Mapping local patterns of childhood overweight and wasting in low- and middle-income countries between 2000 and 2017

A double burden of malnutrition occurs when individuals, household members or communities experience both undernutrition and overweight. Here, we show geospatial estimates of overweight and wasting prevalence among children under 5 years of age in 105 low- and middle-income countries (LMICs) from 2000 to 2017 and aggregate these to policy-relevant administrative units. Wasting decreased overall across LMICs between 2000 and 2017, from 8.4 (62.3 (55.1�70.8) million) to 6.4 (58.3 (47.6�70.7) million), but is predicted to remain above the World Health Organization�s Global Nutrition Target of <5 in over half of LMICs by 2025. Prevalence of overweight increased from 5.2 (30 (22.8�38.5) million) in 2000 to 6.0 (55.5 (44.8�67.9) million) children aged under 5 years in 2017. Areas most affected by double burden of malnutrition were located in Indonesia, Thailand, southeastern China, Botswana, Cameroon and central Nigeria. Our estimates provide a new perspective to researchers, policy makers and public health agencies in their efforts to address this global childhood syndemic. © 2020, The Author(s)

Author Correction: Mapping local patterns of childhood overweight and wasting in low- and middle-income countries between 2000 and 2017 (Nature Medicine, (2020), 26, 5, (750-759), 10.1038/s41591-020-0807-6)

An amendment to this paper has been published and can be accessed via a link at the top of the paper. © 2020, The Author(s)

Author Correction: Mapping local patterns of childhood overweight and wasting in low- and middle-income countries between 2000 and 2017 (Nature Medicine, (2020), 26, 5, (750-759), 10.1038/s41591-020-0807-6)

An amendment to this paper has been published and can be accessed via a link at the top of the paper. © 2020, The Author(s)

Dietary total antioxidant capacity interacts with a variant of chromosome 5q13-14 locus to influence cardio-metabolic risk factors among obese adults

BACKGROUND: The association between cocaine- and amphetamine-regulated transcript prepropeptide gene (CARTPT) and obesity-related outcomes has shown in the epidemiological studies. Nevertheless, there is lack of data regarding the CARTPT gene–diet interactions in terms of antioxidant potential of diet. So, this study aimed to test CARTPT gene–dietary non-enzymatic antioxidant capacity (NEAC) interactions on cardio-metabolic risk factors in obese individuals. METHODS AND MATERIAL: The present cross-sectional study was carried out among 288 apparently healthy obese adults within age range of 20–50 years. Antioxidant capacity of diet was estimated by calculating the oxygen radical absorbance capacity (ORAC), ferric reducing antioxidant power (FRAP), total radical-trapping antioxidant parameter (TRAP) and Trolox equivalent antioxidant capacity (TEAC) using a semiquantitative food frequency questionnaire (FFQ). Genotyping for CARTPT rs2239670 polymorphism was conducted by polymerase chain reaction–restriction fragment length polymorphism (PCR–RFLP) method. RESULTS: A significant interaction was revealed between CARTPT rs2239670 and dietary ORAC on BMI (P(Interaction) = 0.048) and fat mass percent (FM%) (P(Interaction) = 0.008); in A allele carriers, higher adherence to the dietary ORAC was related to lower level of BMI and FM%. And, the significant interactions were observed between FRAP index and rs2239670 in relation to HOMA (P(Interaction) = 0.049) and QUICKI (P(Interaction) = 0.048). Moreover, there were significant interactions of rs2239670 with TRAP (P(Interaction) = 0.029) and TEAC (P(Interaction) = 0.034) on the serum glucose level; individuals with AG genotype were more respondent to higher intake of TRAP. CONCLUSION: The present study indicated that the relationships between CARTPT rs2239670 and obesity and its-related metabolic parameters depend on adherence to the dietary NEAC. Large prospective studies are needed to confirm our findings

Fast and scalable protein motif sequence clustering based on Hadoop framework

In recent years, we are faced with large amounts of sporadic unstructured data on the web. With the explosive growth of such data, there is a growing need for effective methods such as clustering to analyze and extract information. Biological data forms an important part of unstructured data on the web. Protein sequence databases are considered as a primary source of biological data. Clustering can help to organize sequences into homologous and functionally similar groups and can improve the speed of data processing and analysis. Proteins are responsible for most of the activities in cells. The majority of proteins show their function through interaction with other proteins. Hence, prediction of protein interactions is an important research area in the biomedical sciences. Motifs are fragments frequently occurred in protein sequences. A well- known method to specify the protein interaction is based on motif Clustering. Existing works on motif clustering methods share the problem of limitation in the number of clusters. However, regarding the vast amount of motifs and the necessity of a large number of clusters, it seems that an efficient, scalable and fast method is necessary to cluster such large number of sequences. In this paper, we propose a novel approach to cluster a large number of motifs. Our approach includes extracting motifs within protein sequences, feature selection, preprocessing, dimension reduction and utilizing BigFCM (a large-scale fuzzy clustering) on several distributed nodes with Hadoop framework to take the advantage of MapReduce Programming. Experimental Results show very good Performance of our approach