A Comparative Analysis of Inequality in Health Across Europe

The study of inequality in health concerns the relationship between socially structured characteristics and health outcomes. Howewer, health disparities are also linked to purely individual characteristics and contextual ones. In particular, the contextual effect at a national level may reflect differences in the functioning and performing of national health institutions, that may be conceived as further determinants of health inequalities. In this work we aim at estimating the effect of education on self-assessed health across European countries, taking into account potential confounders like age, gender and family social background. Using a multilevel model with individuals nested in countries, we can achieve two aims. First, we can see whether countries differ in their average self-assessed health score. Second, we can test our hypothesis about the existence of a European social gradient, that is that education exerts a relatively constant effect on self-assessed health. We develop our models using data from European Social Surveys (88,842 interviews).Health Inequalities, Health Policies, Public Health Care Systems, Comparative Studies

A Multi-Modal Sensing Glove for Human Manual-Interaction Studies

We present an integrated sensing glove that combines two of the most visionary wearable sensing technologies to provide both hand posture sensing and tactile pressure sensing in a unique, lightweight, and stretchable device. Namely, hand posture reconstruction employs Knitted Piezoresistive Fabrics that allows us to measure bending. From only five of these sensors (one for each finger) the full hand pose of a 19 degrees of freedom (DOF) hand model is reconstructed leveraging optimal sensor placement and estimation techniques. To this end, we exploit a-priori information of synergistic coordination patterns in grasping tasks. Tactile sensing employs a piezoresistive fabric allowing us to measure normal forces in more than 50 taxels spread over the palmar surface of the glove. We describe both sensing technologies, report on the software integration of both modalities, and describe a preliminary evaluation experiment analyzing hand postures and force patterns during grasping. Results of the reconstruction are promising and encourage us to push further our approach with potential applications in neuroscience, virtual reality, robotics and tele-operation

A Synergy-Based Optimally Designed Sensing Glove for Functional Grasp Recognition

Achieving accurate and reliable kinematic hand pose reconstructions represents a challenging task. The main reason for this is the complexity of hand biomechanics, where several degrees of freedom are distributed along a continuous deformable structure. Wearable sensing can represent a viable solution to tackle this issue, since it enables a more natural kinematic monitoring. However, the intrinsic accuracy (as well as the number of sensing elements) of wearable hand pose reconstruction (HPR) systems can be severely limited by ergonomics and cost considerations. In this paper, we combined the theoretical foundations of the optimal design of HPR devices based on hand synergy information, i.e., the inter-joint covariation patterns, with textile goniometers based on knitted piezoresistive fabrics (KPF) technology, to develop, for the first time, an optimally-designed under-sensed glove for measuring hand kinematics. We used only five sensors optimally placed on the hand and completed hand pose reconstruction (described according to a kinematic model with 19 degrees of freedom) leveraging upon synergistic information. The reconstructions we obtained from five different subjects were used to implement an unsupervised method for the recognition of eight functional grasps, showing a high degree of accuracy and robustness

Deep Learning Techniques for the Dermoscopic Differential Diagnosis of Benign/Malignant Melanocytic Skin Lesions: From the Past to the Present

There has been growing scientific interest in the research field of deep learning techniques applied to skin cancer diagnosis in the last decade. Though encouraging data have been globally reported, several discrepancies have been observed in terms of study methodology, result presentations and validation in clinical settings. The present review aimed to screen the scientific literature on the application of DL techniques to dermoscopic melanoma/nevi differential diagnosis and extrapolate those original studies adequately by reporting on a DL model, comparing them among clinicians and/or another DL architecture. The second aim was to examine those studies together according to a standard set of statistical measures, and the third was to provide dermatologists with a comprehensive explanation and definition of the most used artificial intelligence (AI) terms to better/further understand the scientific literature on this topic and, in parallel, to be updated on the newest applications in the medical dermatologic field, along with a historical perspective. After screening nearly 2000 records, a subset of 54 was selected. Comparing the 20 studies reporting on convolutional neural network (CNN)/deep convolutional neural network (DCNN) models, we have a scenario of highly performant DL algorithms, especially in terms of low false positive results, with average values of accuracy (83.99%), sensitivity (77.74%), and specificity (80.61%). Looking at the comparison with diagnoses by clinicians (13 studies), the main difference relies on the specificity values, with a +15.63% increase for the CNN/DCNN models (average specificity of 84.87%) compared to humans (average specificity of 64.24%) with a 14,85% gap in average accuracy; the sensitivity values were comparable (79.77% for DL and 79.78% for humans). To obtain higher diagnostic accuracy and feasibility in clinical practice, rather than in experimental retrospective settings, future DL models should be based on a large dataset integrating dermoscopic images with relevant clinical and anamnestic data that is prospectively tested and adequately compared with physicians

A Comparative Analysis of Inequality in Health Across Europe

Abstract The study of inequality in health concerns the relationship between socially structured characteristics and health outcomes. Howewer, health disparities are also linked to purely individual characteristics and contextual ones. In particular, the contextual effect at a national level may reflect differences in the functioning and performing of national health institutions, that may be conceived as further determinants of health inequalities. In this work we aim at estimating the effect of education on self-assessed health across European countries, taking into account potential confounders like age, gender and family social background. Using a multilevel model with individuals nested in countries, we can achieve two aims. First, we can see whether countries differ in their average self-assessed health score. Second, we can test our hypothesis about the existence of a European social gradient, that is that education exerts a relatively constant effect on self-assessed health. We develop our models using data from European Social Surveys (88,842 interviews)

Multicenter clinical assessment of improved wearable multimodal convulsive seizure detectors

Objective New devices are needed for monitoring seizures, especially those associated with sudden unexpected death in epilepsy (SUDEP). They must be unobtrusive and automated, and provide false alarm rates (FARs) bearable in everyday life. This study quantifies the performance of new multimodal wrist-worn convulsive seizure detectors. Methods Hand-annotated video-electroencephalographic seizure events were collected from 69 patients at six clinical sites. Three different wristbands were used to record electrodermal activity (EDA) and accelerometer (ACM) signals, obtaining 5,928 h of data, including 55 convulsive epileptic seizures (six focal tonic–clonic seizures and 49 focal to bilateral tonic–clonic seizures) from 22 patients. Recordings were analyzed offline to train and test two new machine learning classifiers and a published classifier based on EDA and ACM. Moreover, wristband data were analyzed to estimate seizure-motion duration and autonomic responses. Results The two novel classifiers consistently outperformed the previous detector. The most efficient (Classifier III) yielded sensitivity of 94.55%, and an FAR of 0.2 events/day. No nocturnal seizures were missed. Most patients had <1 false alarm every 4 days, with an FAR below their seizure frequency. When increasing the sensitivity to 100% (no missed seizures), the FAR is up to 13 times lower than with the previous detector. Furthermore, all detections occurred before the seizure ended, providing reasonable latency (median = 29.3 s, range = 14.8–151 s). Automatically estimated seizure durations were correlated with true durations, enabling reliable annotations. Finally, EDA measurements confirmed the presence of postictal autonomic dysfunction, exhibiting a significant rise in 73% of the convulsive seizures. Significance The proposed multimodal wrist-worn convulsive seizure detectors provide seizure counts that are more accurate than previous automated detectors and typical patient self-reports, while maintaining a tolerable FAR for ambulatory monitoring. Furthermore, the multimodal system provides an objective description of motor behavior and autonomic dysfunction, aimed at enriching seizure characterization, with potential utility for SUDEP warning

Herbivory and nutrients shape grassland soil seed banks

Anthropogenic nutrient enrichment and shifts in herbivory can lead to dramatic changes in the composition and diversity of aboveground plant communities. In turn, this can alter seed banks in the soil, which are cryptic reservoirs of plant diversity. Here, we use data from seven Nutrient Network grassland sites on four continents, encompassing a range of climatic and environmental conditions, to test the joint effects of fertilization and aboveground mammalian herbivory on seed banks and on the similarity between aboveground plant communities and seed banks. We find that fertilization decreases plant species richness and diversity in seed banks, and homogenizes composition between aboveground and seed bank communities. Fertilization increases seed bank abundance especially in the presence of herbivores, while this effect is smaller in the absence of herbivores. Our findings highlight that nutrient enrichment can weaken a diversity maintaining mechanism in grasslands, and that herbivory needs to be considered when assessing nutrient enrichment effects on seed bank abundance.EEA Santa CruzFil: Eskelinen, Anu. German Centre for Integrative Biodiversity Research; AlemaniaFil: Eskelinen, Anu. Helmholtz Centre for Environmental Research. Department of Physiological Diversity; AlemaniaFil: Eskelinen, Anu. University of Oulu. Ecology & Genetics; FinlandiaFil: Jessen, Maria Theresa. Helmholtz Centre for Environmental Research. Department of Physiological Diversity; AlemaniaFil: Jessen, Maria Theresa. German Centre for Integrative Biodiversity Research; AlemaniaFil: Jessen, Maria Theresa. Helmholtz Centre for Environmental Research – UFZ. Department of Community Ecology; AlemaniaFil: Bahamonde, Hector Alejandro. Universidad Nacional de La Plata. Ciencias Agrarias y Forestales; Argentina.Fil: Bakker, Jonathan D. University of Washington. School of Environmental and Forest Sciences; Estados UnidosFil: Borer, Elizabeth T. University of Minnesota. Department of Ecology, Evolution & Behavior; Estados UnidosFil: Caldeira, Maria C. University of Lisbon. Forest Research Centre. Associate Laboratory TERRA. School of Agriculture; Portugal.Fil: Harpole, William Stanley. German Centre for Integrative Biodiversity Research (iDiv); AlemaniaFil: Harpole, William Stanley. Helmholtz Centre for Environmental Research – UFZ. Department of Community Ecology; AlemaniaFil: Harpole, William Stanley. Martin Luther University. Institute of Biology; AlemaniaFil: Jia, Meiyu. University of Washington. School of Environmental and Forest Sciences; Estados UnidosFil: Jia, Meiyu. East China University of Technology. School of Water Resources & Environmental Engineering; China.Fil: Jia, Meiyu. Beijing Normal University. College of Life Sciences; China.Fil: Lannes, Luciola S. São Paulo State University-UNESP. Department of Biology and Animal Sciences; Brasil.Fil: Nogueira, Carla. University of Lisbon. Forest Research Centre. Associate Laboratory TERRA. School of Agriculture; Portugal.Fil: Venterink, Harry Olde. Vrije Universiteit Brussel (VUB). Department of Biology; BélgicaFil: Peri, Pablo Luis. Instituto Nacional de Tecnología Agropecuaria (INTA). Estación Experimental Agropecuaria Santa Cruz; Argentina.Fil: Peri, Pablo Luis. Universidad Nacional de la Patagonia Austral; Argentina.Fil: Peri, Pablo Luis. Consejo Nacional de Investigaciones Científicas y Técnicas; Argentina.Fil: Porath-Krause, Anita J. University of Minnesota. Department of Ecology, Evolution & Behavior; Estados UnidosFil: Seabloom, Eric William. University of Minnesota. Department of Ecology, Evolution & Behavior; Estados UnidosFil: Schroeder, Katie. University of Minnesota. Department of Ecology, Evolution & Behavior; Estados UnidosFil: Schroeder, Katie. University of Georgia. Odum School of Ecology; Estados UnidosFil: Tognetti, Pedro M. Universidad de Buenos Aires. Facultad de Agronomía; Argentina.Fil: Tognetti, Pedro M. Consejo Nacional de Investigaciones Científicas y Técnicas. Instituto de Investigaciones Fisiológicas y Ecológicas Vinculadas a la Agricultura (IFEVA); Argentina.Fil: Tognetti, Pedro M. Swiss Federal Institute for Forest, Snow and Landscape Research WSL; SuizaFil: Yasui, Simone-Louise E. Queensland University of Technology. School of Biological and Environmental Sciences; Australia.Fil: Virtanen, Risto. University of Oulu. Ecology & Genetics; FinlandiaFil: Sullivan, Lauren L. University of Missouri. Division of Biological Sciences; Estados UnidosFil: Sullivan, Lauren L. Michigan State University. Department of Plant Biology; Estados UnidosFil: Sullivan, Lauren L. Michigan State University. W. K. Kellogg Biological Station; Estados UnidosFil: Sullivan, Lauren L. Michigan State University. Ecology, Evolution and Behavior Program; Estados Unido

Redox regulation of cell proliferation: Bioinformatics and redox proteomics approaches to identify redox-sensitive cell cycle regulators

Plant stem cells are the foundation of plant growth and development. The balance of quiescence and division is highly regulated, while ensuring that proliferating cells are protected from the adverse effects of environment fluctuations that may damage the genome. Redox regulation is important in both the activation of proliferation and arrest of the cell cycle upon perception of environmental stress. Within this context, reactive oxygen species serve as ‘pro-life’ signals with positive roles in the regulation of the cell cycle and survival. However, very little is known about the metabolic mechanisms and redox-sensitive proteins that influence cell cycle progression. We have identified cysteine residues on known cell cycle regulators in Arabidopsis that are potentially accessible, and could play a role in redox regulation, based on secondary structure and solvent accessibility likelihoods for each protein. We propose that redox regulation may function alongside other known posttranslational modifications to control the functions of core cell cycle regulators such as the retinoblastoma protein. Since our current understanding of how redox regulation is involved in cell cycle control is hindered by a lack of knowledge regarding both which residues are important and how modification of those residues alters protein function, we discuss how critical redox modifications can be mapped at the molecular level

Copayment e spesa in farmaci delle famiglie italiane: il complesso legame tra disuguaglianze sociali, politiche regionali e crisi economica

In recent years Italian citizens have increasingly been asked to share pharmaceutical cost but at the same time households’ medicines expenditures has decreased. Cost-sharing policies have to be assessed not just in terms of limitation of moral hazard and revenue to the State, but also for equal opportunities for citizen users accessing health services. The aim of this paper is to analyze how Italian copayment policies (‘ticket’) on medicines may affect pharmaceutical expenditure of households, considering territorial (Regions) and social groups (differentiated on the basis of deciles of equivalent consumption) variation. We use cross-sectional data from ISTAT Italian household consumptions survey and OSMED on revenue from ‘tickets’ on medicines in the Italian Regions. We applied two regression models (Tobit and OLS) to estimate coefficients between the consumption deciles and the expenditure on medicines. Across the period 2001-2010 we found that the overall per capita private spending on medicines remained substantially stable, although medicine expenditure decreases whereas the ‘ticket’ increases. When cost-sharing raises, out of pocket spending on medicines by poorer families seems to remain unchanged, however poorer families seem to reduce their pharmaceutical expenditure. Our analysis suggests that applying copayment in Italy is partly successful, in terms of greater revenue to the health system, but in the last few years cost-sharing increases would seem to have rebounded negatively on more vulnerable families, due to the economic crisis

Experimental Characterization of a Passive Wearable Tag for Respiration Rate Monitoring

The rise of interest in the Internet of Things (IoT) and the demand for the devices to keep up with the requirements of this new paradigm is driving the research community to develop innovative solutions and products for this purpose. In particular, for the monitoring of vital signs, including the respiration rate, the sensing devices used must measure and store measured data regarding the vital signs, generate reports, and alert healthcare professionals in dangerous or critical situations. In this paper, a passive wearable sensor tag for the monitoring of the respiration rate is presented. The proposed tag is suitable for real-time continuous monitoring and for integration and conformity with IoT requirements. The proposed sensor was experimentally verified on a human test subject and the respiration frequency at different breathing patterns was accurately retrieve