Characteristics and Outcomes of Patients With Cerebral Venous Sinus Thrombosis in SARS-CoV-2 Vaccine–Induced Immune Thrombotic Thrombocytopenia

Importance: Thrombosis with thrombocytopenia syndrome (TTS) has been reported after vaccination with the SARS-CoV-2 vaccines ChAdOx1 nCov-19 (Oxford-AstraZeneca) and Ad26.COV2.S (Janssen/Johnson & Johnson). Objective: To describe the clinical characteristics and outcome of patients with cerebral venous sinus thrombosis (CVST) after SARS-CoV-2 vaccination with and without TTS. Design, setting, and participants: This cohort study used data from an international registry of consecutive patients with CVST within 28 days of SARS-CoV-2 vaccination included between March 29 and June 18, 2021, from 81 hospitals in 19 countries. For reference, data from patients with CVST between 2015 and 2018 were derived from an existing international registry. Clinical characteristics and mortality rate were described for adults with (1) CVST in the setting of SARS-CoV-2 vaccine-induced immune thrombotic thrombocytopenia, (2) CVST after SARS-CoV-2 vaccination not fulling criteria for TTS, and (3) CVST unrelated to SARS-CoV-2 vaccination. Exposures: Patients were classified as having TTS if they had new-onset thrombocytopenia without recent exposure to heparin, in accordance with the Brighton Collaboration interim criteria. Main outcomes and measures: Clinical characteristics and mortality rate. Results: Of 116 patients with postvaccination CVST, 78 (67.2%) had TTS, of whom 76 had been vaccinated with ChAdOx1 nCov-19; 38 (32.8%) had no indication of TTS. The control group included 207 patients with CVST before the COVID-19 pandemic. A total of 63 of 78 (81%), 30 of 38 (79%), and 145 of 207 (70.0%) patients, respectively, were female, and the mean (SD) age was 45 (14), 55 (20), and 42 (16) years, respectively. Concomitant thromboembolism occurred in 25 of 70 patients (36%) in the TTS group, 2 of 35 (6%) in the no TTS group, and 10 of 206 (4.9%) in the control group, and in-hospital mortality rates were 47% (36 of 76; 95% CI, 37-58), 5% (2 of 37; 95% CI, 1-18), and 3.9% (8 of 207; 95% CI, 2.0-7.4), respectively. The mortality rate was 61% (14 of 23) among patients in the TTS group diagnosed before the condition garnered attention in the scientific community and 42% (22 of 53) among patients diagnosed later. Conclusions and relevance: In this cohort study of patients with CVST, a distinct clinical profile and high mortality rate was observed in patients meeting criteria for TTS after SARS-CoV-2 vaccination.info:eu-repo/semantics/publishedVersio

European Academy of Neurology and European Stroke Organization consensus statement and practical guidance for pre-hospital management of stroke

Background and purposeThe reduction of delay between onset and hospital arrival and adequate pre-hospital care of persons with acute stroke are important for improving the chances of a favourable outcome. The objective is to recommend evidence-based practices for the management of patients with suspected stroke in the pre-hospital setting. MethodsThe GRADE (Grading of Recommendations Assessment, Development and Evaluation) methodology was used to define the key clinical questions. An expert panel then reviewed the literature, established the quality of the evidence, and made recommendations. ResultsDespite very low quality of evidence educational campaigns to increase the awareness of immediately calling emergency medical services are strongly recommended. Moderate quality evidence was found to support strong recommendations for the training of emergency medical personnel in recognizing the symptoms of a stroke and in implementation of a pre-hospital code stroke' including highest priority dispatch, pre-hospital notification and rapid transfer to the closest stroke-ready' centre. Insufficient evidence was found to recommend a pre-hospital stroke scale to predict large vessel occlusion. Despite the very low quality of evidence, restoring normoxia in patients with hypoxia is recommended, and blood pressure lowering drugs and treating hyperglycaemia with insulin should be avoided. There is insufficient evidence to recommend the routine use of mobile stroke units delivering intravenous thrombolysis at the scene. Because only feasibility studies have been reported, no recommendations can be provided for pre-hospital telemedicine during ambulance transport. ConclusionsThese guidelines inform on the contemporary approach to patients with suspected stroke in the pre-hospital setting. Further studies, preferably randomized controlled trials, are required to examine the impact of particular interventions on quality parameters and outcome. Click for the corresponding questions to this CME article

Exploring Unsupervised Learning Techniques for the Internet of Things

Nowadays, machine learning (ML) techniques can provide new perspectives to identify hidden patterns and classes inside data. Applying ML to the Internet of Things (IoT) and its produced data represents a great challenge in every application domain, since analyzing IoT data increasingly requires the use of advanced mathematical algorithms, novel computational techniques, and services. In this article, we present and discuss the application of unsupervised learning techniques on IoT data collected in a cultural heritage framework. Behavioral data have been gathered in a noninvasive way in order to achieve an ML classification that can be exploited by cultural stakeholders in terms of the medium-to long-term strategy and also in terms of strictly operational decisions. The application of ML and other learning techniques will acquire a key role to complement the more traditional services with new intelligent ones able to satisfy the needs of companies, stakeholders, and consumers

Path prediction in IoT systems through Markov Chain algorithm

In the Data Technology Era, inferring knowledge from data is an ubiquitous and pervasive research topic. Digital Ecosystems based on the Internet of Things (IoT) are generally designed for generating and collecting complex, real-time and (un)structured data. As one of the main component of the Smart City framework, the huge amount of IoT data has to be opportunely processed, also through Machine Learning algorithms in order to discover new knowledge and to improve the quality-of-life of the citizens. In our research work we propose some learning methodologies to analyse and forecast visitors’ paths within a cultural and complex space. Starting from data collected in a museum equipped with a non-invasive monitoring IoT system, we show how it is possible to discover and predict useful information on visitors’ movements and, finally, we present and discuss some useful insights on their behaviours within a real case-of-study

Unsupervised learning on multimedia data: a Cultural Heritage case study

Integrating and analyzing a large amount of data extracted from different sources can be considered a key asset for businesses, organizations, research institutions that also deal with the Cultural Heritage domain. In the last decade, Internet of Things (IoT) technologies and the massive use of mobile devices contributed to generate an enormous flow of multimedia data, whose collection, analysis and interpretation allows for real-time analysis related to the behaviours, preferences and opinions of users. In this paper we present and discuss an unsupervised learning approach on multimedia features of a dataset coming from an Internet of Things framework. The main research objective of this work is to assess how the collection of behavioural IoT data coming from the Cultural Heritage domain can be opportunely exploited by means of unsupervised learning techniques in order to produce useful insights for the stakeholders, especially considering the multimedia features of such data. The presented experimental results, executed in a real case study, assess how the Cultural Heritage domain, and the related stakeholders, can benefit from these kind of services and applications

Nature-inspired algorithm-based secure data dissemination framework for smart city networks

Unceasing population growth and urbanization have intensified the traditional systems to deal with citizen lifestyle, environment, economic issues and good governess. New communication technologies have played a vital role in changes traditional urbanization into a smarter and comfort zone for the citizen. Due to various systems and integration of several new standards and systems, the smart cities have suffered from various open challenges related to technologies, system controlling and management, scalability and security concerns. The new concepts of nature-inspired solutions have implemented to deal with smart cities’ challenges by more optimization and performance-oriented methods. Therefore, this paper aims to handle at least three areas of smart cities including smart mobility, smart living and security provision by developing three nature-inspired solutions. The three proposed solutions are dragon clustering mobility in IoV, moth flame electric management for smart living and ant colony-based intrusion detection system for security provision. These solutions are based on a dragonfly, moth flame and ant colony optimization techniques. The proposed solutions are evaluated in a simulation to check the performance. These solutions will help new researchers to explore the nature-inspired solutions to tackle the new and complex systems of smart cities

Decision Making in IoT Environment through Unsupervised Learning

Nowadays Unsupervised Learning can provide new perspectives to identify hidden patterns and classes inside the huge amount of data coming from the Internet of Things world. Analyzing IoT data through machine learning techniques requires the use of mathematical algorithms, computational techniques and an accurate tuning of the input parameters. In this paper we present a study of unsupervised learning techniques applied on IoT data to support decision making processes inside intelligent environments. To assess the proposed approach we discuss two case-of-study in which behavioural IoT data have been collected, also in a non-invasive way, in order to achieve an unsupervised classification that can be adopted during a decision making process. The use of Unsupervised Learning techniques is acquiring a key role to complement the more traditional services with new decision making ones supporting the needs of companies, stakeholders and consumer

A Deep Learning approach for Path Prediction in a Location-based IoT system

Knowing in real-time the position of objects and people, both in indoor and outdoor spaces, allows companies and organizations to improve their processes and offer new kind of services. Nowadays Location-based Services (LBS) generate a significant amount of data thank to the widespread of the Internet of Things; since they have been quickly perceived as a potential source of profit, several companies have started to design and develop a wide range of such services. One of the most challenging research tasks is undoubtedly represented by the analysis of LBS data through Machine Learning algorithms and methodologies in order to infer new knowledge and build-up even more customized services. Cultural Heritage is a domain that can benefit from such studies since it is characterized by a strong interaction between people, cultural items and spaces. Data gathered in a museum on visitor movements and behaviours can constitute the knowledge base to realize an advanced monitoring system able to offer museum stakeholders a complete and real-time snapshot of the museum locations occupancy. Furthermore, exploiting such data through Deep Learning methodologies can lead to the development of a predictive monitoring system able to suggest stakeholders the museum locations occupancy not only in real-time but also in the next future, opening new scenarios in the management of a museum. In this paper, we present and discuss a Deep Learning methodology applied to data coming from a non-invasive Bluetooth IoT monitoring system deployed inside a cultural space. Through the analysis of visitors’ paths, the main goal is to predict the occupancy of the available rooms. Experimental results on real data demonstrate the feasibility of the proposed approach; it can represent a useful instrument, in the hands of the museum management, to enhance the quality-of-service within this kind of spaces

Pharmacokinetic evaluation of almotriptan for the treatment of migraines

Introduction: Migraine is a multifactorial neurovascular disorder characterized by recurrent episodes of disabling pain attacks, accompanied with gastrointestinal, neurological systems dysfunction. The pharmacologic treatment of migraine is classically divided in the management of the acute attack and preventive strategies. Triptans represent a powerful pharmacological tool in acute migraine treatment. However, a significant portion of treated patients cannot have access to this class due to possible adverse affects. Today, a total of seven triptan molecules are available, representing a commonly prescribed migraine treatment. Areas covered: The authors take a systematic approach to discuss the pharmacodynamic and pharmacokinetic aspects of almotriptan . They consider the emerging data on the clinical efficacy in the treatment of migraine and menstrual-related migraine. The data were obtained by searching the following key words in MEDLINE: pharmacokinetic, pharmacodynamic, triptans, almotriptan, migraine, menstrual migraine, relatively to the period 1989 - 2012. Expert opinion: The excellent efficacy and superior tolerability profile of almotriptan administered early offer a potential improvement over existing triptans for the symptomatic treatment of migraine attacks. Compared with other triptans, the different pathways involved in the metabolism of almotriptan ensure a limited variability of clinical response to the drug, making it less susceptible to the individual genomic background