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Additional file1: Figure S1. Example of the traceplot before and after thinning for the population mean parasite clearance rate, $$\gamma$$ γ . Figure S2. Example of the traceplot before and after thinning for the probability of lag and tail phases, $${\pi }^{\ell}$$ π ℓ and $${\pi }^{\tau }$$ π τ . Table S1. Summary statistics for parasite clearance rates (/hour) using WWARN PCE and Bayesian hierarchical modelling methods. Table S2. Estimated parasite clearance rates by antimalarial treatment administered using WWARN PCE and Bayesian hierarchical modelling.

This report describes the MalariaGEN Pv4 dataset, a new release of curated genome variation data on 1,895 samples of Plasmodium vivax collected at 88 worldwide locations between 2001 and 2017. It includes 1,370 new samples contributed by MalariaGEN and VivaxGEN partner studies in addition to previously published samples from these and other sources. We provide genotype calls at over 4.5 million variable positions including over 3 million single nucleotide polymorphisms (SNPs), as well as short indels and tandem duplications. This enlarged dataset highlights major compartments of parasite population structure, with clear differentiation between Africa, Latin America, Oceania, Western Asia and different parts of Southeast Asia. Each sample has been classified for drug resistance to sulfadoxine, pyrimethamine and mefloquine based on known markers at the dhfr, dhps and mdr1 loci. The prevalence of all of these resistance markers was much higher in Southeast Asia and Oceania than elsewhere. This open resource of analysis-ready genome variation data from the MalariaGEN and VivaxGEN networks is driven by our collective goal to advance research into the complex biology of P. vivax and to accelerate genomic surveillance for malaria control and elimination.

BackgroundCerebral malaria (CM) pathogenesis remains incompletely understood. Having shown low systemic levels of tetrahydrobiopterin (BH4), an enzymatic cofactor for neurotransmitter synthesis, we hypothesized that BH4 and BH4-dependent neurotransmitters would likewise be low in cerebrospinal fluid (CSF) in CM.MethodsWe prospectively enrolled Tanzanian children with CM and children with nonmalaria central nervous system conditions (NMCs). We measured CSF levels of BH4, neopterin, and BH4-dependent neurotransmitter metabolites, 3-O-methyldopa, homovanillic acid, and 5-hydroxyindoleacetate, and we derived age-adjusted z-scores using published reference ranges.ResultsCerebrospinal fluid BH4 was elevated in CM (n = 49) compared with NMC (n = 51) (z-score 0.75 vs -0.08; P

Additional file 1: Figure S1. No significant correlation between plasma Flt3L and peripheral blood pDC or CD1c+ DC in induced blood-stage malaria. Correlations of DC subset numbers and Flt3L concentration on day 0 (left plot), correlation of DC subset numbers and Flt3L concentration at peak infection (middle plot) and correlation of DC subset numbers at peak infection and Flt3L concentration at day 0 (right plot) for A. pDC (red) and B. CD1c+ DCs (green) in volunteer infection studies. Spearman correlation was used and tests were two-tailed and considered significant if p-values

Background In Papua, Indonesia, maternal malaria is prevalent, multidrug resistant and associated with adverse outcomes for mother and baby. In March 2006, anti-malarial policy was revised for the second and third trimester of pregnancy to dihydroartemisinin–piperaquine (DHP) for all species of malaria. This study presents the temporal analysis of adverse outcomes in pregnancy and early life following this policy change. Methods From April 2004 to May 2010, a standardized questionnaire was used to collect information from all pregnant women admitted to the maternity ward. A physical examination was performed on all live birth newborns. The relative risks (RR) and the associated population attributable risks (PAR) of adverse outcomes in women with a history of malaria treatment to the risk in those without a history of malaria during the current pregnancy were examined to evaluate the temporal trends before and after DHP deployment. Results Of 6,556 women enrolled with known pregnancy outcome, 1,018 (16%) reported prior anti-malarial treatment during their pregnancy. The proportion of women with malaria reporting treatment with DHP rose from 0% in 2004 to 64% (121/189) in 2010. In those with history of malaria during pregnancy, the increasing use of DHP was associated with a 54% fall in the proportion of maternal malaria at delivery and a 98% decrease in congenital malaria (from 7.1% prior to 0.1% after policy change). Overall policy change to more effective treatment was associated with an absolute 2% reduction of maternal severe anaemia and absolute 4.5% decrease in low birth weight babies. Conclusions Introduction of highly effective treatment in pregnancy was associated with a reduction of maternal malaria at delivery and improved neonatal outcomes. Ensuring universal access to arteminisin combination therapy (ACT) in pregnancy in an area of multidrug resistance has potential to impact significantly on maternal and infant health. Electronic supplementary material The online version of this article (doi:10.1186/s12936-015-0794-0) contains supplementary material, which is available to authorized users.

The complex transmission ecologies of vector-borne and zoonotic diseases pose challenges to their control, especially in changing landscapes. Human incidence of zoonotic malaria (Plasmodium knowlesi) is associated with deforestation although mechanisms are unknown. Here, a novel application of a method for predicting disease occurrence that combines machine learning and statistics is used to identify the key spatial scales that define the relationship between zoonotic malaria cases and environmental change. Using data from satellite imagery, a case–control study, and a cross-sectional survey, predictive models of household-level occurrence of P. knowlesi were fitted with 16 variables summarized at 11 spatial scales simultaneously. The method identified a strong and well-defined peak of predictive influence of the proportion of cleared land within 1 km of households on P. knowlesi occurrence. Aspect (1 and 2 km), slope (0.5 km) and canopy regrowth (0.5 km) were important at small scales. In contrast, fragmentation of deforested areas influenced P. knowlesi occurrence probability most strongly at large scales (4 and 5 km). The identification of these spatial scales narrows the field of plausible mechanisms that connect land use change and P. knowlesi, allowing for the refinement of disease occurrence predictions and the design of spatially-targeted interventions.

The complex transmission ecologies of vector-borne and zoonotic diseases pose challenges to their control, especially in changing landscapes. Human incidence of zoonotic malaria (Plasmodium knowlesi) is associated with deforestation although mechanisms are unknown. Here, a novel application of a method for predicting disease occurrence that combines machine learning and statistics is used to identify the key spatial scales that define the relationship between zoonotic malaria cases and environmental change. Using data from satellite imagery, a case–control study, and a cross-sectional survey, predictive models of household-level occurrence of P. knowlesi were fitted with 16 variables summarized at 11 spatial scales simultaneously. The method identified a strong and well-defined peak of predictive influence of the proportion of cleared land within 1 km of households on P. knowlesi occurrence. Aspect (1 and 2 km), slope (0.5 km) and canopy regrowth (0.5 km) were important at small scales. In contrast, fragmentation of deforested areas influenced P. knowlesi occurrence probability most strongly at large scales (4 and 5 km). The identification of these spatial scales narrows the field of plausible mechanisms that connect land use change and P. knowlesi, allowing for the refinement of disease occurrence predictions and the design of spatially-targeted interventions.

Observational studies and nonrandomized trials support an association between periodontal disease and atherosclerotic vascular disease. Both diseases occur frequently in Aboriginal Australians. We hypothesized that nonsurgical periodontal therapy would improve measures of arterial function and structure that are subclinical indicators of atherosclerotic vascular disease. This parallel-group, randomized, open label clinical trial enrolled 273 Aboriginal Australians aged ≥18 years with periodontitis. Intervention participants received full-mouth periodontal scaling during a single visit, whereas controls received no treatment. Prespecified primary end points measured 12-month change in carotid intima-media thickness, an indicator of arterial structure, and 3- and 12-month change in pulse wave velocity, an indicator of arterial function. ANCOVA used complete case data to evaluate treatment group differences. End points could be calculated for 169 participants with follow-up data at 3 months and 168 participants at 12 months. Intima-media thickness decreased significantly after 12 months in the intervention group (mean reduction=−0.023 [95% confidence interval {CI}, −0.038 to −0.008] mm) but not in the control group (mean increase=0.002 [95% CI, −0.017 to 0.022] mm). The difference in intima-media thickness change between treatment groups was statistically significant (−0.026 [95% CI, −0.048 to −0.003] mm; P =0.03). In contrast, there were no significant differences between treatment groups in pulse wave velocity at 3 months (mean difference, 0.06 [95% CI, −0.17 to 0.29] m/s; P =0.594) or 12 months (mean difference, 0.21 [95% CI, −0.01 to 0.43] m/s; P =0.062). Periodontal therapy reduced subclinical arterial thickness but not function in Aboriginal Australians with periodontal disease, suggesting periodontal disease and atherosclerosis are significantly associated.