Elevated platelet counts in a cohort of children with moderate-severe osteogenesis imperfecta suggest that inflammation is present

BACKGROUND: Elevated platelet counts are observed in cancer, autoimmunity and inflammation with concurrent illness. Proinflammatory cytokines are elevated in murine osteogenesis imperfecta (OI) models. We hypothesised that platelet counts might be elevated in children with moderate-severe OI. METHODS: We reviewed the hospital records of 71 children with moderate-severe OI, treated in the Sheffield Children's Hospital's Severe, Complex and Atypical Osteogenesis Imperfecta Highly Specialised Service. Data relating platelet count (below/above average, above upper limit) to prior and concurrent events were summarised as event proportions per child. Additionally, we created platelet SD scores to assess age and time-related trends, and relationship with OI type. RESULTS: 1206 platelet counts were recorded. Platelet SD scores were right-shifted by 0.89 SD overall. 49 of 71 (69%) patients had at least one platelet count above the normal range and 246 (20.4%) of all counts were above the upper limit of normal. Of these, 101 (41%) were high despite no confounding factors being present. For the 47 children with data at age less than 2 years, 89 (30.0%) platelet counts were above the upper limit of normal and 39 (44%) had no associated confounding factor. Elevated platelet counts were recorded most often for children with new or existing vertebral fractures. CONCLUSIONS: Raised platelet counts were observed in association with new and healing vertebral fractures, but also (41%-44%) in the absence of identified proinflammatory factors or events. We speculate that these findings are evidence for a proinflammatory component to OI that could be a target for therapeutic intervention

Rhizosphere microbiome metagenomics of gray mangroves (Avicennia marina) in the Red Sea

AbstractMangroves are unique, and endangered, coastal ecosystems that play a vital role in the tropical and subtropical environments. A comprehensive description of the microbial communities in these ecosystems is currently lacking, and additional studies are required to have a complete understanding of the functioning and resilience of mangroves worldwide.In this work, we carried out a metagenomic study by comparing the microbial community of mangrove sediment with the rhizosphere microbiome of Avicennia marina, in northern Red Sea mangroves, along the coast of Saudi Arabia. Our results revealed that rhizosphere samples presented similar profiles at the taxonomic and functional levels and differentiated from the microbiome of bulk soil controls. Overall, samples showed predominance by Proteobacteria, Bacteroidetes and Firmicutes, with high abundance of sulfate reducers and methanogens, although specific groups were selectively enriched in the rhizosphere. Functional analysis showed significant enrichment in ‘metabolism of aromatic compounds’, ‘mobile genetic elements’, ‘potassium metabolism’ and ‘pathways that utilize osmolytes’ in the rhizosphere microbiomes.To our knowledge, this is the first metagenomic study on the microbiome of mangroves in the Red Sea, and the first application of unbiased 454-pyrosequencing to study the rhizosphere microbiome associated with A. marina. Our results provide the first insights into the range of functions and microbial diversity in the rhizosphere and soil sediments of gray mangrove (A. marina) in the Red Sea

Bayesian inference for palaeoclimate with time uncertainty and stochastic volatility

We propose and fit a Bayesian model to infer palaeoclimate over several thousand years. The data that we use arise as ancient pollen counts taken from sediment cores together with radiocarbon dates which provide (uncertain) ages. When combined with a modern pollen–climate data set, we can calibrate ancient pollen into ancient climate. We use a normal–inverse Gaussian process prior to model the stochastic volatility of palaeoclimate over time, and we present a novel modularized Markov chain Monte Chain algorithm to enable fast computation. We illustrate our approach with a case-study from Sluggan Moss, Northern Ireland, and provide an R package, Bclim, for use at other sites