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Adolescent bullying victimization is recognized as a public health and mental health problem in many countries. However, data on bullying victimization's prevalence and risk factors is scarce in sub-Saharan Africa Sierra Leone. This research aimed to determine bullying victimization prevalence and its associated factors among Sierra Leonean school-going adolescents. The Sierra Leone 2017 Global School-based Health Survey (GSHS) dataset was analyzed. The outcome variable was the respondent's self-report of bullying victimization ("How many days in the previous 30 days were you bullied?"). Descriptive, Pearson chi-square and binary logistic regression analyses were conducted. The regression analysis yielded adjusted odds ratios (aOR) with 95% confidence intervals (CIs) and a significance level of p 0.05. Bullying victimization was prevalent among 48.7% of the in-school adolescents. Adolescents who drank alcohol [aOR = 2.48, 95% CI = 1.50-4.10], who reported feelings of loneliness [aOR = 1.51, 95% CI = 1.07-2.14] and who had attempted suicide [aOR = 1.72, 95% CI = 1.03-2.87] were also more likely to be bullied. Also, school truancy [aOR = 1.53, 95% CI = 1.24-1.88] among teenagers was associated with an increased risk of being bullied. Our findings suggest that bullying is a widespread problem among Sierra Leonean school-aged youth, and alcohol drinking, loneliness, suicide attempt and school truancy are potential risk factors. In light of the aforementioned causes of bullying in schools, policymakers and school administrators in Sierra Leone need to develop and execute anti-bullying policies and initiatives that target the underlying risk factors of bullying among teenagers.

Coronavirus disease 2019 (COVID-19) caused by infection with the severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) is an acute respiratory disease with systemic complications. Therapeutic strategies for COVID-19, including repurposing (partially) developed drugs are urgently needed, regardless of the increasingly successful vaccination outcomes. We characterized two-dimensional (2D) and three-dimensional models (3D) to establish a physiologically relevant airway epithelial model with potential for investigating SARS-CoV-2 therapeutics. Human airway basal epithelial cells maintained in submerged 2D culture were used at low passage to retain the capacity to differentiate into ciliated, club, and goblet cells in both air-liquid interface culture (ALI) and airway organoid cultures, which were then analyzed for cell phenotype makers. Airway biopsies from non-asthmatic and asthmatic donors enabled comparative evaluation of the level and distribution of immunoreactive angiotensin-converting enzyme 2 (ACE2). ACE2 and transmembrane serine proteinase 2 (TMPRSS2) mRNA were expressed in ALI and airway organoids at levels similar to those of native (i.e., non-cultured) human bronchial epithelial cells, whereas furin expression was more faithfully represented in ALI. ACE2 was mainly localized to ciliated and basal epithelial cells in human airway biopsies, ALI, and airway organoids. Cystic fibrosis appeared to have no influence on ACE2 gene expression. Neither asthma nor smoking status had consistent marked influence on the expression or distribution of ACE2 in airway biopsies. SARS-CoV-2 infection of ALI cultures did not increase the levels of selected cytokines. Organotypic, and particularly ALI airway cultures are useful and practical tools for investigation of SARS-CoV-2 infection and evaluating the clinical potential of therapeutics for COVID-19.

Severe acute respiratory syndrome coronavirus-2 (SARS-CoV-2) is an emerging virus that has caused significant human morbidity and mortality since its detection in late 2019. With the rapid emergence has come an unprecedented programme of vaccine development with at least 300 candidates under development. Ferrets have proven to be an appropriate animal model for testing safety and efficacy of SARS-CoV-2 vaccines due to quantifiable virus shedding in nasal washes and oral swabs. Here, we outline our efforts early in the SARS-CoV-2 outbreak to propagate and characterize an Australian isolate of the virus in vitro and in an ex vivo model of human airway epithelium, as well as to demonstrate the susceptibility of domestic ferrets (Mustela putorius furo) to SARS-CoV-2 infection following intranasal challenge.

The respiratory Influenza A Viruses (IAVs) and emerging zoonotic viruses such as Severe Acute Respiratory Syndrome-Coronavirus-2 (SARS-CoV-2) pose a significant threat to human health. To accelerate our understanding of the host-pathogen response to respiratory viruses, the use of more complex in vitro systems such as normal human bronchial epithelial (NHBE) cell culture models has gained prominence as an alternative to animal models. NHBE cells were differentiated under air-liquid interface (ALI) conditions to form an in vitro pseudostratified epithelium. The responses of well-differentiated (wd) NHBE cells were examined following infection with the 2009 pandemic Influenza A/H1N1pdm09 strain or following challenge with the dsRNA mimic, poly(I:C). At 30 h postinfection with H1N1pdm09, the integrity of the airway epithelium was severely impaired and apical junction complex damage was exhibited by the disassembly of zona occludens-1 (ZO-1) from the cell cytoskeleton. wdNHBE cells produced an innate immune response to IAV-infection with increased transcription of pro- and anti-inflammatory cytokines and chemokines and the antiviral viperin but reduced expression of the mucin-encoding MUC5B, which may impair mucociliary clearance. Poly(I:C) produced similar responses to IAV, with the exception of MUC5B expression which was more than 3-fold higher than for control cells. This study demonstrates that wdNHBE cells are an appropriate ex-vivo model system to investigate the pathogenesis of respiratory viruses.

As part of a broader collaborative network of exome sequencing studies, we developed a jointly called data set of 5,685 Ashkenazi Jewish exomes. We make publicly available a resource of site and allele frequencies, which should serve as a reference for medical genetics in the Ashkenazim (hosted in part at https://ibd.broadinstitute.org, also available in gnomAD at http://gnomad.broadinstitute.org). We estimate that 34% of protein-coding alleles present in the Ashkenazi Jewish population at frequencies greater than 0.2% are significantly more frequent (mean 15-fold) than their maximum frequency observed in other reference populations. Arising via a well-described founder effect approximately 30 generations ago, this catalog of enriched alleles can contribute to differences in genetic risk and overall prevalence of diseases between populations. As validation we document 148 AJ enriched protein-altering alleles that overlap with "pathogenic" ClinVar alleles (table available at https://github.com/macarthur-lab/clinvar/blob/master/output/clinvar.tsv), including those that account for 10-100 fold differences in prevalence between AJ and non-AJ populations of some rare diseases, especially recessive conditions, including Gaucher disease (GBA, p.Asn409Ser, 8-fold enrichment); Canavan disease (ASPA, p.Glu285Ala, 12-fold enrichment); and Tay-Sachs disease (HEXA, c.1421+1G>C, 27-fold enrichment; p.Tyr427IlefsTer5, 12-fold enrichment). We next sought to use this catalog, of well-established relevance to Mendelian disease, to explore Crohn's disease, a common disease with an estimated two to four-fold excess prevalence in AJ. We specifically attempt to evaluate whether strong acting rare alleles, particularly protein-truncating or otherwise large effect-size alleles, enriched by the same founder-effect, contribute excess genetic risk to Crohn's disease in AJ, and find that ten rare genetic risk factors in NOD2 and LRRK2 are enriched in AJ (p < 0.005), including several novel con

Hypoxia-regulated microRNA-210 (miR-210) is a highly conserved microRNA, known to regulate various processes under hypoxic conditions. Previously we found that miR-210 is also involved in honeybee learning and memory, raising the questions of how neural activity may induce hypoxia-regulated genes and how miR-210 may regulate plasticity in more complex mammalian systems. Using a pull-down approach, we identified 620 unique target genes of miR-210 in humans, among which there was a significant enrichment of age-related neurodegenerative pathways, including Huntington's, Alzheimer's, and Parkinson's diseases. We have also validated that miR-210 directly regulates various identified target genes of interest involved with neuronal plasticity, neurodegenerative diseases, and miR-210-associated cancers. This data suggests a potentially novel mechanism for how metabolic changes may couple plasticity to neuronal activity through hypoxia-regulated genes such as miR-210.

There is broad agreement that genetic mutations occurring outside of the protein-coding regions play a key role in human disease. Despite this consensus, we are not yet capable of discerning which portions of non-coding sequence are important in the context of human disease. Here, we present Orion, an approach that detects regions of the non-coding genome that are depleted of variation, suggesting that the regions are intolerant of mutations and subject to purifying selection in the human lineage. We show that Orion is highly correlated with known intolerant regions as well as regions that harbor putatively pathogenic variation. This approach provides a mechanism to identify pathogenic variation in the human non-coding genome and will have immediate utility in the diagnostic interpretation of patient genomes and in large case control studies using whole-genome sequences.

Genome-wide association studies (GWAS) have identified many genetic susceptibility loci for colorectal cancer (CRC). However, variants in these loci explain only a small proportion of familial aggregation, and there are likely additional variants that are associated with CRC susceptibility. Genome-wide studies of gene-environment interactions may identify variants that are not detected in GWAS of marginal gene effects. To study this, we conducted a genome-wide analysis for interaction between genetic variants and alcohol consumption and cigarette smoking using data from the Colon Cancer Family Registry (CCFR) and the Genetics and Epidemiology of Colorectal Cancer Consortium (GECCO). Interactions were tested using logistic regression. We identified interaction between CRC risk and alcohol consumption and variants in the 9q22.32/HIATL1 (Pinteraction = 1.76×10-8; permuted p-value 3.51x10-8) region. Compared to non-/occasional drinking light to moderate alcohol consumption was associated with a lower risk of colorectal cancer among individuals with rs9409565 CT genotype (OR, 0.82 [95% CI, 0.74-0.91]; P = 2.1×10-4) and TT genotypes (OR,0.62 [95% CI, 0.51-0.75]; P = 1.3×10-6) but not associated among those with the CC genotype (p = 0.059). No genome-wide statistically significant interactions were observed for smoking. If replicated our suggestive finding of a genome-wide significant interaction between genetic variants and alcohol consumption might contribute to understanding colorectal cancer etiology and identifying subpopulations with differential susceptibility to the effect of alcohol on CRC risk.

Genetic association studies, in particular the genome-wide association study (GWAS) design, have provided a wealth of novel insights into the aetiology of a wide range of human diseases and traits, in particular cardiovascular diseases and lipid biomarkers. The next challenge consists of understanding the molecular basis of these associations. The integration of multiple association datasets, including gene expression datasets, can contribute to this goal. We have developed a novel statistical methodology to assess whether two association signals are consistent with a shared causal variant. An application is the integration of disease scans with expression quantitative trait locus (eQTL) studies, but any pair of GWAS datasets can be integrated in this framework. We demonstrate the value of the approach by re-analysing a gene expression dataset in 966 liver samples with a published meta-analysis of lipid traits including >100,000 individuals of European ancestry. Combining all lipid biomarkers, our re-analysis supported 26 out of 38 reported colocalisation results with eQTLs and identified 14 new colocalisation results, hence highlighting the value of a formal statistical test. In three cases of reported eQTL-lipid pairs (SYPL2, IFT172, TBKBP1) for which our analysis suggests that the eQTL pattern is not consistent with the lipid association, we identify alternative colocalisation results with SORT1, GCKR, and KPNB1, indicating that these genes are more likely to be causal in these genomic intervals. A key feature of the method is the ability to derive the output statistics from single SNP summary statistics, hence making it possible to perform systematic meta-analysis type comparisons across multiple GWAS datasets (implemented online at http://coloc.cs.ucl.ac.uk/coloc/). Our methodology provides information about candidate causal genes in associated intervals and has direct implications for the understanding of complex diseases as well as the design of drugs to targ

Standard approaches to data analysis in genome-wide association studies (GWAS) ignore any potential functional relationships between gene variants. In contrast gene pathways analysis uses prior information on functional structure within the genome to identify pathways associated with a trait of interest. In a second step, important single nucleotide polymorphisms (SNPs) or genes may be identified within associated pathways. The pathways approach is motivated by the fact that genes do not act alone, but instead have effects that are likely to be mediated through their interaction in gene pathways. Where this is the case, pathways approaches may reveal aspects of a trait's genetic architecture that would otherwise be missed when considering SNPs in isolation. Most pathways methods begin by testing SNPs one at a time, and so fail to capitalise on the potential advantages inherent in a multi-SNP, joint modelling approach. Here, we describe a dual-level, sparse regression model for the simultaneous identification of pathways and genes associated with a quantitative trait. Our method takes account of various factors specific to the joint modelling of pathways with genome-wide data, including widespread correlation between genetic predictors, and the fact that variants may overlap multiple pathways. We use a resampling strategy that exploits finite sample variability to provide robust rankings for pathways and genes. We test our method through simulation, and use it to perform pathways-driven gene selection in a search for pathways and genes associated with variation in serum high-density lipoprotein cholesterol levels in two separate GWAS cohorts of Asian adults. By comparing results from both cohorts we identify a number of candidate pathways including those associated with cardiomyopathy, and T cell receptor and PPAR signalling. Highlighted genes include those associated with the L-type calcium channel, adenylate cyclase, integrin, laminin, MAPK signalling and immune fu

A central challenge in interpreting personal genomes is determining which mutations most likely influence disease. Although progress has been made in scoring the functional impact of individual mutations, the characteristics of the genes in which those mutations are found remain largely unexplored. For example, genes known to carry few common functional variants in healthy individuals may be judged more likely to cause certain kinds of disease than genes known to carry many such variants. Until now, however, it has not been possible to develop a quantitative assessment of how well genes tolerate functional genetic variation on a genome-wide scale. Here we describe an effort that uses sequence data from 6503 whole exome sequences made available by the NHLBI Exome Sequencing Project (ESP). Specifically, we develop an intolerance scoring system that assesses whether genes have relatively more or less functional genetic variation than expected based on the apparently neutral variation found in the gene. To illustrate the utility of this intolerance score, we show that genes responsible for Mendelian diseases are significantly more intolerant to functional genetic variation than genes that do not cause any known disease, but with striking variation in intolerance among genes causing different classes of genetic disease. We conclude by showing that use of an intolerance ranking system can aid in interpreting personal genomes and identifying pathogenic mutations.

The purpose of the study was to investigate the relative importance of child and adolescent social and academic pathways to well-being in adulthood (32-years) indicated by a sense of meaning, social engagement, positive coping and prosocial values. Data were drawn from a 15 wave (32-year) longitudinal study of the health and development of around 1000 New Zealanders (Dunedin Multidisciplinary Health and Development Study, New Zealand). Moderate continuity in social connectedness (0.38) and high continuity in academic ability (0.90) was observed across childhood and adolescence. Adolescent social connectedness was a better predictor of adult well-being than academic achievement (0.62 vs. 0.12). There was evidence of an indirect pathway from adolescent academic achievement to adult well-being through social connectedness (0.29). Indicators of well-being in adulthood appear to be better explained by social connection rather than academic competencies pathways. Implications for promoting longer term well-being during the school years are discussed.

Heme oxygenase 1 (HO-1) is an essential enzyme induced by heme and multiple stimuli associated with critical illness. In humans, polymorphisms in the HMOX1 gene promoter may influence the magnitude of HO-1 expression. In many diseases including murine malaria, HO-1 induction produces protective anti-inflammatory effects, but observations from patients suggest these may be limited to a narrow range of HO-1 induction, prompting us to investigate the role of HO-1 in malaria infection. In 307 Gambian children with either severe or uncomplicated P. falciparum malaria, we characterized the associations of HMOX1 promoter polymorphisms, HMOX1 mRNA inducibility, HO-1 protein levels in leucocytes (flow cytometry), and plasma (ELISA) with disease severity. The (GT)(n) repeat polymorphism in the HMOX1 promoter was associated with HMOX1 mRNA expression in white blood cells in vitro, and with severe disease and death, while high HO-1 levels were associated with severe disease. Neutrophils were the main HO-1-expressing cells in peripheral blood, and HMOX1 mRNA expression was upregulated by heme-moieties of lysed erythrocytes. We provide mechanistic evidence that induction of HMOX1 expression in neutrophils potentiates the respiratory burst, and propose this may be part of the causal pathway explaining the association between short (GT)(n) repeats and increased disease severity in malaria and other critical illnesses. Our findings suggest a genetic predisposition to higher levels of HO-1 is associated with severe illness, and enhances the neutrophil burst leading to oxidative damage of endothelial cells. These add important information to the discussion about possible therapeutic manipulation of HO-1 in critically ill patients.

The use of Artificial Neural Network (ANN) classifiers in real-time applications that could be considered critical, such as head movement classification for the control of a powered wheelchair, naturally raises questions over safety due to performance accuracy. One inherent characteristic of an ANN is that the placement of classification boundaries within the decision space is arbitrary and ultimately unknown. The result of this characteristic is to give unpredictable results when presented with data outside the boundary of the training set and is therefore considered as a major source of uncertainty. The problem is one of accuracy and predictability and to address this a novel algorithm termed an Optimised Region and Boundary Classifier (RBC) was created to provide improvements in accuracy and predictability over a conventionally trained ANN. To improve accuracy the RBC requires the formation of effective boundaries, which relies on the training set containing data that is both representative of all types of data likely to be input to the classifier and is complementary (data either side of an implied boundary). To achieve this the original training set consisting of commands only (forward, back, left, and right) was augmented with “data outside the boundary”, which consisted of seventeen types of non-command data that the classifier was likely to see. To improve predictability the RBC uses explicit boundaries, which is achieved using kmeans clustering techniques to define Hyper-Rectangles. Also, two additional regions (vertical and horizontal null regions) are extracted from the Hyper-Rectangles and added to the classifier. To further improve accuracy and predictability an optimisation process is used that expands the Hyper-Rectangles for each of the classes to be classified (forward, back, left, and right) until the associated training error for sensitivity and specificity is optimal. To show that the RBC could provide improvements in performance comparisons were

The use of Artificial Neural Network (ANN) classifiers in real-time applications that could be considered critical, such as head movement classification for the control of a powered wheelchair, naturally raises questions over safety due to performance accuracy. One inherent characteristic of an ANN is that the placement of classification boundaries within the decision space is arbitrary and ultimately unknown. The result of this characteristic is to give unpredictable results when presented with data outside the boundary of the training set and is therefore considered as a major source of uncertainty. The problem is one of accuracy and predictability and to address this a novel algorithm termed an Optimised Region and Boundary Classifier (RBC) was created to provide improvements in accuracy and predictability over a conventionally trained ANN. To improve accuracy the RBC requires the formation of effective boundaries, which relies on the training set containing data that is both representative of all types of data likely to be input to the classifier and is complementary (data either side of an implied boundary). To achieve this the original training set consisting of commands only (forward, back, left, and right) was augmented with “data outside the boundary”, which consisted of seventeen types of non-command data that the classifier was likely to see. To improve predictability the RBC uses explicit boundaries, which is achieved using kmeans clustering techniques to define Hyper-Rectangles. Also, two additional regions (vertical and horizontal null regions) are extracted from the Hyper-Rectangles and added to the classifier. To further improve accuracy and predictability an optimisation process is used that expands the Hyper-Rectangles for each of the classes to be classified (forward, back, left, and right) until the associated training error for sensitivity and specificity is optimal. To show that the RBC could provide improvements in performance comparisons were