Hospitalizations due to rotavirus gastroenteritis in Catalonia, Spain, 2003-2008

BACKGROUND: Rotavirus is the most common cause of severe gastroenteritis among young children in Spain and worldwide. We evaluated hospitalizations due to community and hospital-acquired rotavirus gastroenteritis (RVGE) and estimated related costs in children under 5 years old in Catalonia, Spain. RESULTS: We analyzed hospital discharge data from the Catalan Health Services regarding hospital admissions coded as infectious gastroenteritis in children under 5 for the period 2003-2008. In order to estimate admission incidence, we used population estimates for each study year published by the Statistic Institut of Catalonia (Idescat). The costs associated with hospital admissions due to rotavirus diarrhea were estimated for the same years. A decision tree model was used to estimate the threshold cost of rotavirus vaccine to achieve cost savings from the healthcare system perspective in Catalonia. From 2003 through 2008, 10655 children under 5 years old were admitted with infectious gastroenteritis (IGE). Twenty-two percent of these admissions were coded as RVGE, yielding an estimated average annual incidence of 104 RVGE hospitalizations per 100000 children in Catalonia. Eighty seven percent of admissions for RVGE occurred during December through March. The mean hospital stay was 3.7 days, 0.6 days longer than for other IGE. An additional 892 cases of presumed nosocomial RVGE were detected, yielding an incidence of 2.5 cases per 1000 child admissions. Total rotavirus hospitalization costs due to community acquired RVGE for the years 2003 and 2008 were 431,593 and 809,224 €, respectively. According to the estimated incidence and hospitalization costs, immunization would result in health system cost savings if the cost of the vaccine was 1.93 € or less. At a vaccine cost of 187 € the incremental cost per hospitalization prevented is 195,388 € (CI 95% 159,300; 238,400). CONCLUSIONS: The burden of hospitalizations attributable to rotavirus appeared to be lower in Catalonia than in other regions of Spain and Europe. The relatively low incidence of hospitalization due to rotavirus makes rotavirus vaccination less cost-effective in Catalonia than in other areas with higher rotavirus disease burden

Novel multiple sclerosis susceptibility loci implicated in epigenetic regulation

We conducted a genome-wide association study (GWAS) on multiple sclerosis (MS) susceptibility in German cohorts with 4888 cases and 10,395 controls. In addition to associations within the major histocompatibility complex (MHC) region, 15 non-MHC loci reached genome-wide significance. Four of these loci are novel MS susceptibility loci. They map to the genes L3MBTL3, MAZ, ERG, and SHMT1. The lead variant at SHMT1 was replicated in an independent Sardinian cohort. Products of the genes L3MBTL3, MAZ, and ERG play important roles in immune cell regulation. SHMT1 encodes a serine hydroxymethyltransferase catalyzing the transfer of a carbon unit to the folate cycle. This reaction is required for regulation of methylation homeostasis, which is important for establishment and maintenance of epigenetic signatures. Our GWAS approach in a defined population with limited genetic substructure detected associations not found in larger, more heterogeneous cohorts, thus providing new clues regarding MS pathogenesis

A global reference database of crowdsourced cropland data collected using the Geo-Wiki platform

A global reference data set on cropland was collected through a crowdsourcing campaign using the Geo-Wiki crowdsourcing tool. The campaign lasted three weeks, with over 80 participants from around the world reviewing almost 36,000 sample units, focussing on cropland identification. For quality assessment purposes, two additional data sets are provided. The first is a control set of 1,793 sample locations validated by students trained in satellite image interpretation. This data set was used to assess the quality of the crowd as the campaign progressed. The second data set contains 60 expert validations for additional evaluation of the quality of the contributions. All data sets are split into two parts: the first part shows all areas classified as cropland and the second part shows cropland average per location and user. After further processing, the data presented here might be suitable to validate and compare medium and high resolution cropland maps generated using remote sensing. These could also be used to train classification algorithms for developing new maps of land cover and cropland extent

Superior antigen-specific CD4+ T-cell response with AS03-adjuvantation of a trivalent influenza vaccine in a randomised trial of adults aged 65 and older

BACKGROUND: The effectiveness of trivalent influenza vaccines may be reduced in older versus younger adults because of age-related immunosenescence. The use of an adjuvant in such a vaccine is one strategy that may combat immunosenescence, potentially by bolstering T-cell mediated responses. METHODS: This observer-blind study, conducted in the United States (US) and Spain during the 2008-2009 influenza season, evaluated the effect of Adjuvant System AS03 on specific T-cell responses to a seasonal trivalent influenza vaccine (TIV) in >/=65 year-old adults.Medically-stable adults aged >/=65 years were randomly allocated to receive a single dose of AS03-adjuvanted TIV (TIV/AS03) or TIV. Healthy adults aged 18-40 years received only TIV. Blood samples were collected on Day 0, Day 21, Day 42 and Day 180. Influenza-specific CD4+ T cells, defined by the induction of the immune markers CD40L, IL-2, IFN-gamma, or TNF-alpha, were measured in ex vivo cultures of antigen-stimulated peripheral blood mononuclear cells. RESULTS: A total of 192 adults were vaccinated: sixty nine and seventy three >/=65 year olds received TIV/AS03 and TIV, respectively; and fifty 18 - 40 year olds received TIV. In the >/=65 year-old group on Day 21, the frequency of CD4+ T cells specific to the three vaccine strains was superior in the TIV/AS03 recipients to the frequency in TIV (p /=65 year-old recipients of TIV/AS03 than in the 18 - 40 year old recipients of TIV on Days 21 (p = 0.006) and 42 (p = 0.011). CONCLUSION: This positive effect of AS03 Adjuvant System on the CD4+ T-cell response to influenza vaccine strains in older adults could confer benefit in protection against clinical influenza disease in this population. TRIAL REGISTRATION: (Clinicaltrials.gov.). NCT00765076

Sistema de monitoreo de parámetros cardiovasculares basado en IoT y MQTT para alertas médicas

Este artículo describe el desarrollo de una plataforma informática destinada al monitoreo en tiempo real de parámetros cardiovasculares a partir de señales bioeléctricas. Se realizó un análisis de los usuarios principales y se identificaron los requisitos técnicos y funcionales necesarios. Asimismo, las interfaces fueron diseñadas aplicando la metodología propuesta por Sommerville. La arquitectura del sistema se basa en microservicios, incorporando una base de datos relacional y permitiendo la integración con datos provenientes de dispositivos del internet de las cosas (IoT). La evaluación del sistema se llevó a cabo mediante pruebas de simulación de carga, iniciando con 0 usuarios y aumentando en intervalos de 100 hasta alcanzar los 5000 usuarios. Durante las pruebas, se procesaron 22 132 solicitudes, con una tasa promedio de 440,4 solicitudes por segundo, manteniendo un tiempo de respuesta medio de 930 ms y logrando que el 95 % de las respuestas se ubicaran por debajo de los 2300 ms. Se comprobó que el sistema opera sin errores hasta un umbral de 1700 usuarios concurrentes. Con 5000 usuarios y un total de 26 393 solicitudes, se registró un porcentaje mínimo de error del 0,16 %, lo que evidencia su capacidad para gestionar altas cargas de trabajo de manera estable. Estos resultados confirman la viabilidad de la plataforma para el monitoreo remoto de parámetros biomédicos, ofreciendo una solución eficiente y escalable para la supervisión de la salud en tiempo real.//This paper presents the development of a computing platform for the real-time monitoring of cardiovascular parameters derived from bioelectrical signals. A comprehensive analysis of primary users was conducted, leading to the identification of both technical and functional requirements. The interface design was guided by Sommerville’s methodology. The system architecture is based on a microservices model, incorporating a relational database and enabling integration with data transmitted from Internet of Things (IoT) devices. The platform was evaluated through incremental stress testing, starting with zero users and increasing in steps of 100 up to 5,000. A total of 22,132 requests were processed at a peak rate of 440.4 requests per second, with an average response time of 930 ms and 95% of responses occurring within 2,300 ms. The system demonstrated error-free performance with up to 1,700 concurrent users. At 5,000 users and 26,393 total requests, a minimal error rate of 0.16% was recorded, confirming the platform’s stability under high workloads. These findings validate the feasibility of the proposed solution for remote biomedical monitoring, offering an efficient, scalable, and robust tool for real-time health supervision

StarBorn : Towards making in-situ land cover data generation fun with a location-based game

University Research Priority Program: Language and Space, University of ZurichPeer reviewedPublisher PD

Clinical implications of serum neurofilament in newly diagnosed MS patients: a longitudinal multicentre cohort study

BACKGROUND: We aim to evaluate serum neurofilament light chain (sNfL), indicating neuroaxonal damage, as a biomarker at diagnosis in a large cohort of early multiple sclerosis (MS) patients. METHODS: In a multicentre prospective longitudinal observational cohort, patients with newly diagnosed relapsing-remitting MS (RRMS) or clinically isolated syndrome (CIS) were recruited between August 2010 and November 2015 in 22 centers. Clinical parameters, MRI, and sNfL levels (measured by single molecule array) were assessed at baseline and up to four-year follow-up. FINDINGS: Of 814 patients, 54.7% (445) were diagnosed with RRMS and 45.3% (369) with CIS when applying 2010 McDonald criteria (RRMS[2010] and CIS[2010]). After reclassification of CIS[2010] patients with existing CSF analysis, according to 2017 criteria, sNfL levels were lower in CIS[2017] than RRMS[2017] patients (9.1 pg/ml, IQR 6.2-13.7 pg/ml, n = 45; 10.8 pg/ml, IQR 7.4-20.1 pg/ml, n = 213; p = 0.036). sNfL levels correlated with number of T2 and Gd+ lesions at baseline and future clinical relapses. Patients receiving disease-modifying therapy (DMT) during the first four years had higher baseline sNfL levels than DMT-naïve patients (11.8 pg/ml, IQR 7.5-20.7 pg/ml, n = 726; 9.7 pg/ml, IQR 6.4-15.3 pg/ml, n = 88). Therapy escalation decisions within this period were reflected by longitudinal changes in sNfL levels. INTERPRETATION: Assessment of sNfL increases diagnostic accuracy, is associated with disease course prognosis and may, particularly when measured longitudinally, facilitate therapeutic decisions

Accounting for training data error in machine learning applied to earth observations

Remote sensing, or Earth Observation (EO), is increasingly used to understand Earth system dynamics and create continuous and categorical maps of biophysical properties and land cover, especially based on recent advances in machine learning (ML). ML models typically require large, spatially explicit training datasets to make accurate predictions. Training data (TD) are typically generated by digitizing polygons on high spatial-resolution imagery, by collecting in situ data, or by using pre-existing datasets. TD are often assumed to accurately represent the truth, but in practice almost always have error, stemming from (1) sample design, and (2) sample collection errors. The latter is particularly relevant for image-interpreted TD, an increasingly commonly used method due to its practicality and the increasing training sample size requirements of modern ML algorithms. TD errors can cause substantial errors in the maps created using ML algorithms, which may impact map use and interpretation. Despite these potential errors and their real-world consequences for map-based decisions, TD error is often not accounted for or reported in EO research. Here we review the current practices for collecting and handling TD. We identify the sources of TD error, and illustrate their impacts using several case studies representing different EO applications (infrastructure mapping, global surface flux estimates, and agricultural monitoring), and provide guidelines for minimizing and accounting for TD errors. To harmonize terminology, we distinguish TD from three other classes of data that should be used to create and assess ML models: training reference data, used to assess the quality of TD during data generation; validation data, used to iteratively improve models; and map reference data, used only for final accuracy assessment. We focus primarily on TD, but our advice is generally applicable to all four classes, and we ground our review in established best practices for map accuracy assessment literature. EO researchers should start by determining the tolerable levels of map error and appropriate error metrics. Next, TD error should be minimized during sample design by choosing a representative spatio-temporal collection strategy, by using spatially and temporally relevant imagery and ancillary data sources during TD creation, and by selecting a set of legend definitions supported by the data. Furthermore, TD error can be minimized during the collection of individual samples by using consensus-based collection strategies, by directly comparing interpreted training observations against expert-generated training reference data to derive TD error metrics, and by providing image interpreters with thorough application-specific training. We strongly advise that TD error is incorporated in model outputs, either directly in bias and variance estimates or, at a minimum, by documenting the sources and implications of error. TD should be fully documented and made available via an open TD repository, allowing others to replicate and assess its use. To guide researchers in this process, we propose three tiers of TD error accounting standards. Finally, we advise researchers to clearly communicate the magnitude and impacts of TD error on map outputs, with specific consideration given to the likely map audience

A global reference database of crowdsourced cropland data collected using the Geo-Wiki platform

A global reference data set on cropland was collected through a crowdsourcing campaign using the Geo-Wiki crowdsourcing tool. The campaign lasted three weeks, with over 80 participants from around the world reviewing almost 36,000 sample units, focussing on cropland identification. For quality assessment purposes, two additional data sets are provided. The first is a control set of 1,793 sample locations validated by students trained in satellite image interpretation. This data set was used to assess the quality of the crowd as the campaign progressed. The second data set contains 60 expert validations for additional evaluation of the quality of the contributions. All data sets are split into two parts: the first part shows all areas classified as cropland and the second part shows cropland average per location and user. After further processing, the data presented here might be suitable to validate and compare medium and high resolution cropland maps generated using remote sensing. These could also be used to train classification algorithms for developing new maps of land cover and cropland extent