Plasma 17beta-estradiol and alkali-labile phosphoprotein levels in male and female Tench (Tinca tinca) in the Anzali and Amirkolayeh wetlands

Environmental pollutants are potentiate to disturb biological processes such as metabolism, growth and reproduction of aquatic organisms. These compounds are able to cause gonadal abnormalities, biased sex ratios and alteration in reproductive physiology in fish. The aim of this study was to examine plasma 17β-estradiol (E2) and alkali-labile phosphoprotein (ALP) levels in male and female Tench (Tinca tinca) from a polluted (the Anzali Wetland) and a non-polluted environments (the Amirkolayeh Wetland). Samples were collected over the maturation season of Tench between May and June 2017. The results revealed significant difference in mean ALP and E2 between genders in the polluted environment. However, the mean plasma ALP concentrations in male Tench of the polluted environment (39.46±1.02 µg/ml) was 45% of the average recorded in female (86.18±2.25 µg/ml) and was two times higher than the amount measured in males in the non-polluted environment (18.68±0.35 µg/ml). High concentrations of E2, were detected in the male samples from the Anzali Wetland. Mean plasma E2 concentrations for male in the Anzali Wetland was almost two times higher than male in the Amirkolayeh Wetland. The results indicate that the reproductive physiology of Tench was affected by contaminants found in the Anzali Wetland, a highly polluted area

Rolling up the pieces of a puzzle: A systematic review and meta-analysis of the prevalence of toxoplasmosis in Iran

Toxoplasmosis is a neglected parasitic disease with global distribution in warm-blooded vertebrates and high prevalence among different human societies. We contrived a systematic review and meta-analysis on the prevalence of toxoplasmosis in Iran. Following the general methodology recommended for systematic reviews and meta-analysis, four English and three Persian electronic databases were explored up to April 2016. Out of 105,139 examined samples of different hosts, the weighted overall prevalence was 37% (95% CI = 31–43). Due to the significant heterogeneity (I2 = 81.9%) the random-effects model was used. The pool estimated prevalence of toxoplasmosis in human intermediate hosts, animal intermediate hosts, and definitive hosts was 43% (95% CI = 38–47), 26 (95% CI = 17–35) and, 34% (95% CI = 22–46), respectively. Our results represent that regular inspection in food industries, improved screening programs using standard diagnostic assay as well as distinguishing toxoplasmosis condition in other zoonotic hosts are extremely recommended for better disease management in Iran.Keywords: Toxoplasma gondii, Prevalence, Iran, Systematic review, Meta-analysi

Reliability and Availability Improvement in Economic Data Grid Environment Based On Clustering Approach

Abstract - One of the important problems in grid environments is data replication in grid sites. Reliability and availability of data replication in some cases is considered low. To separate sites with high reliability and high availability of sites with low availability and low reliability, clustering can be used. In this study, the data grid dynamically evaluate and predict the condition of the sites. The reliability and availability of sites were calculated and it was used to make decisions to replicate data. With these calculations, we have information on the locations of users in grid with reliability and availability or cost commensurate with the value of the work they did. This information can be downloaded from users who are willing to send them data with suitable reliability and availability. Simulation results show that the addition of the two parameters, reliability and availability, assessment criteria have been improved in certain access patterns

Socio-Economic Status Inequity in Self Rated Health in Patients with Breast Cancer

AIM: We investigate the evaluation of socio-economic status (SES) inequality on self-rated health (SRH) at women with breast cancer. STUDY DESIGN: Cross-sectional study METHODS: The current study conducted on all 270 breast cancer patients that were admitted to one of the hospitals of Arak University Medical Sciences (Arak, Iran from April to July 2018) by census (using non-random sampling (accessible sampling). SES was calculated by asset-based questionnaire and Principle Component Analysis (PCA) was performed to estimate the families' SES. Concentration Index (C) and Curve (CC) was used to measure SES inequality in SRH. The data were analysed with Stata software. RESULTS: The number of persons with good SRH by the level of SES was 165 (61.1%) and with poor SRH was 105 (38.9%). The number of persons with good SRH in comparison to same-aged people by level of SES was 135 (50%) and with poor SRH was 135 (50%). Concentration index of SRH in all level of SES was 0.061 (SE = 0.03). Also, Concentration index for SRH in comparison to same-aged people at different levels of SES was -0.044 (SE = 0.03). CONCLUSION: The results of this study showed that there is inequality in SRH in a patient with breast cancer of the richest level of SES

Highly catalytically active CeO2-x-based heterojunction nanostructures with mixed micro/meso-porous architectures

Achieving high densities of accessible active sites in catalysts, which depend principally on the architecture of nanostructures, is critical to obtain enhanced performance. The present work introduces a template-free, high-yield, and flexible approach to fabricate 3D, mesoporous, CeO2-x nanostructures in centimeter-scale that are comprised of extremely thin holey 2D nanosheets. The method involves conversion of a stacked, 2D, Ce-based coordination polymer by controlling the removal kinetics of organic species. The resultant polycrystalline 2D-3D CeO2-x exhibits a large density of defects as well as outstanding surface areas of 251 m2 g-1. This mesoporous nanomaterial yields superior CO conversion performance (T90% = 148°C). Further improvements in catalysis were attained by synthesis CeO2-x -based transition metal oxides (TMOs) hetero-nanostructures, for which structural analyses and first principles simulations revealed active sites associated with the TMOs. This versatile fabrication technique delivers new pathways to engineer nanostructures and advance their functionalities for catalysis.Peer ReviewedPostprint (author's final draft

Socioeconomic Inequalities in Metastasis, Recurrence, Stage and Grade of Breast Cancer: A Hospital-based Retrospective Cohort Study

Background: This study aims to estimate the Socio-Economic Status (SES) inequality on the metastasis, recurrence, stage and grade in Breast Cancer (BC).Methods: This retrospective cohort study conducted on 411 BC patients in Arak, Iran. Asset-based questionnaire used to estimate the household SES. For calculate of SES inequality was used from Concentration Index (C). Moreover for investigate the association between recurrence and metastasis with other variables were used from multilevel logistic regression and analysis of variance were used to investigate the relationship between SES and other variables. The data were analyzed with Stata (v.13) software.Results: Results of analysis of variance showed statistical significant relationship between SES with, insurance, surgery, grade, stage, recurrence and metastasis (p-value < 0.05). Moreover the Odds Ratio (OR) were significant of recurrence with age, academic level of education, supplementary insurance history of BC in first-degree relatives, stage and grade, also, metastasis with age of >80 years, insurance, supplementary insurance, history of BC in first-degree relatives, chemotherapy, radiotherapy, stage and grade four. The total C index obtained 0.015 (0.002, 0.026), 0.011 (0.003, 0.031), -0.014 (-0.034, -0.001) and -0.042 (-0.061, -0.002) for metastasis, recurrence, stage and grade of BC respectively.Conclusion: Our results showed evidence of inequality in the metastasis, recurrence, stage and grade in BC patients

Mortality and disability-adjusted life years in North Africa and Middle East attributed to kidney dysfunction : a systematic analysis for the Global Burden of Disease Study 2019

The authors would like to thank the hard work of the staff of the Institute for Health Metrics and Evaluation (IHME) for providing the best possible epidemiologic estimation of diseases in almost all regions and countries of the world, trying to reduce and eliminate poverty of knowledge and information about the diseases globally. Also, the core team authors sincerely thank all the collaborators of the GBD 2019 study who contributed to this study by providing data or a precise review of the manuscript. Publisher Copyright: © The Author(s) 2023. Published by Oxford University Press on behalf of the ERA. This is an Open Access article distributed under the terms of the Creative Commons Attribution License (https://creativecommons.org/licenses/by/4.0/), which permits unrestricted reuse, distribution, and reproduction in any medium, provided the original work is properly cited.Peer reviewe

Population and fertility by age and sex for 195 countries and territories, 1950–2017: a systematic analysis for the Global Burden of Disease Study 2017

Background Population estimates underpin demographic and epidemiological research and are used to track progress on numerous international indicators of health and development. To date, internationally available estimates of population and fertility, although useful, have not been produced with transparent and replicable methods and do not use standardised estimates of mortality. We present single-calendar year and single-year of age estimates of fertility and population by sex with standardised and replicable methods. Methods We estimated population in 195 locations by single year of age and single calendar year from 1950 to 2017 with standardised and replicable methods. We based the estimates on the demographic balancing equation, with inputs of fertility, mortality, population, and migration data. Fertility data came from 7817 location-years of vital registration data, 429 surveys reporting complete birth histories, and 977 surveys and censuses reporting summary birth histories. We estimated age-specific fertility rates (ASFRs; the annual number of livebirths to women of a specified age group per 1000 women in that age group) by use of spatiotemporal Gaussian process regression and used the ASFRs to estimate total fertility rates (TFRs; the average number of children a woman would bear if she survived through the end of the reproductive age span [age 10–54 years] and experienced at each age a particular set of ASFRs observed in the year of interest). Because of sparse data, fertility at ages 10–14 years and 50–54 years was estimated from data on fertility in women aged 15–19 years and 45–49 years, through use of linear regression. Age-specific mortality data came from the Global Burden of Diseases, Injuries, and Risk Factors Study (GBD) 2017 estimates. Data on population came from 1257 censuses and 761 population registry location-years and were adjusted for underenumeration and age misreporting with standard demographic methods. Migration was estimated with the GBD Bayesian demographic balancing model, after incorporating information about refugee migration into the model prior. Final population estimates used the cohort-component method of population projection, with inputs of fertility, mortality, and migration data. Population uncertainty was estimated by use of out-of-sample predictive validity testing. With these data, we estimated the trends in population by age and sex and in fertility by age between 1950 and 2017 in 195 countries and territories.Background Population estimates underpin demographic and epidemiological research and are used to track progress on numerous international indicators of health and development. To date, internationally available estimates of population and fertility, although useful, have not been produced with transparent and replicable methods and do not use standardised estimates of mortality. We present single-calendar year and single-year of age estimates of fertility and population by sex with standardised and replicable methods. Methods We estimated population in 195 locations by single year of age and single calendar year from 1950 to 2017 with standardised and replicable methods. We based the estimates on the demographic balancing equation, with inputs of fertility, mortality, population, and migration data. Fertility data came from 7817 location-years of vital registration data, 429 surveys reporting complete birth histories, and 977 surveys and censuses reporting summary birth histories. We estimated age-specific fertility rates (ASFRs; the annual number of livebirths to women of a specified age group per 1000 women in that age group) by use of spatiotemporal Gaussian process regression and used the ASFRs to estimate total fertility rates (TFRs; the average number of children a woman would bear if she survived through the end of the reproductive age span [age 10–54 years] and experienced at each age a particular set of ASFRs observed in the year of interest). Because of sparse data, fertility at ages 10–14 years and 50–54 years was estimated from data on fertility in women aged 15–19 years and 45–49 years, through use of linear regression. Age-specific mortality data came from the Global Burden of Diseases, Injuries, and Risk Factors Study (GBD) 2017 estimates. Data on population came from 1257 censuses and 761 population registry location-years and were adjusted for underenumeration and age misreporting with standard demographic methods. Migration was estimated with the GBD Bayesian demographic balancing model, after incorporating information about refugee migration into the model prior. Final population estimates used the cohort-component method of population projection, with inputs of fertility, mortality, and migration data. Population uncertainty was estimated by use of out-of-sample predictive validity testing. With these data, we estimated the trends in population by age and sex and in fertility by age between 1950 and 2017 in 195 countries and territories

Global, regional, and national age-sex-specific mortality and life expectancy, 1950–2017: a systematic analysis for the Global Burden of Disease Study 2017

Background Assessments of age-specific mortality and life expectancy have been done by the UN Population Division, Department of Economics and Social Affairs (UNPOP), the United States Census Bureau, WHO, and as part of previous iterations of the Global Burden of Diseases, Injuries, and Risk Factors Study (GBD). Previous iterations of the GBD used population estimates from UNPOP, which were not derived in a way that was internally consistent with the estimates of the numbers of deaths in the GBD. The present iteration of the GBD, GBD 2017, improves on previous assessments and provides timely estimates of the mortality experience of populations globally. Methods The GBD uses all available data to produce estimates of mortality rates between 1950 and 2017 for 23 age groups, both sexes, and 918 locations, including 195 countries and territories and subnational locations for 16 countries. Data used include vital registration systems, sample registration systems, household surveys (complete birth histories, summary birth histories, sibling histories), censuses (summary birth histories, household deaths), and Demographic Surveillance Sites. In total, this analysis used 8259 data sources. Estimates of the probability of death between birth and the age of 5 years and between ages 15 and 60 years are generated and then input into a model life table system to produce complete life tables for all locations and years. Fatal discontinuities and mortality due to HIV/AIDS are analysed separately and then incorporated into the estimation. We analyse the relationship between age-specific mortality and development status using the Socio-demographic Index, a composite measure based on fertility under the age of 25 years, education, and income. There are four main methodological improvements in GBD 2017 compared with GBD 2016: 622 additional data sources have been incorporated; new estimates of population, generated by the GBD study, are used; statistical methods used in different components of the analysis have been further standardised and improved; and the analysis has been extended backwards in time by two decades to start in 1950.Background Assessments of age-specific mortality and life expectancy have been done by the UN Population Division, Department of Economics and Social Affairs (UNPOP), the United States Census Bureau, WHO, and as part of previous iterations of the Global Burden of Diseases, Injuries, and Risk Factors Study (GBD). Previous iterations of the GBD used population estimates from UNPOP, which were not derived in a way that was internally consistent with the estimates of the numbers of deaths in the GBD. The present iteration of the GBD, GBD 2017, improves on previous assessments and provides timely estimates of the mortality experience of populations globally. Methods The GBD uses all available data to produce estimates of mortality rates between 1950 and 2017 for 23 age groups, both sexes, and 918 locations, including 195 countries and territories and subnational locations for 16 countries. Data used include vital registration systems, sample registration systems, household surveys (complete birth histories, summary birth histories, sibling histories), censuses (summary birth histories, household deaths), and Demographic Surveillance Sites. In total, this analysis used 8259 data sources. Estimates of the probability of death between birth and the age of 5 years and between ages 15 and 60 years are generated and then input into a model life table system to produce complete life tables for all locations and years. Fatal discontinuities and mortality due to HIV/AIDS are analysed separately and then incorporated into the estimation. We analyse the relationship between age-specific mortality and development status using the Socio-demographic Index, a composite measure based on fertility under the age of 25 years, education, and income. There are four main methodological improvements in GBD 2017 compared with GBD 2016: 622 additional data sources have been incorporated; new estimates of population, generated by the GBD study, are used; statistical methods used in different components of the analysis have been further standardised and improved; and the analysis has been extended backwards in time by two decades to start in 1950

Measuring progress from 1990 to 2017 and projecting attainment to 2030 of the health-related Sustainable Development Goals for 195 countries and territories: a systematic analysis for the Global Burden of Disease Study 2017

Background Efforts to establish the 2015 baseline and monitor early implementation of the UN Sustainable Development Goals (SDGs) highlight both great potential for and threats to improving health by 2030. To fully deliver on the SDG aim of “leaving no one behind”, it is increasingly important to examine the health-related SDGs beyond national-level estimates. As part of the Global Burden of Diseases, Injuries, and Risk Factors Study 2017 (GBD 2017), we measured progress on 41 of 52 health-related SDG indicators and estimated the health-related SDG index for 195 countries and territories for the period 1990–2017, projected indicators to 2030, and analysed global attainment. Methods We measured progress on 41 health-related SDG indicators from 1990 to 2017, an increase of four indicators since GBD 2016 (new indicators were health worker density, sexual violence by non-intimate partners, population census status, and prevalence of physical and sexual violence [reported separately]). We also improved the measurement of several previously reported indicators. We constructed national-level estimates and, for a subset of health-related SDGs, examined indicator-level differences by sex and Socio-demographic Index (SDI) quintile. We also did subnational assessments of performance for selected countries. To construct the health-related SDG index, we transformed the value for each indicator on a scale of 0–100, with 0 as the 2·5th percentile and 100 as the 97·5th percentile of 1000 draws calculated from 1990 to 2030, and took the geometric mean of the scaled indicators by target. To generate projections through 2030, we used a forecasting framework that drew estimates from the broader GBD study and used weighted averages of indicator-specific and country-specific annualised rates of change from 1990 to 2017 to inform future estimates. We assessed attainment of indicators with defined targets in two ways: first, using mean values projected for 2030, and then using the probability of attainment in 2030 calculated from 1000 draws. We also did a global attainment analysis of the feasibility of attaining SDG targets on the basis of past trends. Using 2015 global averages of indicators with defined SDG targets, we calculated the global annualised rates of change required from 2015 to 2030 to meet these targets, and then identified in what percentiles the required global annualised rates of change fell in the distribution of country-level rates of change from 1990 to 2015. We took the mean of these global percentile values across indicators and applied the past rate of change at this mean global percentile to all health-related SDG indicators, irrespective of target definition, to estimate the equivalent 2030 global average value and percentage change from 2015 to 2030 for each indicator.Background Efforts to establish the 2015 baseline and monitor early implementation of the UN Sustainable Development Goals (SDGs) highlight both great potential for and threats to improving health by 2030. To fully deliver on the SDG aim of “leaving no one behind”, it is increasingly important to examine the health-related SDGs beyond national-level estimates. As part of the Global Burden of Diseases, Injuries, and Risk Factors Study 2017 (GBD 2017), we measured progress on 41 of 52 health-related SDG indicators and estimated the health-related SDG index for 195 countries and territories for the period 1990–2017, projected indicators to 2030, and analysed global attainment. Methods We measured progress on 41 health-related SDG indicators from 1990 to 2017, an increase of four indicators since GBD 2016 (new indicators were health worker density, sexual violence by non-intimate partners, population census status, and prevalence of physical and sexual violence [reported separately]). We also improved the measurement of several previously reported indicators. We constructed national-level estimates and, for a subset of health-related SDGs, examined indicator-level differences by sex and Socio-demographic Index (SDI) quintile. We also did subnational assessments of performance for selected countries. To construct the health-related SDG index, we transformed the value for each indicator on a scale of 0–100, with 0 as the 2·5th percentile and 100 as the 97·5th percentile of 1000 draws calculated from 1990 to 2030, and took the geometric mean of the scaled indicators by target. To generate projections through 2030, we used a forecasting framework that drew estimates from the broader GBD study and used weighted averages of indicator-specific and country-specific annualised rates of change from 1990 to 2017 to inform future estimates. We assessed attainment of indicators with defined targets in two ways: first, using mean values projected for 2030, and then using the probability of attainment in 2030 calculated from 1000 draws. We also did a global attainment analysis of the feasibility of attaining SDG targets on the basis of past trends. Using 2015 global averages of indicators with defined SDG targets, we calculated the global annualised rates of change required from 2015 to 2030 to meet these targets, and then identified in what percentiles the required global annualised rates of change fell in the distribution of country-level rates of change from 1990 to 2015. We took the mean of these global percentile values across indicators and applied the past rate of change at this mean global percentile to all health-related SDG indicators, irrespective of target definition, to estimate the equivalent 2030 global average value and percentage change from 2015 to 2030 for each indicator