenic ligation, preserving left gastric vein and inferior mesenteric vein, for cephalic pancreatic adenocarcinomas, seven months previously

Severe bleeding from esophageal varices resistant to endoscopic treatment in a non cirrhotic patient with portal hypertension

A non cirrhotic patient with esophageal varices and portal vein thrombosis had recurrent variceal bleeding unsuccessfully controlled by endoscopy and esophageal transection. Emergency transhepatic portography confirmed the thrombosed right branch of the portal vein, while the left branch appeared angulated, shifted and stenotic. A stent was successfully implanted into the left branch and the collateral vessels along the epatoduodenal ligament disappeared. In patients with esophageal variceal hemorrhage and portal thrombosis if endoscopy fails, emergency esophageal transection or nonselective portocaval shunting are indicated. The rare patients with only partial portal thrombosis can be treated directly with stenting through an angioradiologic approach

Clinical effects of laparotomy with perioperative continuous peritoneal lavage and postoperative hemofiltration in patients with severe acute pancreatitis

<p>Abstract</p> <p>Background</p> <p>The elevated serum and peritoneal cytokine concentrations responsible for the systemic response syndrome (SIRS) and multiorgan failure in patients with severe acute pancreatitis lead to high morbidity and mortality rates. Prompted by reports underlining the importance of reducing circulating inflammatory mediators in severe acute pancreatitis, we designed this study to evaluate the efficiency of laparotomy followed by continuous perioperative peritoneal lavage combined with postoperative continuous venovenous diahemofiltration (CVVDH) in managing critically ill patients refractory to intensive care therapy. As the major clinical outcome variables we measured morbidity, mortality and changes in the Acute Physiology and Chronic Health Evaluation (APACHE II) score and cytokine concentrations in serum and peritoneal lavage fluid over time.</p> <p>Methods</p> <p>From a consecutive group of 23 patients hospitalized for acute pancreatitis, we studied 6 patients all with Apache II scores ≥19, who underwent emergency surgery for acute complications (5 for an abdominal compartment syndrome and 1 for septic shock) followed by continuous perioperative peritoneal lavage and postoperative CVVDH. CVVDH was started within 12 hours after surgery and maintained for at least 72 hours, until the multiorgan dysfunction syndrome improved. Samples were collected from serum, peritoneal lavage fluid and CVVDH dialysate for cytokine assay. Apache II scores were measured daily and their association with cytokine levels was assessed.</p> <p>Results</p> <p>All six patients tolerated CVVDH well, and the procedure lasted a mean 6 days (range, 3-12). Five patients survived and one died of Acinetobacter infection after surgery (mortality rate 16.6%). The mean APACHE II score was ≥ 19 (range 19-22) before laparotomy and decreased significantly during peritoneal lavage and postoperative CVVDH (P = 0.013 by matched-pairs Students <it>t</it>-test). The decrease in cytokine concentrations in serum and lavage fluid was associated with the decrease in APACHE II scores and high interleukin 6 (IL-6) and tumor necrosis factor (TNF) concentrations in the hemofiltrate.</p> <p>Conclusion</p> <p>In critically ill patients with abdominal compartment syndrome, septic shock or high APACHE II scores related to severe acute pancreatitis, combining emergency laparotomy with continuous perioperative peritoneal lavage followed by postoperative CVVHD effectively reduces the local and systemic cytokines responsible for multiorgan dysfunction syndrome thus improving patients' outcome.</p

Rischio da stress lavoro-correlato nel personale infermieristico

Vengono riportati i risultati dell'applicazione di un questionario sullo stress lavoro-correlato in un gruppo di infermier

Artificial intelligence and healthcare: Forecasting of medical bookings through multi-source time-series fusion

Nowadays, Artificial intelligence (AI), combined with the digitalization of healthcare, can lead to substantial improvements in Patient Care, Disease Management, Hospital Administration, and supply chain effectiveness. Among predictive analytics tools, time series forecasting represents a central task to support healthcare management in terms of bookings and medical services predictions. In this context, the development of flexible frameworks to provide robust and reliable predictions became a central point in this healthcare innovation process. This paper presents and discusses a multi-source time series fusion and forecasting framework relying on Deep Learning. By combining weather, air-quality and medical bookings time series through a feature compression stage which preserves temporal patterns, the prediction is provided through a flexible ensemble technique based on machine learning models and a hybrid neural network. The proposed system is able to predict the number of bookings related to a specific medical examination for a 7-days horizon period. To assess the proposed approach's effectiveness, we rely on time series extracted from a real dataset of administrative e-health records provided by the Campania Region health department, in Italy

Predictive Analytics for Smart Parking: A Deep Learning Approach in Forecasting of IoT Data

Nowadays, a sustainable and smart city focuses on energy efficiency and the reduction of polluting emissions through smart mobility projects and initiatives to "sensitize"infrastructure. Smart parking is one of the building blocks of intelligent mobility, innovative mobility that aims to be flexible, integrated, and sustainable and consequently integrated into a Smart City. By using the Internet of Things (IoT) sensors located in the parking areas or the underground car parks in combination with a mobile application, which indicates to citizens the free places in the different areas of the city and guides them toward the chosen parking, it is possible to reduce air pollution and fluidifying noise traffic. In this article, we present and discuss an innovative Deep Learning-based ensemble technique in forecasting the parking space occupancy to reduce the search time for parking and to optimize the flow of cars in particularly congested areas, with an overall positive impact on traffic in urban centres. A genetic algorithm has also been used to optimize predictors parameters. The main goal is to design an intelligent IoT-based service that can predict, in the next few hours, the parking spaces occupancy of a street. The proposed approach has been assessed on a real IoT dataset composed by over than 15M of collected sensor records. Obtained results demonstrate that our method outperforms both single predictors and the widely used strategy of the mean providing inherently robust predictions

Machine Learning insights for behavioural data analysis supporting the Autonomous Vehicles scenario

The advent of the digital innovation era is changing service, use, and resources management paradigms, offering a wide range of new and essential opportunities. In particular, the advent of the Internet of Things (IoT), i.e. the ability to connect individual objects to the Internet, also capable of communicating autonomously, has its particular declination on the connected vehicle. It is combined with the potential of advanced sensors placed pervasively on vehicles, which offer multi-functional monitoring capabilities of the entire system: from individual components up to the whole vehicle, including driver behaviour and conditions and many exogenous parameters to the vehicle (road and weather conditions, congestion, risk situations, changes to mobility plans, etc.). In this perspective, Machine Learning (ML) models can transform raw data into new knowledge; they can contribute in an innovative way to define and suggest decisions, strategies, and criteria for resource use. Nowadays, most intelligent mobility projects also integrate artificial intelligence (AI) and ML solutions. In this paper, we present and discuss the application of unsupervised learning techniques on a Vehicular IoT dataset. The main goal is to generate new knowledge about a geographical zone by analyzing historical drivers behavioural data. The autonomous vehicle’s framework can exploit the generated valuable insights to optimize the routes and prevent critical issues

Neural networks generative models for time series

Nowadays, time series are a widely-exploited methodology to describe phenomena belonging to different fields. In fact, electrical consumption can be explained, from a data analysis perspective, with a time series, as for healthcare, financial index, air pollution or parking occupancy rate. Applying time series to different areas of interest has contributed to the exponential rise in interest by both practitioners and academics. On the other side, especially regarding static data, a new trend is acquiring even more relevance in the data analysis community, namely neural network generative approaches. Generative approaches aim to generate new, fake samples given a dataset of real data by implicitly learning the probability distribution underlining data. In this way, several tasks can be addressed, such as data augmentation, class imbalance, anomaly detection or privacy. However, even if this topic is relatively well-established in the literature related to static data regarding time series, the debate is still open. This paper contributes to this debate by comparing four neural network-based generative approaches for time series belonging to the state-of-the-art methodologies in literature. The comparison has been carried out on five public and private datasets and on different time granularities, with a total number of 13 experimental scenario. Our work aims to provide a wide overview of the performances of the compared methodologies when working in different conditions like seasonality, strong autoregressive components and long or short sequences

Machine Learning insights for behavioural data analysis supporting the Autonomous Vehicles scenario

The advent of the digital innovation era is changing service, use, and resources management paradigms, offering a wide range of new and essential opportunities. In particular, the advent of the Internet of Things (IoT), i.e. the ability to connect individual objects to the Internet, also capable of communicating autonomously, has its particular declination on the connected vehicle. It is combined with the potential of advanced sensors placed pervasively on vehicles, which offer multi-functional monitoring capabilities of the entire system: from individual components up to the whole vehicle, including driver behaviour and conditions and many exogenous parameters to the vehicle (road and weather conditions, congestion, risk situations, changes to mobility plans, etc.). In this perspective, Machine Learning (ML) models can transform raw data into new knowledge; they can contribute in an innovative way to define and suggest decisions, strategies, and criteria for resource use. Nowadays, most intelligent mobility projects also integrate artificial intelligence (AI) and ML solutions. In this paper, we present and discuss the application of unsupervised learning techniques on a Vehicular IoT dataset. The main goal is to generate new knowledge about a geographical zone by analyzing historical drivers behavioural data. The autonomous vehicle’s framework can exploit the generated valuable insights to optimize the routes and prevent critical issues. IEE