Youth visions in a changing climate: Emerging lessons from using immersive and arts-based methods for strengthening community-engaged research with urban youth

Despite increasing efforts, youth perspectives remain largely excluded from decision- making processes concerning their future and the social-ecological challenges they are set to inherit. While youth are a critical and powerful force for social change, many youths in underserved communities have limited access to appropriate information on the root causes and consequences of environmental change, in addition to an array of other complex social injustices. To address this, we embarked on a participatory action research process which focused on democratising research, science and the arts by facilitating experiential, immersive learning opportunities with the intention of eventually co-producing artifacts (in the form of participatory murals) in public spaces to facilitate longer term engagement with human nature futures. This article outlines and shares reflections on our process and offers insights for future engagement activities that seek to mobilise youth imaginaries and agency. We found participants were better engaged when conversations were (1) facilitated by other participants; (2) were outdoors and centred on public art; and (3) were happening in parallel with a hands-on activity. This contrasted with asking interview-type questions, or asking participants to write down their answers, which felt more like a test than a conversation, minimising participation. Key learnings included: the need to co-develop knowledge around enhancing climate literacy that is based on local realities; that multiple capacities and hives of activity already exist in communities and need to be mobilised and not built; that creative visioning and futuring can help identify options for change; and that many youths are seeking creative, immersive and safe spaces for co-learning and connection. Initiatives that aim to engage diverse voices should therefore be well- resourced so as to carefully co-design processes that start by acknowledging contextual differences and capacities within those contexts, and co-create immersive dialogues, in order to move away from test-like engagements which perpetuate power imbalances and discourage participation

Distinct Salmonella Enteritidis lineages associated with enterocolitis in high-income settings and invasive disease in low-income settings.

An epidemiological paradox surrounds Salmonella enterica serovar Enteritidis. In high-income settings, it has been responsible for an epidemic of poultry-associated, self-limiting enterocolitis, whereas in sub-Saharan Africa it is a major cause of invasive nontyphoidal Salmonella disease, associated with high case fatality. By whole-genome sequence analysis of 675 isolates of S. Enteritidis from 45 countries, we show the existence of a global epidemic clade and two new clades of S. Enteritidis that are geographically restricted to distinct regions of Africa. The African isolates display genomic degradation, a novel prophage repertoire, and an expanded multidrug resistance plasmid. S. Enteritidis is a further example of a Salmonella serotype that displays niche plasticity, with distinct clades that enable it to become a prominent cause of gastroenteritis in association with the industrial production of eggs and of multidrug-resistant, bloodstream-invasive infection in Africa.This work was supported by the Wellcome Trust. We would like to thank the members of the Pathogen Informatics Team and the core sequencing teams at the Wellcome Trust Sanger Institute (Cambridge, UK). We are grateful to D. Harris for work in managing the sequence data

Performance of non-invasive tests and histology for the prediction of clinical outcomes in patients with non-alcoholic fatty liver disease: an individual participant data meta-analysis

BackgroundHistologically assessed liver fibrosis stage has prognostic significance in patients with non-alcoholic fatty liver disease (NAFLD) and is accepted as a surrogate endpoint in clinical trials for non-cirrhotic NAFLD. Our aim was to compare the prognostic performance of non-invasive tests with liver histology in patients with NAFLD.MethodsThis was an individual participant data meta-analysis of the prognostic performance of histologically assessed fibrosis stage (F0–4), liver stiffness measured by vibration-controlled transient elastography (LSM-VCTE), fibrosis-4 index (FIB-4), and NAFLD fibrosis score (NFS) in patients with NAFLD. The literature was searched for a previously published systematic review on the diagnostic accuracy of imaging and simple non-invasive tests and updated to Jan 12, 2022 for this study. Studies were identified through PubMed/MEDLINE, EMBASE, and CENTRAL, and authors were contacted for individual participant data, including outcome data, with a minimum of 12 months of follow-up. The primary outcome was a composite endpoint of all-cause mortality, hepatocellular carcinoma, liver transplantation, or cirrhosis complications (ie, ascites, variceal bleeding, hepatic encephalopathy, or progression to a MELD score ≥15). We calculated aggregated survival curves for trichotomised groups and compared them using stratified log-rank tests (histology: F0–2 vs F3 vs F4; LSM: 2·67; NFS: 0·676), calculated areas under the time-dependent receiver operating characteristic curves (tAUC), and performed Cox proportional-hazards regression to adjust for confounding. This study was registered with PROSPERO, CRD42022312226.FindingsOf 65 eligible studies, we included data on 2518 patients with biopsy-proven NAFLD from 25 studies (1126 [44·7%] were female, median age was 54 years [IQR 44–63), and 1161 [46·1%] had type 2 diabetes). After a median follow-up of 57 months [IQR 33–91], the composite endpoint was observed in 145 (5·8%) patients. Stratified log-rank tests showed significant differences between the trichotomised patient groups (p<0·0001 for all comparisons). The tAUC at 5 years were 0·72 (95% CI 0·62–0·81) for histology, 0·76 (0·70–0·83) for LSM-VCTE, 0·74 (0·64–0·82) for FIB-4, and 0·70 (0·63–0·80) for NFS. All index tests were significant predictors of the primary outcome after adjustment for confounders in the Cox regression.InterpretationSimple non-invasive tests performed as well as histologically assessed fibrosis in predicting clinical outcomes in patients with NAFLD and could be considered as alternatives to liver biopsy in some cases

Patterns of genome evolution that have accompanied host adaptation inSalmonella

Many bacterial pathogens are specialized, infecting one or few hosts, and this is often associated with more acute disease presentation. Specific genomes show markers of this specialization, which often reflect a balance between gene acquisition and functional gene loss. Within Salmonella enterica subspecies enterica, a single lineage exists that includes human and animal pathogens adapted to cause infection in different hosts, including S. enterica serovar Enteritidis (multiple hosts), S. Gallinarum (birds), and S. Dublin (cattle). This provides an excellent evolutionary context in which differences between these pathogen genomes can be related to host range. Genome sequences were obtained from ∼ 60 isolates selected to represent the known diversity of this lineage. Examination and comparison of the clades within the phylogeny of this lineage revealed signs of host restriction as well as evolutionary events that mark a path to host generalism. We have identified the nature and order of events for both evolutionary trajectories. The impact of functional gene loss was predicted based upon position within metabolic pathways and confirmed with phenotyping assays. The structure of S. Enteritidis is more complex than previously known, as a second clade of S. Enteritidis was revealed that is distinct from those commonly seen to cause disease in humans or animals, and that is more closely related to S. Gallinarum. Isolates from this second clade were tested in a chick model of infection and exhibited a reduced colonization phenotype, which we postulate represents an intermediate stage in pathogen-host adaptation

Brief Report: Whole-Exome Sequencing to Identify Rare Variants and Gene Networks That Increase Susceptibility to Scleroderma in African Americans.

Peer Reviewedhttps://deepblue.lib.umich.edu/bitstream/2027.42/146461/1/art40541_am.pdfhttps://deepblue.lib.umich.edu/bitstream/2027.42/146461/2/art40541.pd

Brief Report: Whole-Exome Sequencing to Identify Rare Variants and Gene Networks That Increase Susceptibility to Scleroderma in African Americans

OBJECTIVE: Whole-exome sequencing (WES) studies in systemic sclerosis (SSc) patients of European American (EA) ancestry have identified variants in the ATP8B4 gene and enrichment of variants in genes in the extracellular matrix (ECM)-related pathway that increase SSc susceptibility. This study was undertaken to evaluate the association of the ATP8B4 gene and the ECM-related pathway with SSc in a cohort of African American (AA) patients. METHODS: SSc patients of AA ancestry were enrolled from 23 academic centers across the US under the Genome Research in African American Scleroderma Patients consortium. Unrelated AA individuals without serologic evidence of autoimmunity who were enrolled in the Howard University Family Study were used as unaffected controls. Functional variants in genes reported in the 2 WES studies in EA patients with SSc were selected for gene association testing using the optimized sequence kernel association test (SKAT-O) and pathway analysis by Ingenuity Pathway Analysis in 379 patients and 411 controls. RESULTS: Principal components analysis demonstrated that the patients and controls had similar ancestral backgrounds, with roughly equal proportions of mean European admixture. Using SKAT-O, we examined the association of individual genes that were previously reported in EA patients and none remained significant, including ATP8B4 (P = 0.98). However, we confirmed the previously reported association of the ECM-related pathway with enrichment of variants within the COL13A1, COL18A1, COL22A1, COL4A3, COL4A4, COL5A2, PROK1, and SERPINE1 genes (corrected P = 1.95 × 10-4 ). CONCLUSION: In the largest genetic study in AA patients with SSc to date, our findings corroborate the role of functional variants that aggregate in a fibrotic pathway and increase SSc susceptibility

HLA and autoantibodies define scleroderma subtypes and risk in African and European Americans and suggest a role for molecular mimicry.

Systemic sclerosis (SSc) is a clinically heterogeneous autoimmune disease characterized by mutually exclusive autoantibodies directed against distinct nuclear antigens. We examined HLA associations in SSc and its autoantibody subsets in a large, newly recruited African American (AA) cohort and among European Americans (EA). In the AA population, the African ancestry-predominant HLA-DRB1*08:04 and HLA-DRB1*11:02 alleles were associated with overall SSc risk, and the HLA-DRB1*08:04 allele was strongly associated with the severe antifibrillarin (AFA) antibody subset of SSc (odds ratio = 7.4). These African ancestry-predominant alleles may help explain the increased frequency and severity of SSc among the AA population. In the EA population, the HLA-DPB1*13:01 and HLA-DRB1*07:01 alleles were more strongly associated with antitopoisomerase (ATA) and anticentromere antibody-positive subsets of SSc, respectively, than with overall SSc risk, emphasizing the importance of HLA in defining autoantibody subtypes. The association of the HLA-DPB1*13:01 allele with the ATA+ subset of SSc in both AA and EA patients demonstrated a transancestry effect. A direct correlation between SSc prevalence and HLA-DPB1*13:01 allele frequency in multiple populations was observed (r = 0.98, P = 3 × 10-6). Conditional analysis in the autoantibody subsets of SSc revealed several associated amino acid residues, mostly in the peptide-binding groove of the class II HLA molecules. Using HLA α/β allelic heterodimers, we bioinformatically predicted immunodominant peptides of topoisomerase 1, fibrillarin, and centromere protein A and discovered that they are homologous to viral protein sequences from the Mimiviridae and Phycodnaviridae families. Taken together, these data suggest a possible link between HLA alleles, autoantibodies, and environmental triggers in the pathogenesis of SSc

HLA and autoantibodies define scleroderma subtypes and risk in African and European Americans and suggest a role for molecular mimicry

Systemic sclerosis (SSc) is a clinically heterogeneous autoimmune disease characterized by mutually exclusive autoantibodies directed against distinct nuclear antigens. We examined HLA associations in SSc and its autoantibody subsets in a large, newly recruited African American (AA) cohort and among European Americans (EA). In the AA population, the African ancestry-predominant HLA-DRB1*08:04 and HLA-DRB1*11:02 alleles were associated with overall SSc risk, and the HLA-DRB1*08:04 allele was strongly associated with the severe antifibrillarin (AFA) antibody subset of SSc (odds ratio = 7.4). These African ancestry-predominant alleles may help explain the increased frequency and severity of SSc among the AA population. In the EA population, the HLA-DPB1*13:01 and HLA-DRB1*07:01 alleles were more strongly associated with antitopoisomerase (ATA) and anticentromere antibody-positive subsets of SSc, respectively, than with overall SSc risk, emphasizing the importance of HLA in defining autoantibody subtypes. The association of the HLA-DPB1*13:01 allele with the ATA+ subset of SSc in both AA and EA patients demonstrated a transancestry effect. A direct correlation between SSc prevalence and HLA-DPB1*13:01 allele frequency in multiple populations was observed (r = 0.98, P = 3 × 10-6). Conditional analysis in the autoantibody subsets of SSc revealed several associated amino acid residues, mostly in the peptide-binding groove of the class II HLA molecules. Using HLA α/β allelic heterodimers, we bioinformatically predicted immunodominant peptides of topoisomerase 1, fibrillarin, and centromere protein A and discovered that they are homologous to viral protein sequences from the Mimiviridae and Phycodnaviridae families. Taken together, these data suggest a possible link between HLA alleles, autoantibodies, and environmental triggers in the pathogenesis of SSc

Machine learning algorithm improves the detection of NASH (NAS-based) and at-risk NASH: A development and validation study

Background and aims: Detecting NASH remains challenging, while at-risk NASH (steatohepatitis and F≥ 2) tends to progress and is of interest for drug development and clinical application. We developed prediction models by supervised machine learning techniques, with clinical data and biomarkers to stage and grade patients with NAFLD. Approach and results: Learning data were collected in the Liver Investigation: Testing Marker Utility in Steatohepatitis metacohort (966 biopsy-proven NAFLD adults), staged and graded according to NASH CRN. Conditions of interest were the clinical trial definition of NASH (NAS ≥ 4;53%), at-risk NASH (NASH with F ≥ 2;35%), significant (F ≥ 2;47%), and advanced fibrosis (F ≥ 3;28%). Thirty-five predictors were included. Missing data were handled by multiple imputations. Data were randomly split into training/validation (75/25) sets. A gradient boosting machine was applied to develop 2 models for each condition: clinical versus extended (clinical and biomarkers). Two variants of the NASH and at-risk NASH models were constructed: direct and composite models.Clinical gradient boosting machine models for steatosis/inflammation/ballooning had AUCs of 0.94/0.79/0.72. There were no improvements when biomarkers were included. The direct NASH model produced AUCs (clinical/extended) of 0.61/0.65. The composite NASH model performed significantly better (0.71) for both variants. The composite at-risk NASH model had an AUC of 0.83 (clinical and extended), an improvement over the direct model. Significant fibrosis models had AUCs (clinical/extended) of 0.76/0.78. The extended advanced fibrosis model (0.86) performed significantly better than the clinical version (0.82). Conclusions: Detection of NASH and at-risk NASH can be improved by constructing independent machine learning models for each component, using only clinical predictors. Adding biomarkers only improved the accuracy of fibrosis