Anti-asthma medication prescribing to children in the Lombardy Region of Italy: chronic versus new users

<p>Abstract</p> <p>Background</p> <p>Although anti-asthma medications are amongst those most frequently under or over prescribed it is generally accepted that prescriptions for such agents can be used as a proxy for disease prevalence. The aims of this study were to estimate prevalence and incidence of childhood asthma in a representative Italian area by analysing three years of anti-asthmatic prescriptions and hospitalizations of subjects with chronic or first time treatment, and to underline appropriateness of therapeutic choices.</p> <p>Methods</p> <p>The analysis involved prescriptions given to 6-17 year olds between 2003 and 2005 in Italy's Lombardy Region. The youths were classified as potential asthmatics, based on the different degree of drug utilization: occasional, low or high users, and grouped as 'new onset' or 'chronic' cases based on the duration of therapy dispensed. The analysis of prescriptions and hospitalization rate of these groups provided an estimate of the 2005 asthma prevalence and incidence and allowed an estimation of the level of appropriateness of treatments.</p> <p>Results</p> <p>During 2005, the estimated incidence of potential asthmatics was 0.8% and the estimated prevalence was 3.5%. When viewed retrospectively for two years, records showed that 47% of potential asthmatics received prescriptions also during 2004 and 30% also during 2003. During the three years considered, 7.5%, 2.8%, and 1.5% of high, low, and occasional users, respectively, were hospitalized for asthma. The most important inappropriateness found was the prescription of long acting beta adrenergics as first time treatment.</p> <p>Conclusions</p> <p>This study allowed a proxy of asthma incidence, prevalence, and severity. The analyses highlighted a low compliance with the guidelines, suggesting that educational interventions are needed to obtain a more rational management of childhood asthma, especially in subjects starting therapy.</p

Surface PEGylation suppresses pulmonary effects of CuO in allergen-induced lung inflammation

BACKGROUND: Copper oxide (CuO) nanomaterials are used in a wide range of industrial and commercial applications. These materials can be hazardous, especially if they are inhaled. As a result, the pulmonary effects of CuO nanomaterials have been studied in healthy subjects but limited knowledge exists today about their effects on lungs with allergic airway inflammation (AAI). The objective of this study was to investigate how pristine CuO modulates allergic lung inflammation and whether surface modifications can influence its reactivity. CuO and its carboxylated (CuO COOH), methylaminated (CuO NH3) and PEGylated (CuO PEG) derivatives were administered here on four consecutive days via oropharyngeal aspiration in a mouse model of AAI. Standard genome-wide gene expression profiling as well as conventional histopathological and immunological methods were used to investigate the modulatory effects of the nanomaterials on both healthy and compromised immune system. RESULTS: Our data demonstrates that although CuO materials did not considerably influence hallmarks of allergic airway inflammation, the materials exacerbated the existing lung inflammation by eliciting dramatic pulmonary neutrophilia. Transcriptomic analysis showed that CuO, CuO COOH and CuO NH3 commonly enriched neutrophil-related biological processes, especially in healthy mice. In sharp contrast, CuO PEG had a significantly lower potential in triggering changes in lungs of healthy and allergic mice revealing that surface PEGylation suppresses the effects triggered by the pristine material. CONCLUSIONS: CuO as well as its functionalized forms worsen allergic airway inflammation by causing neutrophilia in the lungs, however, our results also show that surface PEGylation can be a promising approach for inhibiting the effects of pristine CuO. Our study provides information for health and safety assessment of modified CuO materials, and it can be useful in the development of nanomedical applications

Internet of Things for Sustainable Community Development: Introduction and Overview

The two-third of the city-dwelling world population by 2050 poses numerous global challenges in the infrastructure and natural resource management domains (e.g., water and food scarcity, increasing global temperatures, and energy issues). The IoT with integrated sensing and communication capabilities has the strong potential for the robust, sustainable, and informed resource management in the urban and rural communities. In this chapter, the vital concepts of sustainable community development are discussed. The IoT and sustainability interactions are explained with emphasis on Sustainable Development Goals (SDGs) and communication technologies. Moreover, IoT opportunities and challenges are discussed in the context of sustainable community development

Cognitive Information Processing

Contains goals, background, research activities on one research project and reports on three research projects.Center for Advanced Television StudiesAmerican Broadcasting CompanyAmpex CorporationColumbia Broadcasting SystemsHarris CorporationHome Box OfficePublic Broadcasting ServiceNational Broadcasting CompanyRCA CorporationTektronix3M CompanyProvidence Gravure Co. (Grant)International Business Machines, Inc

Identifying Biological Network Structure, Predicting Network Behavior, and Classifying Network State With High Dimensional Model Representation (HDMR)

This work presents an adapted Random Sampling - High Dimensional Model Representation (RS-HDMR) algorithm for synergistically addressing three key problems in network biology: (1) identifying the structure of biological networks from multivariate data, (2) predicting network response under previously unsampled conditions, and (3) inferring experimental perturbations based on the observed network state. RS-HDMR is a multivariate regression method that decomposes network interactions into a hierarchy of non-linear component functions. Sensitivity analysis based on these functions provides a clear physical and statistical interpretation of the underlying network structure. The advantages of RS-HDMR include efficient extraction of nonlinear and cooperative network relationships without resorting to discretization, prediction of network behavior without mechanistic modeling, robustness to data noise, and favorable scalability of the sampling requirement with respect to network size. As a proof-of-principle study, RS-HDMR was applied to experimental data measuring the single-cell response of a protein-protein signaling network to various experimental perturbations. A comparison to network structure identified in the literature and through other inference methods, including Bayesian and mutual-information based algorithms, suggests that RS-HDMR can successfully reveal a network structure with a low false positive rate while still capturing non-linear and cooperative interactions. RS-HDMR identified several higher-order network interactions that correspond to known feedback regulations among multiple network species and that were unidentified by other network inference methods. Furthermore, RS-HDMR has a better ability to predict network response under unsampled conditions in this application than the best statistical inference algorithm presented in the recent DREAM3 signaling-prediction competition. RS-HDMR can discern and predict differences in network state that arise from sources ranging from intrinsic cell-cell variability to altered experimental conditions, such as when drug perturbations are introduced. This ability ultimately allows RS-HDMR to accurately classify the experimental conditions of a given sample based on its observed network state

Oral health service utilization by elderly beneficiaries of the Mexican Institute of Social Security in México city

<p>Abstract</p> <p>Background</p> <p>The aging population poses a challenge to Mexican health services. The aim of this study is to describe recent oral health services utilization and its association with socio-demographic characteristics and co-morbidity in Mexican Social Security beneficiaries 60 years and older.</p> <p>Methods</p> <p>A sample of 700 individuals aged 60+ years was randomly chosen from the databases of the Mexican Institute of Social Security (IMSS). These participants resided in the southwest of Mexico City and made up the final sample of a cohort study for identifying risk factors for root caries in elderly patients. Sociodemographic variables, presence of cognitive decline, depression, morbidity, medication consumption, and utilization of as well as reasons for seeking oral health services within the past 12 months were collected through a questionnaire. Clinical oral assessments were carried out to determine coronal and root caries experience.</p> <p>Results</p> <p>The sample consisted of 698 individuals aged 71.6 years on average, of whom 68.3% were women. 374 participants (53.6%) had made use of oral health services within the past 12 months. 81% of those who used oral health services sought private medical care, 12.8% sought social security services, and 6.2% public health services. 99.7% had experienced coronal caries and 44.0% root caries. Female sex (OR = 2.0), 6 years' schooling or less (OR = 1.4), and caries experience in more than 22 teeth (OR = 0.6) are factors associated with the utilization of these services.</p> <p>Conclusion</p> <p>About half the elderly beneficiaries of social security have made use of oral health services within the past 12 months, and many of them have to use private services. Being a woman, having little schooling, and low caries experience are factors associated with the use of these services.</p

Internet of Things for Sustainable Human Health

The sustainable health IoT has the strong potential to bring tremendous improvements in human health and well-being through sensing, and monitoring of health impacts across the whole spectrum of climate change. The sustainable health IoT enables development of a systems approach in the area of human health and ecosystem. It allows integration of broader health sub-areas in a bigger archetype for improving sustainability in health in the realm of social, economic, and environmental sectors. This integration provides a powerful health IoT framework for sustainable health and community goals in the wake of changing climate. In this chapter, a detailed description of climate-related health impacts on human health is provided. The sensing, communications, and monitoring technologies are discussed. The impact of key environmental and human health factors on the development of new IoT technologies also analyzed

Biomarkers of nanomaterials hazard from multi-layer data

Nanomaterials have a range of potential applications, however, toxicity remains a concern, limiting application and requiring extensive testing. Here, the authors report on a predictive framework made using a range of tests linking materials properties with toxicity, allowing the prediction of toxicity from physiochemical and biological properties.There is an urgent need to apply effective, data-driven approaches to reliably predict engineered nanomaterial (ENM) toxicity. Here we introduce a predictive computational framework based on the molecular and phenotypic effects of a large panel of ENMs across multiple in vitro and in vivo models. Our methodology allows for the grouping of ENMs based on multi-omics approaches combined with robust toxicity tests. Importantly, we identify mRNA-based toxicity markers and extensively replicate them in multiple independent datasets. We find that models based on combinations of omics-derived features and material intrinsic properties display significantly improved predictive accuracy as compared to physicochemical properties alone