Global, regional, and national comparative risk assessment of 79 behavioural, environmental and occupational, and metabolic risks or clusters of risks, 1990-2015: a systematic analysis for the Global Burden of Disease Study 2015

Forouzanfar MH, Afshin A, Alexander LT, et al. Global, regional, and national comparative risk assessment of 79 behavioural, environmental and occupational, and metabolic risks or clusters of risks, 1990-2015: a systematic analysis for the Global Burden of Disease Study 2015. LANCET. 2016;388(10053):1659-1724.Background The Global Burden of Diseases, Injuries, and Risk Factors Study 2015 provides an up-to-date synthesis of the evidence for risk factor exposure and the attributable burden of disease. By providing national and subnational assessments spanning the past 25 years, this study can inform debates on the importance of addressing risks in context. Methods We used the comparative risk assessment framework developed for previous iterations of the Global Burden of Disease Study to estimate attributable deaths, disability-adjusted life-years (DALYs), and trends in exposure by age group, sex, year, and geography for 79 behavioural, environmental and occupational, and metabolic risks or clusters of risks from 1990 to 2015. This study included 388 risk-outcome pairs that met World Cancer Research Fund-defined criteria for convincing or probable evidence. We extracted relative risk and exposure estimates from randomised controlled trials, cohorts, pooled cohorts, household surveys, census data, satellite data, and other sources. We used statistical models to pool data, adjust for bias, and incorporate covariates. We developed a metric that allows comparisons of exposure across risk factors-the summary exposure value. Using the counterfactual scenario of theoretical minimum risk level, we estimated the portion of deaths and DALYs that could be attributed to a given risk. We decomposed trends in attributable burden into contributions from population growth, population age structure, risk exposure, and risk-deleted cause-specific DALY rates. We characterised risk exposure in relation to a Socio-demographic Index (SDI). Findings Between 1990 and 2015, global exposure to unsafe sanitation, household air pollution, childhood underweight, childhood stunting, and smoking each decreased by more than 25%. Global exposure for several occupational risks, high body-mass index (BMI), and drug use increased by more than 25% over the same period. All risks jointly evaluated in 2015 accounted for 57.8% (95% CI 56.6-58.8) of global deaths and 41.2% (39.8-42.8) of DALYs. In 2015, the ten largest contributors to global DALYs among Level 3 risks were high systolic blood pressure (211.8 million [192.7 million to 231.1 million] global DALYs), smoking (148.6 million [134.2 million to 163.1 million]), high fasting plasma glucose (143.1 million [125.1 million to 163.5 million]), high BMI (120.1 million [83.8 million to 158.4 million]), childhood undernutrition (113.3 million [103.9 million to 123.4 million]), ambient particulate matter (103.1 million [90.8 million to 115.1 million]), high total cholesterol (88.7 million [74.6 million to 105.7 million]), household air pollution (85.6 million [66.7 million to 106.1 million]), alcohol use (85.0 million [77.2 million to 93.0 million]), and diets high in sodium (83.0 million [49.3 million to 127.5 million]). From 1990 to 2015, attributable DALYs declined for micronutrient deficiencies, childhood undernutrition, unsafe sanitation and water, and household air pollution; reductions in risk-deleted DALY rates rather than reductions in exposure drove these declines. Rising exposure contributed to notable increases in attributable DALYs from high BMI, high fasting plasma glucose, occupational carcinogens, and drug use. Environmental risks and childhood undernutrition declined steadily with SDI; low physical activity, high BMI, and high fasting plasma glucose increased with SDI. In 119 countries, metabolic risks, such as high BMI and fasting plasma glucose, contributed the most attributable DALYs in 2015. Regionally, smoking still ranked among the leading five risk factors for attributable DALYs in 109 countries; childhood underweight and unsafe sex remained primary drivers of early death and disability in much of sub-Saharan Africa. Interpretation Declines in some key environmental risks have contributed to declines in critical infectious diseases. Some risks appear to be invariant to SDI. Increasing risks, including high BMI, high fasting plasma glucose, drug use, and some occupational exposures, contribute to rising burden from some conditions, but also provide opportunities for intervention. Some highly preventable risks, such as smoking, remain major causes of attributable DALYs, even as exposure is declining. Public policy makers need to pay attention to the risks that are increasingly major contributors to global burden. Copyright (C) The Author(s). Published by Elsevier Ltd

Evaluation Of Electromagnetic Performance Of Emerging Failures In Electrical Machines Using Computational Simulation

Common failures in industrial induction motors equipped with squirrel cage rotor are examined in this paper. These failures are: broken rotor bars and short circuit in the inter-turn winding. The broken rotor bars failure was simulated with two consecutive broken bars in order to see how the magnetic flux density is affected. The inter-turn short circuit was simulated with 40% reduction of winding coil in an inter-turn short circuit, for one of the phases. The waveform of the flux density was plotted directly, while the analysis of the harmonic distribution and its relation to the electrical failures was performed in the Fourier domain. The proposed methodology allows to clearly identify the fault conditions analyzed. Comparative test of the real failure was performed in order to verify the results obtained from the simulation; good agreement between simulation and experimental measurements was obtained

Digital Processing Of Thermographic Images For Medical Applications

Breast cancer is the second leading cause of death in women worldwide with an average probability for a woman to develop breast cancer in her life of about 12%. Among the large variety of medical assessment techniques, thermography has attracted attention in applications related to detection and diagnosis due to its capability to provide valuable information on the physiological variations typical of early stages in cancer development thus making possible to diagnose patients in early stages so more thorough examinations can be done in proper time and manner. This paper presents a digital processing approach that allows identification and subsequent isolation of the region of interest in thermograms based texture analysis of the image. This algorithm was tested on case studies thermograms exhibiting different types of cancer and the results showed successful identification and extraction of the region of interest in all cases. Results are presented with different types of cancer in men and women and different image angles showing the robustness of the proposed method

Parkinson\u27S Disease: Improved Diagnosis Using Image Processing

An intensity-based texture segmentation approach for the detection of regions with abnormal texture characteristics in magnetic resonance imaging is presented. Our algorithm is tested over several images taken from The Parkinson\u27s Progression Markers Initiative (PPMI-database), and the results suggest that this approach is suitable for the successful identification and extraction of regions of interest whose properties can be potentially related to signature features of Parkinson disease

Biosensing Using Long-Range Surface Plasmon Structures

We report a parametric study of a long-range plasmon waveguide for the modal profiles, effective index and propagation losses as a function of the metal layer thickness and the variations in the refraction index of the upper cladding. Such device can be used as an optical biosensor. All calculations are performed using COMSOL Multiphysics, and the amplitude- and phase- responses of the device are obtained from the changes in the real and imaginary part of the effective index of the plasmon mode, respectively

Applications of Artificial Intelligence in the Classification of Magnetic Resonance Images: Advances and Perspectives

This chapter examines the advances and perspectives of the applications of artificial intelligence (AI) in the classification of magnetic resonance (MR) images. It focuses on the development of AI-based automatic classification models that have achieved competitive results compared to the state-of-the-art. Accurate and efficient classification of MR images is essential for medical diagnosis but can be challenging due to the complexity and variability of the data. AI offers tools and techniques that can effectively address these challenges. The chapter first addresses the fundamentals of artificial intelligence applied to the classification of medical images, including machine learning techniques and convolutional neural networks. Here, recent advances in the use of AI to classify MRI images in various clinical applications, such as brain tumor detection, are explored. Additionally, advantages and challenges associated with implementing AI models in clinical settings are discussed, such as the interpretability of results and integration with existing radiology systems. Prospects for AI in MR image classification are also highlighted, including the combination of multiple imaging modalities and the use of more advanced AI approaches such as reinforcement learning and model generation

Fabrication Of Micro-Optical Magnetic Sensor

In this work, we report the micro fabrication of several types of resonant micro magnetic field sensors where we can use an optical technique to read the magnetic variations (MOEMS). The fabrication process we have used, is a simple process in a SOI wafer with 6 μm of active layer and 5 μm of sacrificial SiO2

Fast and accurate cell tracking by a novel optical-digital hybrid method

An innovative methodology to detect and track cells using microscope images enhanced by optical cross-correlation techniques is proposed in this paper. In order to increase the tracking sensibility, image pre-processing has been implemented as a morphological operator on the microscope image. Results show that the pre-processing process allows for additional frames of cell tracking, therefore increasing its robustness. The proposed methodology can be used in analyzing different problems such as mitosis, cell collisions, and cell overlapping, ultimately designed to identify and treat illnesses and malignancies. © Springer Science+Business Media New York 2013.SCOPUS: ar.jinfo:eu-repo/semantics/publishe