Altered inflammasome activation in neonatal encephalopathy persists in childhood

Neonatal encephalopathy (NE) is characterized by altered neurological function in term infants and inflammation plays an important pathophysiological role. Inflammatory cytokines interleukin (IL)-1 beta, IL-1ra and IL-18 are activated by the nucleotide-binding and oligomerization domain (NOD)-, leucine-rich repeat domain (LRR)- and NOD-like receptor protein 3 (NLRP3) inflammasome; furthermore, we aimed to examine the role of the inflammasome multiprotein complex involved in proinflammatory responses from the newborn period to childhood in NE. Cytokine concentrations were measured by multiplex enzyme-linked immunosorbent assay (ELISA) in neonates and children with NE in the absence or presence of lipopolysaccharide (LPS) endotoxin. We then investigated expression of the NLRP3 inflammasome genes, NLRP3, IL-1 beta and ASC by polymerase chain reaction (PCR). Serum samples from 40 NE patients at days 1 and 3 of the first week of life and in 37 patients at age 4-7 years were analysed. An increase in serum IL-1ra and IL-18 in neonates with NE on days 1 and 3 was observed compared to neonatal controls. IL-1ra in NE was decreased to normal levels at school age, whereas serum IL-18 in NE was even higher at school age compared to school age controls and NE in the first week of life. Percentage of LPS response was higher in newborns compared to school-age NE. NLRP3 and IL-1 beta gene expression were up-regulated in the presence of LPS in NE neonates and NLRP3 gene expression remained up-regulated at school age in NE patients compared to controls. Increased inflammasome activation in the first day of life in NE persists in childhood, and may increase the window for therapeutic intervention

The PREDICTS database: a global database of how local terrestrial biodiversity responds to human impacts

Biodiversity continues to decline in the face of increasing anthropogenic pressures such as habitat destruction, exploitation, pollution and introduction of alien species. Existing global databases of species’ threat status or population time series are dominated by charismatic species. The collation of datasets with broad taxonomic and biogeographic extents, and that support computation of a range of biodiversity indicators, is necessary to enable better understanding of historical declines and to project – and avert – future declines. We describe and assess a new database of more than 1.6 million samples from 78 countries representing over 28,000 species, collated from existing spatial comparisons of local-scale biodiversity exposed to different intensities and types of anthropogenic pressures, from terrestrial sites around the world. The database contains measurements taken in 208 (of 814) ecoregions, 13 (of 14) biomes, 25 (of 35) biodiversity hotspots and 16 (of 17) megadiverse countries. The database contains more than 1% of the total number of all species described, and more than 1% of the described species within many taxonomic groups – including flowering plants, gymnosperms, birds, mammals, reptiles, amphibians, beetles, lepidopterans and hymenopterans. The dataset, which is still being added to, is therefore already considerably larger and more representative than those used by previous quantitative models of biodiversity trends and responses. The database is being assembled as part of the PREDICTS project (Projecting Responses of Ecological Diversity In Changing Terrestrial Systems – www.predicts.org.uk). We make site-level summary data available alongside this article. The full database will be publicly available in 2015

Social and economic factors, maternal behaviours in pregnancy and neonatal adiposity in the PANDORA cohort

Background: Australian Indigenous women experience high rates of social disadvantage and type 2 diabetes (T2D) in pregnancy, but it is not known how social factors and maternal behaviours impact neonatal adiposity in offspring of women with hyperglycaemia in pregnancy. Methods: Participants were Indigenous (n=404) and Europid (n=240) women with gestational diabetes mellitus (GDM) or T2D in pregnancy and their offspring in the Pregnancy and Neonatal Diabetes Outcomes in Remote Australia (PANDORA) study. Social, economic factors, and maternal behaviours were measured in pregnancy and six neonatal anthropometric outcomes were examined after birth. Results: On univariate analysis, maternal education 2 times/week (p=0.002) were associated with increased sum of skinfolds (SSF) in offspring. Smoking was significantly associated with a reduction in anthropometric measures, except SSF. In multivariable models adjusted for ethnicity, BMI and hyperglycaemia, social and economic factors were no longer significant predictors of neonatal outcomes. Smoking was independently associated with a reduction in length, head circumference and fat free mass. Frequent fast food intake remained independently associated with SSF (β-coefficient 1.08mm, p=0.02). Conclusion: In women with hyperglycaemia in pregnancy, social factors were associated with neonatal adiposity, particularly skinfold measures. Promoting smoking cessation and limited intake of energy-dense, nutrient-poor foods in pregnancy are important to improve neonatal adiposity and lean mass outcomes. Addressing inequities in social and economic factors are likely to be important, particularly for Indigenous women or women experiencing social disadvantage.Danielle K. Longmore, Elizabeth L.M. Barr, Federica Barzi, I-Lynn Lee, Marie Kirkwood, Christine Connors, Jacqueline Boyle, Kerin O’Dea, Paul Zimmet, Jeremy Oats, Patrick Catalano, H. David McIntyre, Alex D.H. Brown, Jonathan E. Shaw, Louise J. Maple-Brown, on behalf of the PANDORA study research tea

Real-world experience of metformin use in pregnancy: observational data from the Northern Territory Diabetes in Pregnancy Clinical Register

Background: In Australia's Northern Territory, Indigenous mothers account for 33% of births and have high rates of hyperglycemia in pregnancy. The prevalence of type 2 diabetes (T2D) in pregnancy is up to 10-fold higher in Indigenous than non-Indigenous Australian mothers, and the use of metformin is common. We assessed birth outcomes in relation to metformin use during pregnancy from a clinical register. Methods: The study included women with gestational diabetes (GDM), newly diagnosed diabetes in pregnancy (DIP), or pre-existing T2D from 2012 to 2016. Data were analyzed for metformin use in the third trimester. Regression models were adjusted for maternal age, body mass index, parity, and insulin use. Results: Of 1649 pregnancies, 814 (49.4%) were to Indigenous women, of whom 234 (28.7%) had T2D (vs 4.6% non-Indigenous women; P < 0.001). Metformin use was high in Indigenous women (84%-90% T2D, 42%-48% GDM/DIP) and increased over time in non-Indigenous women (43%-100% T2D, 14%-35% GDM/DIP). Among Indigenous women with GDM/DIP, there were no significant differences between groups with and without metformin in cesarean section (51% vs 39%; adjusted odds ratio [aOR] 1.25, 95% confidence interval [CI] 0.87-1.81), large for gestational age (24% vs 13%; aOR 1.5, 95% CI 0.9-2.5), or serious neonatal adverse events (9.4% vs 5.9%; aOR 1.32, 95% CI 0.68-2.57). Metformin use was independently associated with earlier gestational age (37.7 vs 38.5 weeks), but the risk did not remain independently higher after exclusion of women managed with medical nutrition therapy alone, and the increase in birth

Evaluation of individual and ensemble probabilistic forecasts of COVID-19 mortality in the United States.

Short-term probabilistic forecasts of the trajectory of the COVID-19 pandemic in the United States have served as a visible and important communication channel between the scientific modeling community and both the general public and decision-makers. Forecasting models provide specific, quantitative, and evaluable predictions that inform short-term decisions such as healthcare staffing needs, school closures, and allocation of medical supplies. Starting in April 2020, the US COVID-19 Forecast Hub (https://covid19forecasthub.org/) collected, disseminated, and synthesized tens of millions of specific predictions from more than 90 different academic, industry, and independent research groups. A multimodel ensemble forecast that combined predictions from dozens of groups every week provided the most consistently accurate probabilistic forecasts of incident deaths due to COVID-19 at the state and national level from April 2020 through October 2021. The performance of 27 individual models that submitted complete forecasts of COVID-19 deaths consistently throughout this year showed high variability in forecast skill across time, geospatial units, and forecast horizons. Two-thirds of the models evaluated showed better accuracy than a naïve baseline model. Forecast accuracy degraded as models made predictions further into the future, with probabilistic error at a 20-wk horizon three to five times larger than when predicting at a 1-wk horizon. This project underscores the role that collaboration and active coordination between governmental public-health agencies, academic modeling teams, and industry partners can play in developing modern modeling capabilities to support local, state, and federal response to outbreaks