Early role of vascular dysregulation on late-onset Alzheimer's disease based on multifactorial data-driven analysis

Multifactorial mechanisms underlying late-onset Alzheimer's disease (LOAD) are poorly characterized from an integrative perspective. Here spatiotemporal alterations in brain amyloid-β deposition, metabolism, vascular, functional activity at rest, structural properties, cognitive integrity and peripheral proteins levels are characterized in relation to LOAD progression. We analyse over 7,700 brain images and tens of plasma and cerebrospinal fluid biomarkers from the Alzheimer's Disease Neuroimaging Initiative (ADNI). Through a multifactorial data-driven analysis, we obtain dynamic LOAD–abnormality indices for all biomarkers, and a tentative temporal ordering of disease progression. Imaging results suggest that intra-brain vascular dysregulation is an early pathological event during disease development. Cognitive decline is noticeable from initial LOAD stages, suggesting early memory deficit associated with the primary disease factors. High abnormality levels are also observed for specific proteins associated with the vascular system's integrity. Although still subjected to the sensitivity of the algorithms and biomarkers employed, our results might contribute to the development of preventive therapeutic interventions

Inflammatory blockade prevents injury to the developing pulmonary gas exchange surface in preterm primates

Perinatal inflammatory stress is associated with early life morbidity and lifelong consequences for pulmonary health. Chorioamnionitis, an inflammatory condition affecting the placenta and fluid surrounding the developing fetus, affects 25 to 40% of preterm births. Severe chorioamnionitis with preterm birth is associated with significantly increased risk of pulmonary disease and secondary infections in childhood, suggesting that fetal inflammation may markedly alter the development of the lung. Here, we used intra-amniotic lipopolysaccharide (LPS) challenge to induce experimental chorioamnionitis in a prenatal rhesus macaque (Macaca mulatta) model that mirrors structural and temporal aspects of human lung development. Inflammatory injury directly disrupted the developing gas exchange surface of the primate lung, with extensive damage to alveolar structure, particularly the close association and coordinated differentiation of alveolar type 1 pneumocytes and specialized alveolar capillary endothelium. Single-cell RNA sequencing analysis defined a multicellular alveolar signaling niche driving alveologenesis that was extensively disrupted by perinatal inflammation, leading to a loss of gas exchange surface and alveolar simplification, with notable resemblance to chronic lung disease in newborns. Blockade of the inflammatory cytokines interleukin-1β and tumor necrosis factor-α ameliorated LPS-induced inflammatory lung injury by blunting stromal responses to inflammation and modulating innate immune activation in myeloid cells, restoring structural integrity and key signaling networks in the developing alveolus. These data provide new insight into the pathophysiology of developmental lung injury and suggest that modulating inflammation is a promising therapeutic approach to prevent fetal consequences of chorioamnionitis

Genomic Comparison of <em>Escherichia coli</em> O104:H4 Isolates from 2009 and 2011 Reveals Plasmid, and Prophage Heterogeneity, Including Shiga Toxin Encoding Phage stx2

<div><p>In May of 2011, an enteroaggregative <em>Escherichia coli</em> O104:H4 strain that had acquired a Shiga toxin 2-converting phage caused a large outbreak of bloody diarrhea in Europe which was notable for its high prevalence of hemolytic uremic syndrome cases. Several studies have described the genomic inventory and phylogenies of strains associated with the outbreak and a collection of historical <em>E. coli</em> O104:H4 isolates using draft genome assemblies. We present the complete, closed genome sequences of an isolate from the 2011 outbreak (2011C–3493) and two isolates from cases of bloody diarrhea that occurred in the Republic of Georgia in 2009 (2009EL–2050 and 2009EL–2071). Comparative genome analysis indicates that, while the Georgian strains are the nearest neighbors to the 2011 outbreak isolates sequenced to date, structural and nucleotide-level differences are evident in the Stx2 phage genomes, the <em>mer/tet</em> antibiotic resistance island, and in the prophage and plasmid profiles of the strains, including a previously undescribed plasmid with homology to the pMT virulence plasmid of <em>Yersinia pestis</em>. In addition, multiphenotype analysis showed that 2009EL–2071 possessed higher resistance to polymyxin and membrane-disrupting agents. Finally, we show evidence by electron microscopy of the presence of a common phage morphotype among the European and Georgian strains and a second phage morphotype among the Georgian strains. The presence of at least two stx2 phage genotypes in host genetic backgrounds that may derive from a recent common ancestor of the 2011 outbreak isolates indicates that the emergence of stx2 phage-containing <em>E. coli</em> O104:H4 strains probably occurred more than once, or that the current outbreak isolates may be the result of a recent transfer of a new stx2 phage element into a pre-existing stx2-positive genetic background.</p> </div

Using electronic health records to enhance surveillance of diabetes in children, adolescents and young adults: a study protocol for the DiCAYA Network

Introduction Traditional survey-based surveillance is costly, limited in its ability to distinguish diabetes types and time-consuming, resulting in reporting delays. The Diabetes in Children, Adolescents and Young Adults (DiCAYA) Network seeks to advance diabetes surveillance efforts in youth and young adults through the use of large-volume electronic health record (EHR) data. The network has two primary aims, namely: (1) to refine and validate EHR-based computable phenotype algorithms for accurate identification of type 1 and type 2 diabetes among youth and young adults and (2) to estimate the incidence and prevalence of type 1 and type 2 diabetes among youth and young adults and trends therein. The network aims to augment diabetes surveillance capacity in the USA and assess performance of EHR-based surveillance. This paper describes the DiCAYA Network and how these aims will be achieved.Methods and analysis The DiCAYA Network is spread across eight geographically diverse US-based centres and a coordinating centre. Three centres conduct diabetes surveillance in youth aged 0–17 years only (component A), three centres conduct surveillance in young adults aged 18–44 years only (component B) and two centres conduct surveillance in components A and B. The network will assess the validity of computable phenotype definitions to determine diabetes status and type based on sensitivity, specificity, positive predictive value and negative predictive value of the phenotypes against the gold standard of manually abstracted medical charts. Prevalence and incidence rates will be presented as unadjusted estimates and as race/ethnicity, sex and age-adjusted estimates using Poisson regression.Ethics and dissemination The DiCAYA Network is well positioned to advance diabetes surveillance methods. The network will disseminate EHR-based surveillance methodology that can be broadly adopted and will report diabetes prevalence and incidence for key demographic subgroups of youth and young adults in a large set of regions across the USA