Comparing the effect of unfractionated heparin and low molecular weight heparin in preventing of deep vein thrombosis prophylaxis after craniotomy in patients with brain tumor

کرانیوتومی با توجه به مستعد کردن بیماران به بی حرکتی، پس از عمل عاملی خطرساز برای ایجاد ترومبوز وریدی محسوب می شود. هدف از مطالعه حاضر مقایسه اثر دو روش درمان رایج برای پیشگیری از ترومبوز وریدی شامل هپارین معمولی ((unfractionated و هپارین با وزن مولکولی کم (کلگزان) در پیشگیری از این عارضه است. روش بررسی: در این کارآزمایی بالینی تصادفی و دوسوکور که از سال 1387 تا 1389 در اصفهان انجام شد، تعداد 154 بیمار که به علت تومور مغزی مورد عمل جراحی کرانیوتومی قرار گرفته در دو گروه تقسیم و مورد مطالعه قرار گرفتند. در گروه اول از 48 ساعت بعد از عمل کلگزان به میزان mg/d 40 به صورت زیرجلدی تجویز شد. بیماران در گروه دوم از 48 ساعت بعد از عمل، هپارین unfractionated به میزان IU/12h 5000 به صورت زیرجلدی دریافت کردند و نتایج در نرم افزار با کمک آزمون های T-test و Chi-Square مورد تجزیه و تحلیل قرار گرفتند. یافته ها:در این مطالعه تفاوت آماری معنی داری از نظر سن، جنس و بیماری های زمینه ای بین دو گروه مشاهده نشد (05/0P>). طی مطالعه 14 مورد (2/18) ترومبوز وریدهای عمقی تحت بالینی در گروه هپارین و 3 مورد (9/3) در گروه کلگزان مشاهده شد (011/0=P). هیچکدام از بیماران در پیگیری پس از کرانیوتومی دچار خونریزی داخل مغزی نشدند. نتیجه گیری: نتایج مطالعه حاضر نشان می دهد که کلگزان نسبت به هپارین unfractionated در پیشگیری از ترومبوز ورید عمقی پس از کرانیوتومی در بیماران مبتلا به تومور مغزی مؤثرتر و ایمن تر است

Global burden of 369 diseases and injuries in 204 countries and territories, 1990–2019: a systematic analysis for the Global Burden of Disease Study 2019

Background: In an era of shifting global agendas and expanded emphasis on non-communicable diseases and injuries along with communicable diseases, sound evidence on trends by cause at the national level is essential. The Global Burden of Diseases, Injuries, and Risk Factors Study (GBD) provides a systematic scientific assessment of published, publicly available, and contributed data on incidence, prevalence, and mortality for a mutually exclusive and collectively exhaustive list of diseases and injuries. Methods: GBD estimates incidence, prevalence, mortality, years of life lost (YLLs), years lived with disability (YLDs), and disability-adjusted life-years (DALYs) due to 369 diseases and injuries, for two sexes, and for 204 countries and territories. Input data were extracted from censuses, household surveys, civil registration and vital statistics, disease registries, health service use, air pollution monitors, satellite imaging, disease notifications, and other sources. Cause-specific death rates and cause fractions were calculated using the Cause of Death Ensemble model and spatiotemporal Gaussian process regression. Cause-specific deaths were adjusted to match the total all-cause deaths calculated as part of the GBD population, fertility, and mortality estimates. Deaths were multiplied by standard life expectancy at each age to calculate YLLs. A Bayesian meta-regression modelling tool, DisMod-MR 2.1, was used to ensure consistency between incidence, prevalence, remission, excess mortality, and cause-specific mortality for most causes. Prevalence estimates were multiplied by disability weights for mutually exclusive sequelae of diseases and injuries to calculate YLDs. We considered results in the context of the Socio-demographic Index (SDI), a composite indicator of income per capita, years of schooling, and fertility rate in females younger than 25 years. Uncertainty intervals (UIs) were generated for every metric using the 25th and 975th ordered 1000 draw values of the posterior distribution. Findings: Global health has steadily improved over the past 30 years as measured by age-standardised DALY rates. After taking into account population growth and ageing, the absolute number of DALYs has remained stable. Since 2010, the pace of decline in global age-standardised DALY rates has accelerated in age groups younger than 50 years compared with the 1990–2010 time period, with the greatest annualised rate of decline occurring in the 0–9-year age group. Six infectious diseases were among the top ten causes of DALYs in children younger than 10 years in 2019: lower respiratory infections (ranked second), diarrhoeal diseases (third), malaria (fifth), meningitis (sixth), whooping cough (ninth), and sexually transmitted infections (which, in this age group, is fully accounted for by congenital syphilis; ranked tenth). In adolescents aged 10–24 years, three injury causes were among the top causes of DALYs: road injuries (ranked first), self-harm (third), and interpersonal violence (fifth). Five of the causes that were in the top ten for ages 10–24 years were also in the top ten in the 25–49-year age group: road injuries (ranked first), HIV/AIDS (second), low back pain (fourth), headache disorders (fifth), and depressive disorders (sixth). In 2019, ischaemic heart disease and stroke were the top-ranked causes of DALYs in both the 50–74-year and 75-years-and-older age groups. Since 1990, there has been a marked shift towards a greater proportion of burden due to YLDs from non-communicable diseases and injuries. In 2019, there were 11 countries where non-communicable disease and injury YLDs constituted more than half of all disease burden. Decreases in age-standardised DALY rates have accelerated over the past decade in countries at the lower end of the SDI range, while improvements have started to stagnate or even reverse in countries with higher SDI. Interpretation: As disability becomes an increasingly large component of disease burden and a larger component of health expenditure, greater research and developm nt investment is needed to identify new, more effective intervention strategies. With a rapidly ageing global population, the demands on health services to deal with disabling outcomes, which increase with age, will require policy makers to anticipate these changes. The mix of universal and more geographically specific influences on health reinforces the need for regular reporting on population health in detail and by underlying cause to help decision makers to identify success stories of disease control to emulate, as well as opportunities to improve. Funding: Bill & Melinda Gates Foundation. © 2020 The Author(s). Published by Elsevier Ltd. This is an Open Access article under the CC BY 4.0 licens

Global age-sex-specific fertility, mortality, healthy life expectancy (HALE), and population estimates in 204 countries and territories, 1950-2019 : a comprehensive demographic analysis for the Global Burden of Disease Study 2019

Background: Accurate and up-to-date assessment of demographic metrics is crucial for understanding a wide range of social, economic, and public health issues that affect populations worldwide. The Global Burden of Diseases, Injuries, and Risk Factors Study (GBD) 2019 produced updated and comprehensive demographic assessments of the key indicators of fertility, mortality, migration, and population for 204 countries and territories and selected subnational locations from 1950 to 2019. Methods: 8078 country-years of vital registration and sample registration data, 938 surveys, 349 censuses, and 238 other sources were identified and used to estimate age-specific fertility. Spatiotemporal Gaussian process regression (ST-GPR) was used to generate age-specific fertility rates for 5-year age groups between ages 15 and 49 years. With extensions to age groups 10–14 and 50–54 years, the total fertility rate (TFR) was then aggregated using the estimated age-specific fertility between ages 10 and 54 years. 7417 sources were used for under-5 mortality estimation and 7355 for adult mortality. ST-GPR was used to synthesise data sources after correction for known biases. Adult mortality was measured as the probability of death between ages 15 and 60 years based on vital registration, sample registration, and sibling histories, and was also estimated using ST-GPR. HIV-free life tables were then estimated using estimates of under-5 and adult mortality rates using a relational model life table system created for GBD, which closely tracks observed age-specific mortality rates from complete vital registration when available. Independent estimates of HIV-specific mortality generated by an epidemiological analysis of HIV prevalence surveys and antenatal clinic serosurveillance and other sources were incorporated into the estimates in countries with large epidemics. Annual and single-year age estimates of net migration and population for each country and territory were generated using a Bayesian hierarchical cohort component model that analysed estimated age-specific fertility and mortality rates along with 1250 censuses and 747 population registry years. We classified location-years into seven categories on the basis of the natural rate of increase in population (calculated by subtracting the crude death rate from the crude birth rate) and the net migration rate. We computed healthy life expectancy (HALE) using years lived with disability (YLDs) per capita, life tables, and standard demographic methods. Uncertainty was propagated throughout the demographic estimation process, including fertility, mortality, and population, with 1000 draw-level estimates produced for each metric. Findings: The global TFR decreased from 2·72 (95% uncertainty interval [UI] 2·66–2·79) in 2000 to 2·31 (2·17–2·46) in 2019. Global annual livebirths increased from 134·5 million (131·5–137·8) in 2000 to a peak of 139·6 million (133·0–146·9) in 2016. Global livebirths then declined to 135·3 million (127·2–144·1) in 2019. Of the 204 countries and territories included in this study, in 2019, 102 had a TFR lower than 2·1, which is considered a good approximation of replacement-level fertility. All countries in sub-Saharan Africa had TFRs above replacement level in 2019 and accounted for 27·1% (95% UI 26·4–27·8) of global livebirths. Global life expectancy at birth increased from 67·2 years (95% UI 66·8–67·6) in 2000 to 73·5 years (72·8–74·3) in 2019. The total number of deaths increased from 50·7 million (49·5–51·9) in 2000 to 56·5 million (53·7–59·2) in 2019. Under-5 deaths declined from 9·6 million (9·1–10·3) in 2000 to 5·0 million (4·3–6·0) in 2019. Global population increased by 25·7%, from 6·2 billion (6·0–6·3) in 2000 to 7·7 billion (7·5–8·0) in 2019. In 2019, 34 countries had negative natural rates of increase; in 17 of these, the population declined because immigration was not sufficient to counteract the negative rate of decline. Globally, HALE increased from 58·6 years (56·1–60·8) in 2000 to 63·5 years (60·8–66·1) in 2019. HALE increased in 202 of 204 countries and territories between 2000 and 2019

Tracking development assistance for health and for COVID-19: a review of development assistance, government, out-of-pocket, and other private spending on health for 204 countries and territories, 1990-2050

Background The rapid spread of COVID-19 renewed the focus on how health systems across the globe are financed, especially during public health emergencies. Development assistance is an important source of health financing in many low-income countries, yet little is known about how much of this funding was disbursed for COVID-19. We aimed to put development assistance for health for COVID-19 in the context of broader trends in global health financing, and to estimate total health spending from 1995 to 2050 and development assistance for COVID-19 in 2020. Methods We estimated domestic health spending and development assistance for health to generate total health-sector spending estimates for 204 countries and territories. We leveraged data from the WHO Global Health Expenditure Database to produce estimates of domestic health spending. To generate estimates for development assistance for health, we relied on project-level disbursement data from the major international development agencies' online databases and annual financial statements and reports for information on income sources. To adjust our estimates for 2020 to include disbursements related to COVID-19, we extracted project data on commitments and disbursements from a broader set of databases (because not all of the data sources used to estimate the historical series extend to 2020), including the UN Office of Humanitarian Assistance Financial Tracking Service and the International Aid Transparency Initiative. We reported all the historic and future spending estimates in inflation-adjusted 2020 US$, 2020 US$ per capita, purchasing-power parity-adjusted US$per capita, and as a proportion of gross domestic product. We used various models to generate future health spending to 2050. Findings In 2019, health spending globally reached$8. 8 trillion (95% uncertainty interval UI] 8.7-8.8) or $1132 (1119-1143) per person. Spending on health varied within and across income groups and geographical regions. Of this total,$40.4 billion (0.5%, 95% UI 0.5-0.5) was development assistance for health provided to low-income and middle-income countries, which made up 24.6% (UI 24.0-25.1) of total spending in low-income countries. We estimate that $54.8 billion in development assistance for health was disbursed in 2020. Of this,$13.7 billion was targeted toward the COVID-19 health response. $12.3 billion was newly committed and$1.4 billion was repurposed from existing health projects. $3.1 billion (22.4$2.4 billion (17.9%) was for supply chain and logistics. Only $714.4 million (7.7$1519 (1448-1591) per person in 2050, although spending across countries is expected to remain varied. Interpretation Global health spending is expected to continue to grow, but remain unequally distributed between countries. We estimate that development organisations substantially increased the amount of development assistance for health provided in 2020. Continued efforts are needed to raise sufficient resources to mitigate the pandemic for the most vulnerable, and to help curtail the pandemic for all. Copyright (C) 2021 The Author(s). Published by Elsevier Ltd

Data on the fluoride adsorption from aqueous solutions by metal-organic frameworks (ZIF-8 and Uio-66)

The variables examined were initial fluoride concentration, ZIF-8 and Uio-66 dosage, pH, and contact time. The residual concentration of fluoride was measured by a spectrophotometer. According to BET, the specific surface area of the ZIF-8 and Uio-66 was 1050 m2/g and 800 m2/g, respectively. Total pore volume and average pore diameter of the ZIF-8 and Uio-66 were 0.57 cm3/g, 0.45 cm3/g and 4.5 nm, 3.2 nm, respectively. The best pH for fluoride adsorption was neutral conditions. By increasing the ZIF-8 and Uio-66 dose, the fluoride uptake increased at first, but then decreased. Also, the maximum adsorption for ZIF-8 and Uio-66 was observed in adsorbent dose 0.2 and 0.6 g/L, respectively. The best model for describing kinetic and isotherms of fluoride adsorption were the pseudo-second-order model and Langmuir isotherm model, respectively. Based on the Langmuir model, the adsorption capacity of fluoride by ZIF-8 and Uio-66 was reported to be 25 mg/g and 20 mg/g, respectively. Keywords: Fluoride, Aqueous solution, Adsorption, Metal-organic framewor

Data on the alizarin red S adsorption from aqueous solutions on PAC, treated PAC, and PAC/γ≈Fe2O3

Three types of adsorbents of powdered activated carbon (PAC), treated PAC, and PAC/γ ≈ Fe2O3 nanocomposite were used. The adsorption experiments were performed in batch conditions. pHZPC of PAC/γ ≈ Fe2O3 was 6.7. As a result, at lower than pHZPC, acidic pH, the adsorption of alizarin red S on PAC/γ ≈ Fe2O3 was favourable. The maximum of alizarin red S adsorption of PAC, treated PAC, and PAC/γ ≈ Fe2O3 was 24.5 mg/g, 57.8 mg/g, and 112.56 mg/g, respectively. The models of Langmuir and pseudo-first-order were a fit model to describe the adsorption isotherm and the Kinetic, respectively. The PAC/γ ≈ Fe2O3 is a promising class of the adsorbents in the adsorption of various dyes from textile effluents. Keywords: Adsorption, Textile effluents, Dye, Aqueous solutions, Fe2O

Data on biosurfactant assisted removal of TNT from contaminated soil

Contamination of environment, especially soil, is in great concern and can cause health problems. Thus, remediation of these pollutants through environmentally friendly methods should be considered. The aim of this data was bioremediation of TNT from contaminated soil. Two plastic pans were used as bioreactor. In each pan, 3 kg of soil was used. Concentration of TNT in contaminated soil was 1000 mg/kg. Rhamnolipid in concentration of 60 mg/l was added to intended pan. Sampling was done in each two weeks. In order to assessment of TNT degradation, samples were analyzed with HPLC. The data showed that after 154 days of experiment, TNT removal in soil that amended with rhamnolipid was 73% and in experiment with no addition of rhamnolipid was 58%. Based on the obtained data rhamnolipid was effective in remediation of TNT contaminated soil. Keywords: TNT, Soil contamination, Rhamnolipid, Bioremediation, Soi