Support vector regression: A novel soft computing technique for predicting the removal of cadmium from wastewater

43-50The presence of toxic heavy metals in the wastewater coming from industries is of great concern across the world. In the present work, a novel soft computing technique support vector regression (SVR)technique has been used to predict the removal of cadmium ions from wastewater with agricultural waste ‘rice polish’ as a low-cost adsorbent, with contact time, initial adsorbate concentration, pH of the medium, and temperature as the independent parameters. The developed SVR-based model has been compared with the widely used multiple regression (MR) model based on the statistical parameters such as coefficient of determination (R2), average relative error (AARE) etc. The prediction performance of SVR-based model has been found to be more accurate and generalized in comparison to MR model with low AARE values of 0.67% and high R2 values of 0.9997 while MR model gives an AARE value of 29.27% and 0.2161 as coefficient of determination (R2). Furthermore, it has also been observed that the SVR model effectively predicts the behavior of the complex interaction process of cadmium ions removal from waste water under various experimental conditions

Potential Use of Agro/Food Wastes as Biosorbents in the Removal of Heavy Metals

The production of large quantities of agro/food wastes from food processing industries and the release of pollutants in the form of heavy metals from various metallurgical industries are the grave problems of the society as well as serious threats to the environment. It is estimated that approximately one–third of all food that is produced goes to waste, meaning thereby that nearly 1.3 billion tonnes of agro/food wastes are generated per year. This readily available and large amount waste can be utilized for the removal of toxic metals obtained from metallurgical industries by converting it into the adsorbents. For example, mango peel showed adsorption capacity of 68.92 mg/g in removing cadmium II ions. Similarly, coconut waste showed a higher adsorption capacity of 285 and 263 mg/g in removing cadmium and lead ion, respectively. Biosorption and bioaccumulation are recommended as novel, efficient, eco-friendly, and less costly alternative technologies over the conventional methods such as ion exchange, chemical precipitation, and membrane filtration, etc. for the removal of toxic metal ions. Because of the presence of metal-binding functional groups, the industrial by-products, agro-wastes and microbial biomass are considered as the potential biosorbents. Thus they can be used for the removal of toxic metal ions. This chapter highlights the available information and methods on utilizing the agro/food waste for the eradication of toxic and heavy metal ions. Furthermore, this chapter also focuses on the sorption mechanisms of different adsorbents as well as their adsorbing capacities

Antiviral evaluation of an Hsp90 inhibitor, gedunin, against dengue virus

Purpose: To evaluate the antiviral potential of a tetranortriterpenoid, gedunin, against dengue virus (DENV) replication by targeting the host chaperone, Hsp90.Methods: The compound, gedunin, was tested against the replication of DENV in vitro using BHK-15 cells transfected with DENV-2 subgenomic replicon. Molecular docking of gedunin with Hsp90 protein was performed for evaluation of mode of action, using the program, Autodock vina.Results: In vitro antiviral data showed that gedunin significantly (p < 0.05) reduced DENV replication with EC50 of 10 μM. Further, in silico molecular docking data revealed strong interaction of gedunin with the ATP/ADP binding site of the host protein, Hsp90, with an estimated average free binding energy of -8.9 kcal/mol.Conclusion: The results validate gedunin as a potential antiviral candidate. Further in vitro assays and in vivo viral challenge studies are required to confirm the exact mode of action and pharmacological profile of gedunin in DENV infections.Keywords: Dengue virus replication, Hsp90, Gedunin, Antiviral, Molecular dockin

Removal of Heavy Metals from Wastewater with Special Reference to Groundnut Shells: Recent Advances

Wastewater contains organic pollutants and heavy metals which presents a significant threat to aquatic life and impacts human health and animals. In the past few years, the incomplete remediation of wastewater has made living beings suffer from various problems, and many health diseases are being noticed at a peak rate. Different methods have been employed to remove heavy metals from wastewater to date. However, the adsorption technique is the most efficient and eco-friendly for removing heavy metals and pollutants in wastewater remediation. Many agricultural wastes have been used as adsorbents for removing toxic pollutants and heavy metals from wastewater. Groundnut shell is widely considered agro-industrial waste. Groundnut shells account for nearly 20% of the dried peanut pod by weight, and millions of tons of its quantity are wasted every year. An increase in groundnut production leads to accumulating these groundnut shells in colossal quantities, which is not utilized; thus, they are either burnt or buried. Groundnut shells undergo slow degradation in the natural environment because they are rich in lignin content. Therefore, these shells can be converted into a valuable bio-product to produce less waste. Groundnut shells and groundnut shell-derived biochar act as good biosorbents in the wastewater treatment

Support vector regression: A novel soft computing technique for predicting the removal of cadmium from wastewater

The presence of toxic heavy metals in the wastewater coming from industries is of great concern across the world. In the present work, a novel soft computing technique support vector regression (SVR)technique has been used to predict the removal of cadmium ions from wastewater with agricultural waste ‘rice polish’ as a low-cost adsorbent, with contact time, initial adsorbate concentration, pH of the medium, and temperature as the independent parameters. The developed SVR-based model has been compared with the widely used multiple regression (MR) model based on the statistical parameters such as coefficient of determination (R2), average relative error (AARE) etc. The prediction performance of SVR-based model has been found to be more accurate and generalized in comparison to MR model with low AARE values of 0.67% and high R2 values of 0.9997 while MR model gives an AARE value of 29.27% and 0.2161 as coefficient of determination (R2). Furthermore, it has also been observed that the SVR model effectively predicts the behavior of the complex interaction process of cadmium ions removal from waste water under various experimental conditions

Global, regional, and national burden of chronic kidney disease, 1990–2017 : a systematic analysis for the Global Burden of Disease Study 2017

Background Health system planning requires careful assessment of chronic kidney disease (CKD) epidemiology, but data for morbidity and mortality of this disease are scarce or non-existent in many countries. We estimated the global, regional, and national burden of CKD, as well as the burden of cardiovascular disease and gout attributable to impaired kidney function, for the Global Burden of Diseases, Injuries, and Risk Factors Study 2017. We use the term CKD to refer to the morbidity and mortality that can be directly attributed to all stages of CKD, and we use the term impaired kidney function to refer to the additional risk of CKD from cardiovascular disease and gout. Methods The main data sources we used were published literature, vital registration systems, end-stage kidney disease registries, and household surveys. Estimates of CKD burden were produced using a Cause of Death Ensemble model and a Bayesian meta-regression analytical tool, and included incidence, prevalence, years lived with disability, mortality, years of life lost, and disability-adjusted life-years (DALYs). A comparative risk assessment approach was used to estimate the proportion of cardiovascular diseases and gout burden attributable to impaired kidney function. Findings Globally, in 2017, 1·2 million (95% uncertainty interval [UI] 1·2 to 1·3) people died from CKD. The global all-age mortality rate from CKD increased 41·5% (95% UI 35·2 to 46·5) between 1990 and 2017, although there was no significant change in the age-standardised mortality rate (2·8%, −1·5 to 6·3). In 2017, 697·5 million (95% UI 649·2 to 752·0) cases of all-stage CKD were recorded, for a global prevalence of 9·1% (8·5 to 9·8). The global all-age prevalence of CKD increased 29·3% (95% UI 26·4 to 32·6) since 1990, whereas the age-standardised prevalence remained stable (1·2%, −1·1 to 3·5). CKD resulted in 35·8 million (95% UI 33·7 to 38·0) DALYs in 2017, with diabetic nephropathy accounting for almost a third of DALYs. Most of the burden of CKD was concentrated in the three lowest quintiles of Socio-demographic Index (SDI). In several regions, particularly Oceania, sub-Saharan Africa, and Latin America, the burden of CKD was much higher than expected for the level of development, whereas the disease burden in western, eastern, and central sub-Saharan Africa, east Asia, south Asia, central and eastern Europe, Australasia, and western Europe was lower than expected. 1·4 million (95% UI 1·2 to 1·6) cardiovascular disease-related deaths and 25·3 million (22·2 to 28·9) cardiovascular disease DALYs were attributable to impaired kidney function. Interpretation Kidney disease has a major effect on global health, both as a direct cause of global morbidity and mortality and as an important risk factor for cardiovascular disease. CKD is largely preventable and treatable and deserves greater attention in global health policy decision making, particularly in locations with low and middle SDI

Future and potential spending on health 2015-40 : development assistance for health, and government, prepaid private, and out-of-pocket health spending in 184 countries

Background The amount of resources, particularly prepaid resources, available for health can affect access to health care and health outcomes. Although health spending tends to increase with economic development, tremendous variation exists among health financing systems. Estimates of future spending can be beneficial for policy makers and planners, and can identify financing gaps. In this study, we estimate future gross domestic product (GDP), all-sector government spending, and health spending disaggregated by source, and we compare expected future spending to potential future spending. Methods We extracted GDP, government spending in 184 countries from 1980-2015, and health spend data from 1995-2014. We used a series of ensemble models to estimate future GDP, all-sector government spending, development assistance for health, and government, out-of-pocket, and prepaid private health spending through 2040. We used frontier analyses to identify patterns exhibited by the countries that dedicate the most funding to health, and used these frontiers to estimate potential health spending for each low-income or middle-income country. All estimates are inflation and purchasing power adjusted. Findings We estimated that global spending on health will increase from US$9.21 trillion in 2014 to$24.24 trillion (uncertainty interval [UI] 20.47-29.72) in 2040. We expect per capita health spending to increase fastest in upper-middle-income countries, at 5.3% (UI 4.1-6.8) per year. This growth is driven by continued growth in GDP, government spending, and government health spending. Lower-middle income countries are expected to grow at 4.2% (3.8-4.9). High-income countries are expected to grow at 2.1% (UI 1.8-2.4) and low-income countries are expected to grow at 1.8% (1.0-2.8). Despite this growth, health spending per capita in low-income countries is expected to remain low, at $154 (UI 133-181) per capita in 2030 and$195 (157-258) per capita in 2040. Increases in national health spending to reach the level of the countries who spend the most on health, relative to their level of economic development, would mean $321 (157-258) per capita was available for health in 2040 in low-income countries. Interpretation Health spending is associated with economic development but past trends and relationships suggest that spending will remain variable, and low in some low-resource settings. Policy change could lead to increased health spending, although for the poorest countries external support might remain essential.Peer reviewe

Future and potential spending on health 2015-40: Development assistance for health, and government, prepaid private, and out-of-pocket health spending in 184 countries

Background: The amount of resources, particularly prepaid resources, available for health can affect access to health care and health outcomes. Although health spending tends to increase with economic development, tremendous variation exists among health financing systems. Estimates of future spending can be beneficial for policy makers and planners, and can identify financing gaps. In this study, we estimate future gross domestic product (GDP), all-sector government spending, and health spending disaggregated by source, and we compare expected future spending to potential future spending. Methods: We extracted GDP, government spending in 184 countries from 1980-2015, and health spend data from 1995-2014. We used a series of ensemble models to estimate future GDP, all-sector government spending, development assistance for health, and government, out-of-pocket, and prepaid private health spending through 2040. We used frontier analyses to identify patterns exhibited by the countries that dedicate the most funding to health, and used these frontiers to estimate potential health spending for each low-income or middle-income country. All estimates are inflation and purchasing power adjusted. Findings: We estimated that global spending on health will increase from US$9.21 trillion in 2014 to$24.24 trillion (uncertainty interval [UI] 20.47-29.72) in 2040. We expect per capita health spending to increase fastest in upper-middle-income countries, at 5.3% (UI 4.1-6.8) per year. This growth is driven by continued growth in GDP, government spending, and government health spending. Lower-middle income countries are expected to grow at 4.2% (3.8-4.9). High-income countries are expected to grow at 2.1% (UI 1.8-2.4) and low-income countries are expected to grow at 1.8% (1.0-2.8). Despite this growth, health spending per capita in low-income countries is expected to remain low, at $154 (UI 133-181) per capita in 2030 and$195 (157-258) per capita in 2040. Increases in national health spending to reach the level of the countries who spend the most on health, relative to their level of economic development, would mean $321 (157-258) per capita was available for health in 2040 in low-income countries. Interpretation: Health spending is associated with economic development but past trends and relationships suggest that spending will remain variable, and low in some low-resource settings. Policy change could lead to increased health spending, although for the poorest countries external support might remain essential

Frequency of hepatitis E and Hepatitis A virus in water sample collected from Faisalabad, Pakistan

Hepatitis E and Hepatitis A virus both are highly prevalent in Pakistan mainly present as a sporadic disease. The aim of the current study is to isolate and characterized the specific genotype of Hepatitis E virus from water bodies of Faisalabad, Pakistan. Drinking and sewage samples were qualitatively analyzed by using RT-PCR. HEV Genotype 1 strain was recovered from sewage water of Faisalabad. Prevalence of HEV and HAV in sewage water propose the possibility of gradual decline in the protection level of the circulated vaccine in the Pakistani population