ORCID Profile
0000-0002-2045-1269
Current Organisation
Heriot-Watt University
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Publisher: Public Library of Science (PLoS)
Date: 26-01-2023
DOI: 10.1371/JOURNAL.PMED.1004036
Abstract: Preterm birth is the leading cause of perinatal morbidity and mortality and is associated with adverse developmental and long-term health outcomes, including several cardiometabolic risk factors and outcomes. However, evidence about the association of preterm birth with later body size derives mainly from studies using birth weight as a proxy of prematurity rather than an actual length of gestation. We investigated the association of gestational age (GA) at birth with body size from infancy through adolescence. We conducted a two-stage in idual participant data (IPD) meta-analysis using data from 253,810 mother–child dyads from 16 general population-based cohort studies in Europe (Denmark, Finland, France, Italy, Norway, Portugal, Spain, the Netherlands, United Kingdom), North America (Canada), and Australasia (Australia) to estimate the association of GA with body mass index (BMI) and overweight (including obesity) adjusted for the following maternal characteristics as potential confounders: education, height, prepregnancy BMI, ethnic background, parity, smoking during pregnancy, age at child’s birth, gestational diabetes and hypertension, and preecl sia. Pregnancy and birth cohort studies from the LifeCycle and the EUCAN-Connect projects were invited and were eligible for inclusion if they had information on GA and minimum one measurement of BMI between infancy and adolescence. Using a federated analytical tool (DataSHIELD), we fitted linear and logistic regression models in each cohort separately with a complete-case approach and combined the regression estimates and standard errors through random-effects study-level meta-analysis providing an overall effect estimate at early infancy ( .0 to 0.5 years), late infancy ( .5 to 2.0 years), early childhood ( .0 to 5.0 years), mid-childhood ( .0 to 9.0 years), late childhood ( .0 to 14.0 years), and adolescence ( .0 to 19.0 years). GA was positively associated with BMI in the first decade of life, with the greatest increase in mean BMI z-score during early infancy (0.02, 95% confidence interval (CI): 0.00 0.05, p 0.05) per week of increase in GA, while in adolescence, preterm in iduals reached similar levels of BMI (0.00, 95% CI: −0.01 0.01, p 0.9) as term counterparts. The association between GA and overweight revealed a similar pattern of association with an increase in odds ratio (OR) of overweight from late infancy through mid-childhood (OR 1.01 to 1.02) per week increase in GA. By adolescence, however, GA was slightly negatively associated with the risk of overweight (OR 0.98 [95% CI: 0.97 1.00], p 0.1) per week of increase in GA. Although based on only four cohorts ( n = 32,089) that reached the age of adolescence, data suggest that in iduals born very preterm may be at increased odds of overweight (OR 1.46 [95% CI: 1.03 2.08], p 0.05) compared with term counterparts. Findings were consistent across cohorts and sensitivity analyses despite considerable heterogeneity in cohort characteristics. However, residual confounding may be a limitation in this study, while findings may be less generalisable to settings in low- and middle-income countries. This study based on data from infancy through adolescence from 16 cohort studies found that GA may be important for body size in infancy, but the strength of association attenuates consistently with age. By adolescence, preterm in iduals have on average a similar mean BMI to peers born at term.
Publisher: Springer Science and Business Media LLC
Date: 13-06-2018
DOI: 10.1007/S13246-018-0656-5
Abstract: Neuroscientists have investigated the functionality of the brain in detail and achieved remarkable results but this area still need further research. Functional magnetic resonance imaging (fMRI) is considered as the most reliable and accurate technique to decode the human brain activity, on the other hand electroencephalography (EEG) is a portable and low cost solution in brain research. The purpose of this study is to find whether EEG can be used to decode the brain activity patterns like fMRI. In fMRI, data from a very specific brain region is enough to decode the brain activity patterns due to the quality of data. On the other hand, EEG can measure the rapid changes in neuronal activity patterns due to its higher temporal resolution i.e., in msec. These rapid changes mostly occur in different brain regions. In this study, multivariate pattern analysis (MVPA) is used both for EEG and fMRI data analysis and the information is extracted from distributed activation patterns of the brain. The significant information among different classes is extracted using two s le t test in both data sets. Finally, the classification analysis is done using the support vector machine. A fair comparison of both data sets is done using the same analysis techniques, moreover simultaneously collected data of EEG and fMRI is used for this comparison. The final analysis is done with the data of eight participants the average result of all conditions are found which is 65.7% for EEG data set and 64.1% for fMRI data set. It concludes that EEG is capable of doing brain decoding with the data from multiple brain regions. In other words, decoding accuracy with EEG MVPA is as good as fMRI MVPA and is above chance level.
Publisher: American Scientific Publishers
Date: 12-2017
Publisher: IEEE
Date: 12-2014
Publisher: IEEE
Date: 09-2017
Publisher: Cold Spring Harbor Laboratory
Date: 03-06-2022
DOI: 10.1101/2022.06.01.22275859
Abstract: Preterm birth is the leading cause of perinatal morbidity and mortality, and is associated with adverse developmental and long-term health outcomes, including several cardio-metabolic risk factors. However, evidence about the association of preterm birth with later body size derives mainly from studies using birth weight as proxy of prematurity rather than actual length of gestation. We investigated the association of gestational age at birth (GA) with body size from infancy through adolescence. We conducted a two-stage In idual Participant Data (IPD) meta-analysis using data from 253,810 mother-children dyads from 16 general population-based cohort studies in Europe, North America and Australasia to estimate the association of GA with standardized Body Mass Index (BMI) and overweight (including obesity) adjusted for confounders. Using a federated analytical tool (DataSHIELD), we fitted linear and logistic regression models in each cohort separately, and combined the regression estimates and standard errors through random-effects study-level meta-analysis providing an overall effect estimate at early infancy ( .0-0.5 years), late infancy ( .5-2.0 years), early childhood ( .0-5.0 years), mid-childhood ( .0-9.0 years), late childhood ( .0-14.0 years) and adolescence ( .0-19.0 years). GA was positively associated with BMI in the first decade of life with mean differences in BMI z-score (0.01-0.02) per week of increase in GA, however preterm infants reached similar levels of BMI as term infants by adolescence. The association of GA with risk of overweight revealed a similar pattern of results from late infancy through mid-childhood with an increased odds of overweight (OR 1.01-1.02) per week increase in GA. By adolescence, however, GA was slightly negatively associated with risk of overweight (OR 0.98 [95% CI: 0.97:1.00]) per week of increase in GA, and children born very preterm had increased odds of overweight (OR 1.46 [95% CI: 1.03 2.08]) compared with term. The findings were consistent across cohorts and sensitivity analyses, despite considerable heterogeneity in cohort characteristics. Higher GA is potentially clinically important for higher BMI in infancy, while the association attenuates consistently with age. By adolescence, preterm children have on average a similar mean BMI to those born term.
Publisher: IEEE
Date: 10-2015
Publisher: IEEE
Date: 05-2016
Publisher: IEEE
Date: 11-2015
Publisher: IEEE
Date: 08-2015
Publisher: Microbiology Society
Date: 2021
Abstract: Cryptosporidiosis is a major cause of diarrhoeal illness among African children, and is associated with childhood mortality, malnutrition, cognitive development and growth retardation. Cryptosporidium hominis is the dominant pathogen in Africa, and genotyping at the glycoprotein 60 ( gp60 ) gene has revealed a complex distribution of different subtypes across this continent. However, a comprehensive exploration of the metapopulation structure and evolution based on whole-genome data has yet to be performed. Here, we sequenced and analysed the genomes of 26 C . hominis isolates, representing different gp60 subtypes, collected at rural sites in Gabon, Ghana, Madagascar and Tanzania. Phylogenetic and cluster analyses based on single-nucleotide polymorphisms showed that isolates predominantly clustered by their country of origin, irrespective of their gp60 subtype. We found a significant isolation-by-distance signature that shows the importance of local transmission, but we also detected evidence of hybridization between isolates of different geographical regions. We identified 37 outlier genes with exceptionally high nucleotide ersity, and this group is significantly enriched for genes encoding extracellular proteins and signal peptides. Furthermore, these genes are found more often than expected in recombinant regions, and they show a distinct signature of positive or balancing selection. We conclude that: (1) the metapopulation structure of C. hominis can only be accurately captured by whole-genome analyses (2) local anthroponotic transmission underpins the spread of this pathogen in Africa (3) hybridization occurs between distinct geographical lineages and (4) genetic introgression provides novel substrate for positive or balancing selection in genes involved in host–parasite coevolution.
Publisher: IEEE
Date: 10-2015
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Date: 2017
Publisher: Japan Epidemiological Association
Date: 05-06-2023
Publisher: IEEE
Date: 06-2014
Publisher: Public Library of Science (PLoS)
Date: 30-05-2017
Publisher: IEEE
Date: 09-2017
Location: United Kingdom of Great Britain and Northern Ireland
No related grants have been discovered for Johanna Nader.