The salicylic acid effect on the Salvia officianlis L. sugar, protein and proline contents under salinity (NaCl) stress

Plant growth is impressed by biotic and abiotic stress inversely. There are many reports about proteins change level in salinity stress. Leaves fill up more soluble sugar of glucose, fructose and proline with treatment of salicylic acid. In this research, Salivia officialis seeds planted in pots containing perlite were put in a growth chamber under controlled conditions of 27 ±2 0C and 23 ±2 0C temperature, 14h lightness and 10h darkness; NaCl concentration of 0,4,8,12 ds/m and salicylic acid concentration of 0,1,2,4 mM were used in the form of factorial experiment in a complete randomized design (CRD). The results demonstrated that increasing of proline and sugars due to osmotic slope in plants lead to increasing of tolerance against dehydrations of leave content and acceleration of plant developments in stress conditions

Coagulation Disorder following Red Clover (Trifolium Pratense) Misuse: a Case Report

Introduction: An increasing variety of alternative health care products and supplements known as over-the-counter (OTC) or non-prescription herbal medicines are taken by patients for different reasons. Unfortunately, these self-prescribed remedies have many food and drug interactions and unknown adverse effects and can lead to some important consequences. Case presentation: Here a case of bleeding disorder in a 28-year-old woman taking red clover is reported. She had no history of warfarin use, but warfarin was detected in her blood serum analysis. Conclusion: This agent is a source of natural coumarin and can cause an increase of international normalized ratio (INR) and bleeding. It is important that prescribers be alert to the possible disadvantage of herbal remedies and also probable herb-drug and herb-food interactions

Behaviors of Human T cells in SARS-CoV-2 Infection: Lessons and Tips

Cell-mediated immunity (CMI) is crucial in controlling the highly aggressive and progressive SARS-CoV-2 infection. Despite extensive researches on severe COVID-19 infection, the etiology and/or mechanisms of lymphopenia, decreased T cell-mediated responses in patients, cytokine release storms (CRS), and enhanced pro-inflammatory mediators are not fully understood. Several T cell subpopulations, including innate-like lymphocytes (ILLs) and conventional T cells, are involved in COVID-19 infection; however, their contribution to immunity and complications remains to be more elucidated. CD16+ T cells are among the effective players in the development of T helper1 (Th1) responses in COVID-19 infection, while their robust cytolytic properties contribute to lung tissue damage. While CD56-CD16bright NK cells play a protective role, natural killer T (NKT) cells, mucosal-associated invariant T (MAIT) cells, and γδ T cells and their roles in COVID-19 require further investigation. The involvement of the other T cell subsets, such as Th17, along with neutrophils, adds to the complexity of the situation. In this review, we presented and discussed the findings of recent studies on T cell responses and the contribution of each type of immune cells to COVID-19

Investigating the Trend of Dust Changes in The Eastern Half of Iran

IntroductionThe impact of dust storms on the ecosystem, human health, and economy is significant in the affected areas. In arid and semi-arid regions, dust is a natural occurrence that covers about one third of the earth's surface. This phenomenon is caused by wind erosion and factors such as the size of soil particles and their adhesion force, surface roughness, weather conditions such as long-term droughts cause it to intensify. To understand the impact of the dust phenomenon on humans and the environment, it is essential to evaluate the spatial and temporal distribution of dust and its changes and effects. Numerous studies have been carried out in the field of evaluating dust changes using different methods. For example, using CMIP5 models, synoptic fog systems are predicted to increase during the 21st century in the Middle East. In addition, using AOD obtained from MODIS and MERRA-2 sensors, researchers showed a significant upward trend in dust changes from 2000 to 2010. A significant upward trend was shown in Iran's winter AOD values during the period 2000 to 2010, and a decreasing trend during the period 2010 to 2018. A point can be spotted by examining various studies in the Middle East and Iran that evaluate the spatio-temporal changes of dust. Statistical tests of time series study, such as Mann-Kendall spatially and pixel by pixel, have been used in limited research to evaluate the trend of dust changes. In Iran, there is a research gap in not using spatial and pixel-by-pixel statistical tests to evaluate the trend of dust changes, as stated. This research aimed to provide a solution and address the problem by analyzing the spatial and temporal changes of dust using the AOD index in the eastern half of the country. Material and Methods In this research, in order to evaluate the temporal-spatial changes of dust, the AOD data of the blue band (470 nm) of the MCD19A2 product of the MODIS sensor was used. AOD parameter is known as one of the most key factors in studying the climatic effects of aerosols and atmospheric pollution. In order to extract AOD data, monthly data from 2001 to 2022 were obtained in the Google Earth Engine system by averaging the daily AOD data. Over a 22-year period, the average of each month was calculated. The months that had the highest average AOD values were chosen and their changes were evaluated. In this research, the Mann-Kendall test was used to evaluate the change process. Menkendall's ZM coefficient was calculated for months in the Earth Trend Modeler (ETM) of the TerrSet software to achieve this. In the next step, the intensity of monthly AOD changes per time unit was calculated for 22 years in selected months. To simulate the process of changes, linear regression analysis can be utilized for this purpose. This method is used to determine the linear relationship between all the data of a dependent variable and the corresponding data of the independent index. If the slope is higher than zero, the dependent variable will change in the same direction as the independent variable. The dependent variable changes in the opposite direction of the independent variable if the slope is smaller than zero. The steeper the slope of changes, the greater the impact of the independent variable on the dependent variable. The Earth Trend Modeler (ETM) of TerrSet software also carried out this step. Results and DiscussionBased on the evaluation of the monthly average AOD changes in the studied area, the trend and intensity of AOD changes from 2001 to 2022 were assessed in April, May, June, and July. In most areas of the studied area, AOD is increasing with a probability of more than 70%, and the intensity of changes is mostly high and very high in April. It can be concluded that AOD is experiencing a strong increase in April. This is despite the fact that in May, June and July, respectively, a considerable part of the western half, northern half and eastern half is increasing with different intensities with a probability of more than 70%. It can be concluded that the trend and intensity of AOD changes in the above-mentioned months follow a different spatial pattern. The dispersion of dust production centers inside and outside Iran, and the local and regional synoptic conditions governing dust production centers is the cause of changes in the spatio-temporal patterns of dust storms. The unprincipled extraction of water resources by humans, land degradation, soil moisture reduction, and the loss of vegetation due to climate change all affect these factors in turn. The results showed that the monitoring of monthly average AOD changes can help to identify new hotspots and evaluate the results of wind and dust erosion control and management activities. Therefore, it can be suggested that a system based on remote sensing must be designed and presented to monitor dust changes, so that the management of the dust phenomenon in Iran becomes more. We need to pay attention to the factors that influence these changes and evaluate their impact on the dust phenomenon.  On the other hand, by modeling the environmental factors affecting on the trend of dust changes in each region by using methods such as dust evaluation, it is possible to determine the role of each factor and the most important factor affecting the trend of dust changes in each region

Prevalence and Risk Factors of Asthma and Allergic Diseases in Primary Schoolchildren Living in Bushehr, Iran: Phase I, III ISAAC Protocol

Asthma and allergic diseases present a major health burden. Information on the prevalence of these diseases indicates that these diseases are increasing in various parts of the world. It was hoped that this study would be helpful to health system policy-makers in planning allergy prevention programs in the region. The prevalence of asthma and allergic diseases and relation between the various risk factors involved were assessed among schoolchildren in the city of Bushehr, Iran. The ISAAC Phase I and III questionnaires were completed by parents of 1280 children aged 6-7 years and self-completed by 1115 students aged 13-14 years. The prevalence of atopic eczema, allergic rhinitis and asthma among 6-7 year-old students were 12.1%, 11.8% and 6.7%, respectively. While, the prevalence of these diseases among 13-14 year-old students were found to be 19%, 30% and 7.6%, respectively. There was an association between asthma and allergic rhinitis as well as eczema (p<0.05). Consumption of fast food as a risk factor was significantly associated with asthma (p=0.03). The prevalence of asthma and allergic diseases was high among schoolchildren in the city of Bushehr, Iran. Also an association was observed between the fast food consumption and asthma. Keywords: Allergic rhinitis; Asthma, Atopic eczema; Children; ISAAC; Prevalenc

Application of artificial intelligence techniques for automated detection of myocardial infarction: A review

Myocardial infarction (MI) results in heart muscle injury due to receiving insufficient blood flow. MI is the most common cause of mortality in middle-aged and elderly individuals around the world. To diagnose MI, clinicians need to interpret electrocardiography (ECG) signals, which requires expertise and is subject to observer bias. Artificial intelligence-based methods can be utilized to screen for or diagnose MI automatically using ECG signals. In this work, we conducted a comprehensive assessment of artificial intelligence-based approaches for MI detection based on ECG as well as other biophysical signals, including machine learning (ML) and deep learning (DL) models. The performance of traditional ML methods relies on handcrafted features and manual selection of ECG signals, whereas DL models can automate these tasks. The review observed that deep convolutional neural networks (DCNNs) yielded excellent classification performance for MI diagnosis, which explains why they have become prevalent in recent years. To our knowledge, this is the first comprehensive survey of artificial intelligence techniques employed for MI diagnosis using ECG and other biophysical signals.Comment: 16 pages, 8 figure

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. Findings: From 1950 to 2017, TFRs decreased by 49\ub74% (95% uncertainty interval [UI] 46\ub74–52\ub70). The TFR decreased from 4\ub77 livebirths (4\ub75–4\ub79) to 2\ub74 livebirths (2\ub72–2\ub75), and the ASFR of mothers aged 10–19 years decreased from 37 livebirths (34–40) to 22 livebirths (19–24) per 1000 women. Despite reductions in the TFR, the global population has been increasing by an average of 83\ub78 million people per year since 1985. The global population increased by 197\ub72% (193\ub73–200\ub78) since 1950, from 2\ub76 billion (2\ub75–2\ub76) to 7\ub76 billion (7\ub74–7\ub79) people in 2017; much of this increase was in the proportion of the global population in south Asia and sub-Saharan Africa. The global annual rate of population growth increased between 1950 and 1964, when it peaked at 2\ub70%; this rate then remained nearly constant until 1970 and then decreased to 1\ub71% in 2017. Population growth rates in the southeast Asia, east Asia, and Oceania GBD super-region decreased from 2\ub75% in 1963 to 0\ub77% in 2017, whereas in sub-Saharan Africa, population growth rates were almost at the highest reported levels ever in 2017, when they were at 2\ub77%. The global average age increased from 26\ub76 years in 1950 to 32\ub71 years in 2017, and the proportion of the population that is of working age (age 15–64 years) increased from 59\ub79% to 65\ub73%. At the national level, the TFR decreased in all countries and territories between 1950 and 2017; in 2017, TFRs ranged from a low of 1\ub70 livebirths (95% UI 0\ub79–1\ub72) in Cyprus to a high of 7\ub71 livebirths (6\ub78–7\ub74) in Niger. The TFR under age 25 years (TFU25; number of livebirths expected by age 25 years for a hypothetical woman who survived the age group and was exposed to current ASFRs) in 2017 ranged from 0\ub708 livebirths (0\ub707–0\ub709) in South Korea to 2\ub74 livebirths (2\ub72–2\ub76) in Niger, and the TFR over age 30 years (TFO30; number of livebirths expected for a hypothetical woman ageing from 30 to 54 years who survived the age group and was exposed to current ASFRs) ranged from a low of 0\ub73 livebirths (0\ub73–0\ub74) in Puerto Rico to a high of 3\ub71 livebirths (3\ub70–3\ub72) in Niger. TFO30 was higher than TFU25 in 145 countries and territories in 2017. 33 countries had a negative population growth rate from 2010 to 2017, most of which were located in central, eastern, and western Europe, whereas population growth rates of more than 2\ub70% were seen in 33 of 46 countries in sub-Saharan Africa. In 2017, less than 65% of the national population was of working age in 12 of 34 high-income countries, and less than 50% of the national population was of working age in Mali, Chad, and Niger. Interpretation: Population trends create demographic dividends and headwinds (ie, economic benefits and detriments) that affect national economies and determine national planning needs. Although TFRs are decreasing, the global population continues to grow as mortality declines, with diverse patterns at the national level and across age groups. To our knowledge, this is the first study to provide transparent and replicable estimates of population and fertility, which can be used to inform decision making and to monitor progress. Funding: Bill &amp; Melinda Gates Foundation

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. Findings: From 1950 to 2017, TFRs decreased by 49·4% (95% uncertainty interval [UI] 46·4–52·0). The TFR decreased from 4·7 livebirths (4·5–4·9) to 2·4 livebirths (2·2–2·5), and the ASFR of mothers aged 10–19 years decreased from 37 livebirths (34–40) to 22 livebirths (19–24) per 1000 women. Despite reductions in the TFR, the global population has been increasing by an average of 83·8 million people per year since 1985. The global population increased by 197·2% (193·3–200·8) since 1950, from 2·6 billion (2·5–2·6) to 7·6 billion (7·4–7·9) people in 2017; much of this increase was in the proportion of the global population in south Asia and sub-Saharan Africa. The global annual rate of population growth increased between 1950 and 1964, when it peaked at 2·0%; this rate then remained nearly constant until 1970 and then decreased to 1·1% in 2017. Population growth rates in the southeast Asia, east Asia, and Oceania GBD super-region decreased from 2·5% in 1963 to 0·7% in 2017, whereas in sub-Saharan Africa, population growth rates were almost at the highest reported levels ever in 2017, when they were at 2·7%. The global average age increased from 26·6 years in 1950 to 32·1 years in 2017, and the proportion of the population that is of working age (age 15–64 years) increased from 59·9% to 65·3%. At the national level, the TFR decreased in all countries and territories between 1950 and 2017; in 2017, TFRs ranged from a low of 1·0 livebirths (95% UI 0·9–1·2) in Cyprus to a high of 7·1 livebirths (6·8–7·4) in Niger. The TFR under age 25 years (TFU25; number of livebirths expected by age 25 years for a hypothetical woman who survived the age group and was exposed to current ASFRs) in 2017 ranged from 0·08 livebirths (0·07–0·09) in South Korea to 2·4 livebirths (2·2–2·6) in Niger, and the TFR over age 30 years (TFO30; number of livebirths expected for a hypothetical woman ageing from 30 to 54 years who survived the age group and was exposed to current ASFRs) ranged from a low of 0·3 livebirths (0·3–0·4) in Puerto Rico to a high of 3·1 livebirths (3·0–3·2) in Niger. TFO30 was higher than TFU25 in 145 countries and territories in 2017. 33 countries had a negative population growth rate from 2010 to 2017, most of which were located in central, eastern, and western Europe, whereas population growth rates of more than 2·0% were seen in 33 of 46 countries in sub-Saharan Africa. In 2017, less than 65% of the national population was of working age in 12 of 34 high-income countries, and less than 50% of the national population was of working age in Mali, Chad, and Niger. Interpretation: Population trends create demographic dividends and headwinds (ie, economic benefits and detriments) that affect national economies and determine national planning needs. Although TFRs are decreasing, the global population continues to grow as mortality declines, with diverse patterns at the national level and across age groups. To our knowledge, this is the first study to provide transparent and replicable estimates of population and fertility, which can be used to inform decision making and to monitor progress

Global, regional, and national incidence, prevalence, and years lived with disability for 354 diseases and injuries for 195 countries and territories, 1990-2017: a systematic analysis for the Global Burden of Disease Study 2017.

The Global Burden of Diseases, Injuries and Risk Factors 2017 includes a comprehensive assessment of incidence, prevalence, and years lived with disability (YLDs) for 354 causes in 195 countries and territories from 1990 to 2017. Previous GBD studies have shown how the decline of mortality rates from 1990 to 2016 has led to an increase in life expectancy, an ageing global population, and an expansion of the non-fatal burden of disease and injury. These studies have also shown how a substantial portion of the world's population experiences non-fatal health loss with considerable heterogeneity among different causes, locations, ages, and sexes. Ongoing objectives of the GBD study include increasing the level of estimation detail, improving analytical strategies, and increasing the amount of high-quality data. METHODS: We estimated incidence and prevalence for 354 diseases and injuries and 3484 sequelae. We used an updated and extensive body of literature studies, survey data, surveillance data, inpatient admission records, outpatient visit records, and health insurance claims, and additionally used results from cause of death models to inform estimates using a total of 68 781 data sources. Newly available clinical data from India, Iran, Japan, Jordan, Nepal, China, Brazil, Norway, and Italy were incorporated, as well as updated claims data from the USA and new claims data from Taiwan (province of China) and Singapore. We used DisMod-MR 2.1, a Bayesian meta-regression tool, as the main method of estimation, ensuring consistency between rates of incidence, prevalence, remission, and cause of death for each condition. YLDs were estimated as the product of a prevalence estimate and a disability weight for health states of each mutually exclusive sequela, adjusted for comorbidity. We updated the Socio-demographic Index (SDI), a summary development indicator of income per capita, years of schooling, and total fertility rate. Additionally, we calculated differences between male and female YLDs to identify divergent trends across sexes. GBD 2017 complies with the Guidelines for Accurate and Transparent Health Estimates Reporting