Transcatheter Closure of Perimembranous Ventricular Septal Defects

n/

Echocardiographic Assessment after Surgical Repair of Tetralogy of Fallot

Surgical correction of Tetralogy of Fallot is still one of the most frequently performed intervention in pediatric cardiac surgery, and in many cases it is far from being a complete and definitive correction. It is rather an excellent palliation that solves the problem of cyanosis but predisposes the patients to medical and surgical complications during follow-up. The decision-making process regarding the treatment of late sequel are among the most discussed topics in adult congenital cardiology. In postoperative Fallot patients, echocardiography is used as the first method of diagnostic imaging and currently allows both a qualitative observation of the anatomical alterations and a detailed quantification of right ventricular volumes and function, of the right ventricular outflow tract, and of the pulmonary valve and pulmonary arteries. The literature introduced many quantitative echocardiographic criteria useful for the understanding of the pathophysiological mechanisms involving the right ventricle and those have made much more objective any decision-making processes

The climate in the European Union and the enlarged European Region is a determinant of the COVID-19 case fatality ratio

Climate could influence the COVID-19 pandemic, but while no evidence has been advanced on the influence of colder climates, some studies have provided data to support a possible heat-related protective factor. The objective is to verify whether areas with a Cold Temperate Climate (TC) have a higher Case Fatality Ratio (CFR) for COVID-19 than areas with a Cold Climate (CC) or with a Mediterranean Climate (MC) in the European Union and the Enlarged European Region. Countries or regions were subdivided into 3 groups according to the Köppen climate classification system: TC (Cfa, Cfb and Cfc in the Köppen system); MC (Csa, Csb); CC (D and E in the Köppen system). The total number of cases and the total number of deaths were detected on 13 August 2020 on the COVID-19 Map-Johns Hopkins Coronavirus Resource Center-the CFR was thus calculated by area. Living in TC areas is strongly associated with risk of a high Case Fatality Ratio for COVID-19, OR for MC =0.42, IC 95% 0.41-0.43; OR for CC=0.33, IC 95% 0.33-0.35. The results are confirmed in the EU, OR per MC=0.85, CI 95% 0.84-0.87; OR per CC=0.63, IC 95% 0.61-0.65.The study found that the IC in a humid temperate climate is associated with higher CFR with respect to the coldest and warmest temperate climates in Europe. This does not appear to be the only determinant of the pandemic

Data-Driven Methodology for Knowledge Graph Generation Within the Tourism Domain

The tourism and hospitality sectors have become increasingly important in the last few years and the companies operating in this field are constantly challenged with providing new innovative services. At the same time, (big-) data has become the “new oil” of this century and Knowledge Graphs are emerging as the most natural way to collect, refine, and structure this heterogeneous information. In this paper, we present a methodology for semi-automatic generating a Tourism Knowledge Graph (TKG), which can be used for supporting a variety of intelligent services in this space, and a new ontology for modelling this domain, the Tourism Analytics Ontology (TAO). Our approach processes and integrates data from Booking.com, Airbnb, DBpedia, and GeoNames. Due to its modular structure, it can be easily extended to include new data sources or to apply new enrichment and refinement functions. We report a comprehensive evaluation of the functional, logical, and structural dimensions of TKG and TAO

Enriching Data Lakes with Knowledge Graphs

Data lakes are repositories of data stored in natural/raw format. A data lake may include structured data from relational databases, semi-structured data (i.e., JSON, CSV), unstructured data (i.e., text data), or binary data (i.e., images, audio, video). It is usually built on top of cost-efficient infrastructures such as Hadoop, Amazon S3, MongoDB, ElasticSearch, etc. Several organisations rely on big data lakes for crucial tasks such as reporting, visualisation, advanced analytics, machine learning, and business intelligence. A major limitation of this solution is that without descriptive metadata and a mechanism to maintain it, such data tend to be noisy, making their management and analysis complex and time-consuming. Therefore, there is the need to add a semantic layer based on a formal ontology to describe the data and efficient mechanism to represent them as a knowledge graph. In this paper, we present a methodology to add a semantic layer to a data lake and thus obtain a knowledge graph that can support structured queries and advanced data exploration. We describe a practical implementation of a methodology applied to a data lake consisting of text data describing the online marketplace for lodging and tourism activities. We report statistics about the data lake and the resulting knowledge graph

Is the Inversion in the Trend of the Lethality of the COVID-19 in the Two Hemispheres due to the Difference in Seasons and Weather?

The climate has an influence on the COVID-19 virus lethality. The aim of this study is to verify if the summer weather coincided with the decrease of the Case Fatality Ratio (CFR) in Europe and if, on the contrary, an inverse trend was observed in Australia and New Zealand. To verify our hypothesis, we considered the largest European countries (Germany, UK, France, Italy, and Spain), plus Belgium and the Netherlands. Furthermore, we compared these countries with Australia and New Zealand. For each country considered, we have calculated the CFR from the beginning of the pandemic to May 6th and from May 6th to September 21st (late summer in Europe, late winter in the southern hemisphere). The CFRs were calculated from the John Hopkins University database. According to the results, in all European countries, a progressive decrease in CFR is observed. A diametrically opposite result is found in Australia where, on the contrary, the CFR is much higher at the end of September (at the end of winter) than on May 6th (mid-autumn), and the risk of dying if we count the infection is higher in September. In New Zealand, there are no statistically significant differences between the two surveys. The present study was based on public access macro data

Gastrointestinal Coronavirus disease 2019: epidemiology, clinical features, pathogenesis, prevention, and management

Introduction: The new Severe Acute Respiratory Syndrome Coronavirus 2 (SARS-CoV-2) is the etiologic agent of coronavirus disease 2019. Some authors reported evidences that patients with SARS-CoV-2 infection could have a direct involvement of the gastrointestinal tract, and in symptomatic cases, gastrointestinal symptoms (diarrhea, nausea/vomiting, abdominal pain) could be very common. Area covered: In this article, we reviewed current published data of the gastrointestinal aspects involved in SARS-CoV-2 infection, including prevalence and incidence of specific symptoms, presumptive biological mechanism of GI infection, prognosis, clinical management and public health related concerns on the possible risk of oral-fecal transmission. Expert opinion: Different clues point to a direct virus infection and replication in mucosal cells of the gastrointestinal tract. In vitro studies showed that SARS-CoV-2 could enters into the gastrointestinal epithelial cells by the Angiotensin-Converting enzyme 2 membrane receptor. These findings, coupled with identification of viral RNA found in stools of patients, clearly suggest that a direct involvement of gastrointestinal tract is very likely. This can justify most of the gastrointestinal symptoms but also suggest a risk for an oral fecal route for transmission, additionally or alternatively to the main respiratory route

Enhancing Scholarly Understanding: A Comparison of Knowledge Injection Strategies in Large Language Models

The use of transformer-based models like BERT for natural language processing has achieved remarkable performance across multiple domains. However, these models face challenges when dealing with very specialized domains, such as scientific literature. In this paper, we conduct a comprehensive analysis of knowledge injection strategies for transformers in the scientific domain, evaluating four distinct methods for injecting external knowledge into transformers. We assess these strategies in a single-label multi-class classification task involving scientific papers. For this, we develop a public benchmark based on 12k scientific papers from the AIDA knowledge graph, categorized into three fields. We utilize the Computer Science Ontology as our external knowledge source. Our findings indicate that most proposed knowledge injection techniques outperform the BERT baseline

Optimizing Tourism Accommodation Offers by Integrating Language Models and Knowledge Graph Technologies

Online platforms have become the primary means for travellers to search, compare, and book accommodations for their trips. Consequently, online platforms and revenue managers must acquire a comprehensive comprehension of these dynamics to formulate a competitive and appealing offerings. Recent advancements in natural language processing, specifically through the development of large language models, have demonstrated significant progress in capturing the intricate nuances of human language. On the other hand, knowledge graphs have emerged as potent instruments for representing and organizing structured information. Nevertheless, effectively integrating these two powerful technologies remains an ongoing challenge. This paper presents an innovative deep learning methodology that combines large language models with domain-specific knowledge graphs for classification of tourism offers. The main objective of our system is to assist revenue managers in the following two fundamental dimensions: (i) comprehending the market positioning of their accommodation offerings, taking into consideration factors such as accommodation price and availability, together with user reviews and demand, and (ii) optimizing presentations and characteristics of the offerings themselves, with the intention of improving their overall appeal. For this purpose, we developed a domain knowledge graph covering a variety of information about accommodations and implemented targeted feature engineering techniques to enhance the information representation within a large language model. To evaluate the effectiveness of our approach, we conducted a comparative analysis against alternative methods on four datasets about accommodation offers in London. The proposed solution obtained excellent results, significantly outperforming alternative methods

Leveraging Knowledge Graphs with Large Language Models for Classification Tasks in the Tourism Domain

Online platforms, serving as the primary conduit for travelers to seek, compare, and secure travel accommodations, require a profound understanding of user dynamics to craft competitive and enticing offerings. Concurrently, recent advancements in Natural Language Processing, particularly large language models, have made substantial strides in capturing the complexity of human language. Simultaneously, knowledge graphs have become a formidable instrument for structuring and categorizing information. This paper introduces a cutting-edge deep learning methodology integrating large language models with domain-specific knowledge graphs to classify tourism offers. It aims at aiding hospitality operators in understanding their accommodation offerings’ market positioning, taking into account the visit propensity and user review ratings, with the goal of optimizing the offers themselves and enhancing their appeal. Comparative analysis against alternative methods on two datasets of London accommodation offers attests to our approach’s effectiveness, demonstrating superior results