University of Zanjan

, where X is Cl, Br or I) and bismuth chalcogenides, including bismuth oxide, bismuth sulfide, bismuth selenide, and bismuth telluride, have been heavily investigated for therapeutic purposes. The pharmacokinetics of these BiNPs can be easily improved via the facile modification of their surfaces with biocompatible polymers and proteins, resulting in enhanced colloidal stability, extended blood circulation, and reduced toxicity. Desirable antibacterial effects, bone regeneration potential, and tumor growth suppression under NIR laser radiation are the main biomedical research areas involving BiNPs that have opened up a new paradigm for their future clinical translation. This review emphasizes the synthesis and state-of-the-art progress related to the biomedical applications of BiNPs with different structures, sizes, and compositions. Furthermore, a comprehensive discussion focusing on challenges and future opportunities is presented.

BACKGROUND: At the end of December 2019, a novel coronavirus tentatively named SARS-CoV-2 in Wuhan, a central city in China, was announced by the World Health Organization. SARS-CoV-2 is an RNA virus that has become a major public health concern after the outbreak of the Middle East Respiratory Syndrome-CoV (MERS-CoV) and Severe Acute Respiratory Syndrome-CoV (SARS-CoV) in 2002 and 2012, respectively. As of 29 October 2020, the total number of COVID-19 cases had reached over 44 million worldwide, with more than 1.17 million confirmed deaths. DISCUSSION: SARS-CoV-2 infected patients usually present with severe viral pneumonia. Similar to SARS-CoV, the virus enters respiratory tract cells via the angiotensin-converting enzyme receptor 2. The structural proteins play an essential role in budding the virus particles released from different host cells. To date, an approved vaccine or treatment option of a preventive character to avoid severe courses of COVID-19 is still not available. CONCLUSIONS: In the present study, we provide a brief review of the general biological features of CoVs and explain the pathogenesis, clinical symptoms and diagnostic approaches regarding monitoring future infectivity and prevent emerging COVID-19 infections.

The use of biomarkers as early warning systems in the evaluation of disease risk has increased markedly in the last decade. Biomarkers are indicators of typical biological processes, pathogenic processes, or pharmacological reactions to therapy. The application and identification of biomarkers in the medical and clinical fields have an enormous impact on society. In this review, we discuss the history, various definitions, classifications, characteristics, and discovery of biomarkers. Furthermore, the potential application of biomarkers in the diagnosis, prognosis, and treatment of various diseases over the last decade are reviewed. The present review aims to inspire readers to explore new avenues in biomarker research and development.

With the increasing concern over the environmental impact of conventional chemical methods, environmentally friendly processes, commonly known as green chemistry, for the synthesis of nanoparticles have gained growing interest in the field of nanobiotechnology. This review focuses on synthesis of metallic nanoparticles (NPs) based on green chemistry and their applications as new drug delivery system in anticancer and antimicrobial treatment. The review encompasses a survey of the production and characterization of green synthetic NPs, along with an examination of their physico-chemical properties and biological activities. Notably, this review goes beyond previous reports by providing an extensive analysis of recent studies that utilize in silico design for the green synthesis of nanoparticles and computational modeling to gain deeper insights into the interactions between these NPs and their targets. The simulation helps not only with comprehending the biological mechanism of NPs, but also with predicting any potential bioactivities. By offering a broad perspective and novel ideas, this review attempts to shed light on the future of green chemistry in the development of smart medicine and modern generation of cancer therapy and other disease treatments.

BACKGROUND: Timely and comprehensive analyses of causes of death stratified by age, sex, and location are essential for shaping effective health policies aimed at reducing global mortality. The Global Burden of Diseases, Injuries, and Risk Factors Study (GBD) 2023 provides cause-specific mortality estimates measured in counts, rates, and years of life lost (YLLs). GBD 2023 aimed to enhance our understanding of the relationship between age and cause of death by quantifying the probability of dying before age 70 years (70q0) and the mean age at death by cause and sex. This study enables comparisons of the impact of causes of death over time, offering a deeper understanding of how these causes affect global populations. METHODS: GBD 2023 produced estimates for 292 causes of death disaggregated by age-sex-location-year in 204 countries and territories and 660 subnational locations for each year from 1990 until 2023. We used a modelling tool developed for GBD, the Cause of Death Ensemble model (CODEm), to estimate cause-specific death rates for most causes. We computed YLLs as the product of the number of deaths for each cause-age-sex-location-year and the standard life expectancy at each age. Probability of death was calculated as the chance of dying from a given cause in a specific age period, for a specific population. Mean age at death was calculated by first assigning the midpoint age of each age group for every death, followed by computing the mean of all midpoint ages across all deaths attributed to a given cause. We used GBD death estimates to calculate the observed mean age at death and to model the expected mean age across causes, sexes, years, and locations. The expected mean age reflects the expected mean age at death for individuals within a population, based on global mortality rates and the population's age structure. Comparatively, the observed mean age represents the actual mean age at death, influenced by all factors unique to a location-specific population, including its age structure. As part of the modelling process, uncertainty intervals (UIs) were generated using the 2·5th and 97·5th percentiles from a 250-draw distribution for each metric. Findings are reported as counts and age-standardised rates. Methodological improvements for cause-of-death estimates in GBD 2023 include a correction for the misclassification of deaths due to COVID-19, updates to the method used to estimate COVID-19, and updates to the CODEm modelling framework. This analysis used 55 761 data sources, including vital registration and verbal autopsy data as well as data from surveys, censuses, surveillance systems, and cancer registries, among others. For GBD 2023, there were 312 new country-years of vital registration cause-of-death data, 3 country-years of surveillance data, 51 country-years of verbal autopsy data, and 144 country-years of other data types that were added to those used in previous GBD rounds. FINDINGS: The initial years of the COVID-19 pandemic caused shifts in long-standing rankings of the leading causes of global deaths: it ranked as the number one age-standardised cause of death at Level 3 of the GBD cause classification hierarchy in 2021. By 2023, COVID-19 dropped to the 20th place among the leading global causes, returning the rankings of the leading two causes to those typical across the time series (ie, ischaemic heart disease and stroke). While ischaemic heart disease and stroke persist as leading causes of death, there has been progress in reducing their age-standardised mortality rates globally. Four other leading causes have also shown large declines in global age-standardised mortality rates across the study period: diarrhoeal diseases, tuberculosis, stomach cancer, and measles. Other causes of death showed disparate patterns between sexes, notably for deaths from conflict and terrorism in some locations. A large reduction in age-standardised rates of YLLs occurred for neonatal disorders. Despite this, neonatal disorders remained the leading cause of global YLLs over the period studied, except in 2021, when COVID-19 was temporarily the leading cause. Compared to 1990, there has been a considerable reduction in total YLLs in many vaccine-preventable diseases, most notably diphtheria, pertussis, tetanus, and measles. In addition, this study quantified the mean age at death for all-cause mortality and cause-specific mortality and found noticeable variation by sex and location. The global all-cause mean age at death increased from 46·8 years (95% UI 46·6-47·0) in 1990 to 63·4 years (63·1-63·7) in 2023. For males, mean age increased from 45·4 years (45·1-45·7) to 61·2 years (60·7-61·6), and for females it increased from 48·5 years (48·1-48·8) to 65·9 years (65·5-66·3), from 1990 to 2023. The highest all-cause mean age at death in 2023 was found in the high-income super-region, where the mean age for females reached 80·9 years (80·9-81·0) and for males 74·8 years (74·8-74·9). By comparison, the lowest all-cause mean age at death occurred in sub-Saharan Africa, where it was 38·0 years (37·5-38·4) for females and 35·6 years (35·2-35·9) for males in 2023. Lastly, our study found that all-cause 70q0 decreased across each GBD super-region and region from 2000 to 2023, although with large variability between them. For females, we found that 70q0 notably increased from drug use disorders and conflict and terrorism. Leading causes that increased 70q0 for males also included drug use disorders, as well as diabetes. In sub-Saharan Africa, there was an increase in 70q0 for many non-communicable diseases (NCDs). Additionally, the mean age at death from NCDs was lower than the expected mean age at death for this super-region. By comparison, there was an increase in 70q0 for drug use disorders in the high-income super-region, which also had an observed mean age at death lower than the expected value. INTERPRETATION: We examined global mortality patterns over the past three decades, highlighting-with enhanced estimation methods-the impacts of major events such as the COVID-19 pandemic, in addition to broader trends such as increasing NCDs in low-income regions that reflect ongoing shifts in the global epidemiological transition. This study also delves into premature mortality patterns, exploring the interplay between age and causes of death and deepening our understanding of where targeted resources could be applied to further reduce preventable sources of mortality. We provide essential insights into global and regional health disparities, identifying locations in need of targeted interventions to address both communicable and non-communicable diseases. There is an ever-present need for strengthened health-care systems that are resilient to future pandemics and the shifting burden of disease, particularly among ageing populations in regions with high mortality rates. Robust estimates of causes of death are increasingly essential to inform health priorities and guide efforts toward achieving global health equity. The need for global collaboration to reduce preventable mortality is more important than ever, as shifting burdens of disease are affecting all nations, albeit at different paces and scales. FUNDING: Gates Foundation.

Dye-sensitized solar cells (DSSCs) have motivated many researchers to develop various sensitizers with tailored properties involving anchoring and ancillary ligands. Ancillary ligands carry favorable light-harvesting abilities and are therefore crucial in determining the overall power conversion efficiencies. The use of ancillary ligands having aliphatic chains and/or π-extended aromatic units decreases charge recombination and permits the collection of a large fraction of sunlight. This review aims to provide insight into the relationship between ancillary ligand structure and DSSC properties, which can further guide the function-oriented design and synthesis of different sensitizers for DSSCs. This review outlines how the new and rapidly expanding class of chelating ancillary ligands bearing 2,2'-bipyridyl, 1,10-phenanthroline, carbene, dipyridylamine, pyridyl-benzimidazole, pyridyl-azolate, and other aromatic ligands provides a conduit for potentially enhancing the performance and stability of DSSCs. Finally, these classes of Ru polypyridyl complexes have gained increasing interest for feasible large-scale commercialization of DSSCs due to their more favorable light-harvesting abilities and long-term thermal and chemical stabilities compared with other conventional sensitizers. Therefore, the main idea is to inspire readers to explore new avenues in the design of new sensitizers for DSSCs based on different ancillary ligands.

Current strategies of tissue engineering are focused on the reconstruction and regeneration of damaged or deformed tissues by grafting of cells with scaffolds and biomolecules. Recently, much interest is given to scaffolds which are based on mimic the extracellular matrix that have induced the formation of new tissues. To return functionality of the organ, the presence of a scaffold is essential as a matrix for cell colonization, migration, growth, differentiation and extracellular matrix deposition, until the tissues are totally restored or regenerated. A wide variety of approaches has been developed either in scaffold materials and production procedures or cell sources and cultivation techniques to regenerate the tissues/organs in tissue engineering applications. This study has been conducted to present an overview of the different scaffold fabrication techniques such as solvent casting and particulate leaching, electrospinning, emulsion freeze-drying, thermally induced phase separation, melt molding and rapid prototyping with their properties, limitations, theoretical principles and their prospective in tailoring appropriate micro-nanostructures for tissue regeneration applications. This review also includes discussion on recent works done in the field of tissue engineering.

Recent advancements in machine learning achieved by Deep Neural Networks (DNNs) have been significant. While demonstrating high accuracy, DNNs are associated with a huge number of parameters and computations, which leads to high memory usage and energy consumption. As a result, deploying DNNs on devices with constrained hardware resources poses significant challenges. To overcome this, various compression techniques have been widely employed to optimize DNN accelerators. A promising approach is quantization, in which the full-precision values are stored in low bit-width precision. Quantization not only reduces memory requirements but also replaces high-cost operations with low-cost ones. DNN quantization offers flexibility and efficiency in hardware design, making it a widely adopted technique in various methods. Since quantization has been extensively utilized in previous works, there is a need for an integrated report that provides an understanding, analysis, and comparison of different quantization approaches. Consequently, we present a comprehensive survey of quantization concepts and methods, with a focus on image classification. We describe clustering-based quantization methods and explore the use of a scale factor parameter for approximating full-precision values. Moreover, we thoroughly review the training of a quantized DNN, including the use of a straight-through estimator and quantization regularization. We explain the replacement of floating-point operations with low-cost bitwise operations in a quantized DNN and the sensitivity of different layers in quantization. Furthermore, we highlight the evaluation metrics for quantization methods and important benchmarks in the image classification task. We also present the accuracy of the state-of-the-art methods on CIFAR-10 and ImageNet. This article attempts to make the readers familiar with the basic and advanced concepts of quantization, introduce important works in DNN quantization, and highlight challenges for future research in this field.

Recently, global climate change discussions have become more prominent, and forests are considered as the ecosystems most at risk by the consequences of climate change. Wildfires are among one of the main drivers leading to losses in forested areas. The increasing availability of free remotely sensed data has enabled the precise locations of wildfires to be reliably monitored. A wildfire data inventory was created by integrating global positioning system (GPS) polygons with data collected from the moderate resolution imaging spectroradiometer (MODIS) thermal anomalies product between 2012 and 2017 for Amol County, northern Iran. The GPS polygon dataset from the state wildlife organization was gathered through extensive field surveys. The integrated inventory dataset, along with sixteen conditioning factors (topographic, meteorological, vegetation, anthropological, and hydrological factors), was used to evaluate the potential of different machine learning (ML) approaches for the spatial prediction of wildfire susceptibility. The applied ML approaches included an artificial neural network (ANN), support vector machines (SVM), and random forest (RF). All ML approaches were trained using 75% of the wildfire inventory dataset and tested using the remaining 25% of the dataset in the four-fold cross-validation (CV) procedure. The CV method is used for dealing with the randomness effects of the training and testing dataset selection on the performance of applied ML approaches. To validate the resulting wildfire susceptibility maps based on three different ML approaches and four different folds of inventory datasets, the true positive and false positive rates were calculated. In the following, the accuracy of each of the twelve resulting maps was assessed through the receiver operating characteristics (ROC) curve. The resulting CV accuracies were 74%, 79% and 88% for the ANN, SVM and RF, respectively.

Cancer is one of the leading causes of death worldwide. Mutation of the cell regulates genes and protein causing cancer. Surgery, radiotherapy, and the use of anticancer agents are the current therapy of cancer despite their side effects. The general area of research relates to heterocyclic chemistry. The purpose of the article is to review the most recent advances in nitrogen-containing heterocyclics as possible chemotherapy agents for cancer. More than 90% of the novel drugs bear heterocyclics and among them, nitrogencontaining heterocyclic compounds show superior pharmaceutical effect than non-nitrogen compounds. Nitrogen-containing compounds, the heart of drug discovery, present a significant and valuable group of molecules that play a chief and vital role in the metabolism of living cells. Keywords: Anticancer, heterocyclic, nitrogen-containing heterocyclic, natural nitrogen-containing heterocyclic, synthetic nitrogencontaining heterocyclic, chemotherapy agents.

Biochar, as a carbon-rich substance produced from the thermal decomposition of organic biomass, is promising for sustainable environmental management. The production and consumption of this substance can directly and indirectly lead to the reduction of carbon emissions and, as a result, in neutralizing the effects of global warming and climate change can actively participate. The biochar route can be even more powerful than the bioenergy route in terms of carbon reduction targets. In line with these facts, this review tries to study biochar, its production by slow thermal decomposition biochar, and its diverse applications to reduce climate change and ecosystem stability. Also, the applications of biochar such as carbon sequestration, soil strengthening, and reducing pollutants are technically and environmentally discussed. Biochar's adaptability is consistent with sustainable development goals, making it a valuable tool for tackling global challenges such as climate change, soil degradation, and pollution control. Through the process of pyrolyzing residues and producing biochar, it is possible to avoid cumulative emissions of 66–130 billion metric tons of CO2 equivalent over a century. Approximately 50 % of these avoided emissions result from the long-term carbon sequestration capabilities of biochar. Additionally, 30 % of the reductions come from substituting fossil fuels with pyrolysis-derived energy, while the remaining 20 % arise from the prevention of methane and nitrous oxide emissions. Reflecting its growing significance, the biochar industry, valued at approximately USD 541.8 million in 2023, is poised for remarkable growth. Projected to expand at an impressive annual rate of 13.9 % from 2024 to 2030.

In this investigation, nanofluids of carbon nanotubes are prepared and the thermal conductivity and volumetric heat capacity of these fluids are measured using a thin layer technique as a function of time of ultrasonication, temperature, and volume fraction. It has been observed that after using the ultrasonic disrupter, the size of agglomerated particles and number of primary particles in a particle cluster was significantly decreased and that the thermal conductivity increased with elapsed ultrasonication time. The clustering of carbon nanotubes was also confirmed microscopically. The strong dependence of the effective thermal conductivity on temperature and volume fraction of nanofluids was attributed to Brownian motion and the interparticle potential, which influences the particle motion. The effect of temperature will become much more evident with an increase in the volume fraction and the agglomeration of the nanoparticles, as observed experimentally. The data obtained from this work have been compared with those of other studies and also with mathematical models at present proven for suspensions. Using a 2.5% volumetric concentration of carbon nanotubes resulted in a 20% increase in the thermal conductivity of the base fluid (ethylene glycol).The volumetric heat capacity also showed a pronounced increase with respect to that of the pure base fluid.

Salt stress as a major adverse factor can lower leaf water potential, leading to reduced turgor and some other responses, and ultimately lower crop productivity in arid and semi arid zones. Wheat is one of the main crops occupying a large area in Iran, where salt stress is the most limiting factor. Clearly, plant salt stress tolerance requires the activation of complex metabolic activities including antioxidative pathways, especially reactive oxygen species (ROS) and scavenging systems within the cells which can contribute to continued growth under water stress. In the work reported in this paper, the seeds of two local wheat cultivars (Alvand and Sardari) were grown hydroponically. Seedlings were subjected to Hoagland's solution as control, and 50, 100, 150 and 200 mM NaCl for 10 days. As a result, SOD (superoxide dismutase) increased in Sardari with the increase of salt stress, while in the case of Alvand, SOD showed constant activity at all salt stress levels. Meanwhile, CAT and GR exhibited the same trends in the two cultivars of wheat in salt stress conditions. Results indicated that in the case of Sardari, the scavenging of ROS by the scavenging system especially by SOD, CAT and GR was done well and damage to membranes or MDA was controlled. But in the case of Alvand, damage to membranes increased with the rise of stress levels. It can be concluded that all three antioxidant enzymes were limiting factors for this cultivar. Also these reasons led to the sensitivity of Alvand to salt stress.

The aim of this study is to find a suitable substitution for diglycidyl ether bisphenol A (DGEBA) to avoid the devastating side effects of bisphenol A.

Due to the explosion of available information on the Internet, the need for effective means of accessing and processing them has become vital for everyone. Recommender systems have been developed to help users to find what they may be interested in and business owners to sell their products more efficiently. They have found much attention in both academia and industry. A recommender algorithm takes into account user–item interactions, i.e., rating (or purchase) history of users on items, and their contextual information, if available. It then provides a list of potential items for each target user, such that the user is likely to positively rate (or purchase) them. In this paper, we review evaluation metrics used to assess performance of recommendation algorithms. We also survey a number of classical and modern recommendation algorithms and compare their performance in terms of different evaluation metrics on five benchmark datasets. Our experiments show that there is no golden recommendation algorithm showing the best performance in all evaluation metrics. We also find large variability across the datasets. This indicates that one should carefully consider the evaluation criteria in choosing a recommendation algorithm for a particular application.

Similar to many fields of sciences, recent deep learning advances have been applied extensively in geosciences for both small- and large-scale problems. However, the necessity of using large training data and the ‘black box’ nature of learning have limited them in practice and difficult to interpret. Furthermore, including the governing equations and physical facts in such methods is also another challenge, which entails either ignoring the physics or simplifying them using unrealistic data. To address such issues, physics informed machine learning methods have been developed which can integrate the governing physics law into the learning process. In this work, a 1-dimensional (1D) time-dependent seismic wave equation is considered and solved using two methods, namely Gaussian process (GP) and physics informed neural networks. We show that these meshless methods are trained by smaller amount of data and can predict the solution of the equation with even high accuracy. They are also capable of inverting any parameter involved in the governing equation such as wave velocity in our case. Results show that the GP can predict the solution of the seismic wave equation with a lower level of error, while our developed neural network is more accurate for velocity (P- and S-wave) and density inversion.

A novel and efficient method has been developed for the synthesis of 2,5-disubstituted 1,3,4-oxadiazole derivatives using (N-isocyanimino)triphenylphosphorane, a secondary amine, a carboxylic acid, and an aromatic aldehyde in CH(2)Cl(2) at ambient temperature in high yields without using any catalyst or activation. The procedure provides an alternative method to the synthesis of fully substituted 1,3,4-oxadiazole derivatives.

Abstract. In this paper, we present and analyze a novel global database of soil infiltration measurements, the Soil Water Infiltration Global (SWIG) database. In total, 5023 infiltration curves were collected across all continents in the SWIG database. These data were either provided and quality checked by the scientists who performed the experiments or they were digitized from published articles. Data from 54 different countries were included in the database with major contributions from Iran, China, and the USA. In addition to its extensive geographical coverage, the collected infiltration curves cover research from 1976 to late 2017. Basic information on measurement location and method, soil properties, and land use was gathered along with the infiltration data, making the database valuable for the development of pedotransfer functions (PTFs) for estimating soil hydraulic properties, for the evaluation of infiltration measurement methods, and for developing and validating infiltration models. Soil textural information (clay, silt, and sand content) is available for 3842 out of 5023 infiltration measurements (∼ 76%) covering nearly all soil USDA textural classes except for the sandy clay and silt classes. Information on land use is available for 76 % of the experimental sites with agricultural land use as the dominant type (∼ 40%). We are convinced that the SWIG database will allow for a better parameterization of the infiltration process in land surface models and for testing infiltration models. All collected data and related soil characteristics are provided online in *.xlsx and *.csv formats for reference, and we add a disclaimer that the database is for public domain use only and can be copied freely by referencing it. Supplementary data are available at https://doi.org/10.1594/PANGAEA.885492 (Rahmati et al., 2018). Data quality assessment is strongly advised prior to any use of this database. Finally, we would like to encourage scientists to extend and update the SWIG database by uploading new data to it.

facile synthetic procedures, containing hole transport materials (HTMs) with versatile triphenylamine (TPA) structural cores, amenable to functionalization, have become a focus of intense global research activity. To optimize the efficiency of the solar cells to achieve efficiencies closer to rival silicon-based technology, TPA building blocks must exhibit favourable electrochemical, photophysical, and photochemical properties that can be chemically tuned in a rational manner. Although PSCs based on TPA building blocks exhibit attractive properties such as high-power efficiencies, a reduction in their synthetic costs coupled with higher stabilities and environmental considerations still need to be addressed. Considering the above, a detailed summary of the most promising compounds and current methodologies employed to overcome the remaining challenges in this field is provided. The objective of this review is to provide guidance to readers on exploring new avenues for the discovery of efficient TPA derivatives, to aid in the future development and advancement of TPA-based PSCs for commercial applications.

BACKGROUND: Chronic kidney disease (CKD) is common and ranks among the leading causes of mortality and morbidity. This analysis aimed to present global CKD estimates using the Global Burden of Diseases, Injuries, and Risk Factors Study (GBD) 2023 to inform evidence-based policies for CKD identification and treatment. METHODS: This analysis focused on adults aged 20 years and older over the period 1990 to 2023, from 204 countries and territories. Data sources used were published literature, vital registration systems, kidney failure treatment registries, and household surveys. Estimates of CKD burden, including deaths, incidence, prevalence, and disability-adjusted life-years (DALYs), were produced using a Cause of Death Ensemble model and a Bayesian meta-regression analytical tool. A comparative risk assessment approach estimated the proportion of cardiovascular deaths attributable to impaired kidney function and estimated risk factors for CKD. FINDINGS: Globally, in 2023, 788 million (95% uncertainty interval 743-843) people aged 20 years and older were estimated to have CKD, up from 378 million (354-407) in 1990. The global age-standardised prevalence of CKD in adults was 14·2% (13·4-15·2), a relative rise of 3·5% (2·7-4·1) from 1990. The region with the highest age-standardised prevalence was north Africa and the Middle East (18·0%; 16·9-19·4). Most people had stage 1-3 CKD, with a combined prevalence of 13·9% (13·1-15·0). In 2023, CKD was the ninth leading cause of death globally, accounting for 1·48 million (1·30-1·65) deaths, and the 12th leading cause of DALYs, with an age-standardised DALY rate of 769·2 (691·8-857·4) per 100 000. Impaired kidney function as a risk factor accounted for 11·5% (8·4-14·5) of cardiovascular deaths. High fasting plasma glucose, body-mass index, and systolic blood pressure were all leading risk factors for CKD DALYs. INTERPRETATION: CKD is a major global health issue, with rising prevalence and increasing importance as a cause of death and as a risk factor for cardiovascular death. A better understating of aetiology, appropriate screening, and implementation programmes are needed to translate advances in CKD treatment into improved patient outcomes. FUNDING: Gates Foundation, Wellcome, US National Kidney Foundation, and US National Institute of Diabetes and Digestive and Kidney Diseases.