Fat and Carbohydrate Intake over Three Generations Modify Growth,Metabolism and Cardiovascular Phenotype in Female Mice in an Age-Related Manner

Environmental challenges such as a high fat diet during pregnancy can induce changes in offspring growth, metabolism and cardiovascular function. However, challenges that are sustained over several generations can induce progressive compensatory metabolic adjustments in young adults. It is not known if such effects persist during ageing. We investigated whether diets with different fat and carbohydrate contents over three generations modifies markers of ageing. Female C57BL/6 F0 mice were fed diets containing 5% or 21% fat (w/w) throughout pregnancy and lactation. Female offspring were fed the same diet as their dams until the F3 generation. In each generation, body weight, 24-hour food intake were recorded weekly, and plasma metabolites were measured by colorimetric assays, blood pressure by tail cuff plethysmography and vasoconstriction by myography on postnatal day 90 or 456. There was little effect of diet or generation on phenotypic markers in day 90 adults. There was a significant increase in whole body, liver and heart weight with ageing (d456) in the F3 21% fat group compared to the F1 and F3 5% groups. Fasting plasma glucose concentration was significantly increased with ageing in the 5% group in the F3 generation and in the 21% group in both generations. There was a significant effect of diet and generation on ex-vivo vasoconstriction in ageing females. Differences in dietary fat may induce metabolic compensation in young adults that persist over three generations. However, such compensatory effects decline during ageing.     

Measuring the performance of vaccination programs using cross-sectional surveys: a likelihood framework and retrospective analysis

Background

The performance of routine and supplemental immunization activities is usually measured by the administrative method: dividing the number of doses distributed by the size of the target population. This method leads to coverage estimates that are sometimes impossible (e.g., vaccination of 102% of the target population), and are generally inconsistent with the proportion found to be vaccinated in Demographic and Health Surveys (DHS). We describe a method that estimates the fraction of the population accessible to vaccination activities, as well as within-campaign inefficiencies, thus providing a consistent estimate of vaccination coverage.

Methods and Findings

We developed a likelihood framework for estimating the effective coverage of vaccination programs using cross-sectional surveys of vaccine coverage combined with administrative data. We applied our method to measles vaccination in three African countries: Ghana, Madagascar, and Sierra Leone, using data from each country''s most recent DHS survey and administrative coverage data reported to the World Health Organization. We estimate that 93% (95% CI: 91, 94) of the population in Ghana was ever covered by any measles vaccination activity, 77% (95% CI: 78, 81) in Madagascar, and 69% (95% CI: 67, 70) in Sierra Leone. “Within-activity” inefficiencies were estimated to be low in Ghana, and higher in Sierra Leone and Madagascar. Our model successfully fits age-specific vaccination coverage levels seen in DHS data, which differ markedly from those predicted by naïve extrapolation from country-reported and World Health Organization–adjusted vaccination coverage.

Conclusions

Combining administrative data with survey data substantially improves estimates of vaccination coverage. Estimates of the inefficiency of past vaccination activities and the proportion not covered by any activity allow us to more accurately predict the results of future activities and provide insight into the ways in which vaccination programs are failing to meet their goals. Please see later in the article for the Editors'' Summary     

Spatial Transmission of 2009 Pandemic Influenza in the US

The 2009 H1N1 influenza pandemic provides a unique opportunity for detailed examination of the spatial dynamics of an emerging pathogen. In the US, the pandemic was characterized by substantial geographical heterogeneity: the 2009 spring wave was limited mainly to northeastern cities while the larger fall wave affected the whole country. Here we use finely resolved spatial and temporal influenza disease data based on electronic medical claims to explore the spread of the fall pandemic wave across 271 US cities and associated suburban areas. We document a clear spatial pattern in the timing of onset of the fall wave, starting in southeastern cities and spreading outwards over a period of three months. We use mechanistic models to tease apart the external factors associated with the timing of the fall wave arrival: differential seeding events linked to demographic factors, school opening dates, absolute humidity, prior immunity from the spring wave, spatial diffusion, and their interactions. Although the onset of the fall wave was correlated with school openings as previously reported, models including spatial spread alone resulted in better fit. The best model had a combination of the two. Absolute humidity or prior exposure during the spring wave did not improve the fit and population size only played a weak role. In conclusion, the protracted spread of pandemic influenza in fall 2009 in the US was dominated by short-distance spatial spread partially catalysed by school openings rather than long-distance transmission events. This is in contrast to the rapid hierarchical transmission patterns previously described for seasonal influenza. The findings underline the critical role that school-age children play in facilitating the geographic spread of pandemic influenza and highlight the need for further information on the movement and mixing patterns of this age group.     

Newly Established Monoclonal Antibody Diagnostic Assays for Schistosoma mansoni Direct Detection in Areas of Low Endemicity

Background

Current available methods for diagnosis of schistosomiasis mansoni lack sufficient sensitivity, which results in underreporting of infectious in areas of low endemicity.

Methodology/Principal Findings

We developed three novel diagnostic methodologies for the direct detection of schistosome infection in serum samples. These three new methods were evaluated with positive patients from a low endemicity area in southeast Brazil. The basis of the assay was the production of monoclonal antibodies against the protein backbone of heavily glycosylated Circulating Cathodic Antigen (CCA). The antibodies were also selected for having no specificity to repeating poly-Lewis x units. Assays based on the detection CCA-protein should not encounter a limitation in sensitivity due to a biological background of this particular epitope. Three diagnostic methodologies were developed and validated, (i) Immunomagnetic Separation based on improved incubation steps of non-diluted serum, (ii) Direct Enzyme-linked Immunosorbent Assay and (iii) Fluorescent Microscopy Analysis as a qualitative assay. The two quantitative assays presented high sensitivity (94% and 92%, respectively) and specificity (100%), equivalent to the analysis of 3 stool samples and 16 slides by Kato-Katz, showing promising results on the determination of cure.

Conclusions/Significance

The Immunomagnetic Separation technique showed excellent correlation with parasite burden by Cohen coefficient. The qualitative method detected 47 positive individuals out of 50 with the analysis of 3 slides. This easy-to-do method was capable of discriminating positive from negative cases, even for patients with low parasite burden.     

Demonstrating the Use of High-Volume Electronic Medical Claims Data to Monitor Local and Regional Influenza Activity in the US

Introduction

Fine-grained influenza surveillance data are lacking in the US, hampering our ability to monitor disease spread at a local scale. Here we evaluate the performances of high-volume electronic medical claims data to assess local and regional influenza activity.

Material and Methods

We used electronic medical claims data compiled by IMS Health in 480 US locations to create weekly regional influenza-like-illness (ILI) time series during 2003–2010. IMS Health captured 62% of US outpatient visits in 2009. We studied the performances of IMS-ILI indicators against reference influenza surveillance datasets, including CDC-ILI outpatient and laboratory-confirmed influenza data. We estimated correlation in weekly incidences, peak timing and seasonal intensity across datasets, stratified by 10 regions and four age groups (<5, 5–29, 30–59, and 60+ years). To test IMS-Health performances at the city level, we compared IMS-ILI indicators to syndromic surveillance data for New York City. We also used control data on laboratory-confirmed Respiratory Syncytial Virus (RSV) activity to test the specificity of IMS-ILI for influenza surveillance.

Results

Regional IMS-ILI indicators were highly synchronous with CDC''s reference influenza surveillance data (Pearson correlation coefficients rho≥0.89; range across regions, 0.80–0.97, P<0.001). Seasonal intensity estimates were weakly correlated across datasets in all age data (rho≤0.52), moderately correlated among adults (rho≥0.64) and uncorrelated among school-age children. IMS-ILI indicators were more correlated with reference influenza data than control RSV indicators (rho = 0.93 with influenza v. rho = 0.33 with RSV, P<0.05). City-level IMS-ILI indicators were highly consistent with reference syndromic data (rho≥0.86).

Conclusion

Medical claims-based ILI indicators accurately capture weekly fluctuations in influenza activity in all US regions during inter-pandemic and pandemic seasons, and can be broken down by age groups and fine geographical areas. Medical claims data provide more reliable and fine-grained indicators of influenza activity than other high-volume electronic algorithms and should be used to augment existing influenza surveillance systems.     

Impact of natural enemies on obligately cooperative breeders

Obligately cooperative breeders (cooperators) display a negative growth rate once they fall below a minimum density. Constraints imposed by natural enemies, such as predators or competitors, may push cooperator groups closer to this threshold, thus increasing the risk that stochastic fluctuations will drive them below it. This may indirectly drive these groups to extinction, thereby increasing the risk of population extinction. In this paper, we construct mathematical models of the dynamics of groups of cooperators and non-cooperators in the presence of two types of enemies: enemies whose dynamics do not depend on the dynamics of their victim (e.g., amensal competitor, generalist predator) and those whose dynamics do. In the latter case, we distinguish positive (e.g., specialist predator) and negative (e.g., bilateral competitor) reciprocal effects. These models correspond to the classical amensal, predation and competition models, in the presence of an Allee effect. We then develop the models to study consequences at the population level. By comparing models with or without an Allee effect, we show that enemies decrease the group size of cooperators more than that of non-cooperators, and this increases their group extinction risk. We also demonstrate how an Allee effect at a lower dynamical level can have consequences at a higher level: inverse density dependence at the group level generated lower population sizes and higher risks of population extinction. Our results also suggest that demographic compensation can be achieved by cooperators through an increased intrinsic growth rate, or by decreasing the enemy constraint. Both of these types of compensation have been observed in empirical studies of cooperators.     

Changes in Rodent Abundance and Weather Conditions Potentially Drive Hemorrhagic Fever with Renal Syndrome Outbreaks in Xi’an,China, 2005–2012

BackgroundIncreased risks for hemorrhagic fever with renal syndrome (HFRS) caused by Hantaan virus have been observed since 2005, in Xi’an, China. Despite increased vigilance and preparedness, HFRS outbreaks in 2010, 2011, and 2012 were larger than ever, with a total of 3,938 confirmed HFRS cases and 88 deaths in 2010 and 2011.ConclusionsIn addition to a strong seasonal pattern, HFRS incidence was correlated with rodent density and rainfall, indicating that they potentially drive the HFRS outbreaks. Future work should aim to determine the mechanism underlying the seasonal pattern and autocorrelation. However, this model can be useful in risk management to provide early warning of potential outbreaks of this disease.     

Antigenic extracts of Leishmania braziliensis and Leishmania amazonensis associated with saponin partially protects BALB/c mice against Leishmania chagasi infection by suppressing IL-10 and IL-4 production

This study evaluated two vaccine candidates for their effectiveness in protecting BALB/c mice against Leishmania chagasi infection. These immunogenic preparations were composed of Leishmania amazonensis or Leishmania braziliensis antigenic extracts in association with saponin adjuvant. Mice were given three subcutaneous doses of one of these vaccine candidates weekly for three weeks and four weeks later challenged with promastigotes of L. chagasi by intravenous injection. We observed that both vaccine candidates induced a significant reduction in the parasite load of the liver, while the L. amazonensis antigenic extract also stimulated a reduction in spleen parasite load. This protection was associated with a suppression of both interleukin (IL)-10 and IL-4 cytokines by spleen cells in response to L. chagasi antigen. No change was detected in the production of IFN-γ. Our data show that these immunogenic preparations reduce the type 2 immune response leading to the control of parasite replication.     

Trip duration drives shift in travel network structure with implications for the predictability of spatial disease spread

Human travel is one of the primary drivers of infectious disease spread. Models of travel are often used that assume the amount of travel to a specific destination decreases as cost of travel increases with higher travel volumes to more populated destinations. Trip duration, the length of time spent in a destination, can also impact travel patterns. We investigated the spatial patterns of travel conditioned on trip duration and find distinct differences between short and long duration trips. In short-trip duration travel networks, trips are skewed towards urban destinations, compared with long-trip duration networks where travel is more evenly spread among locations. Using gravity models to inform connectivity patterns in simulations of disease transmission, we show that pathogens with shorter generation times exhibit initial patterns of spatial propagation that are more predictable among urban locations. Further, pathogens with a longer generation time have more diffusive patterns of spatial spread reflecting more unpredictable disease dynamics.     

Measles on the edge: coastal heterogeneities and infection dynamics

Mathematical models can help elucidate the spatio-temporal dynamics of epidemics as well as the impact of control measures. The gravity model for directly transmitted diseases is currently one of the most parsimonious models for spatial epidemic spread. This model uses distance-weighted, population size-dependent coupling to estimate host movement and disease incidence in metapopulations. The model captures overall measles dynamics in terms of underlying human movement in pre-vaccination England and Wales (previously established). In spatial models, edges often present a special challenge. Therefore, to test the model's robustness, we analyzed gravity model incidence predictions for coastal cities in England and Wales. Results show that, although predictions are accurate for inland towns, they significantly underestimate coastal persistence. We examine incidence, outbreak seasonality, and public transportation records, to show that the model's inaccuracies stem from an underestimation of total contacts per individual along the coast. We rescue this predicted 'edge effect' by increasing coastal contacts to approximate the number of per capita inland contacts. These results illustrate the impact of 'edge effects' on epidemic metapopulations in general and illustrate directions for the refinement of spatiotemporal epidemic models.