The use of dried blood spots and a modified particle agglutination test for cost-effective sero-epidemiological HIV surveys

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Sequence-based prediction for vaccine strain selection and identification of antigenic variability in foot-and-mouth disease virus

Identifying when past exposure to an infectious disease will protect against newly emerging strains is central to understanding the spread and the severity of epidemics, but the prediction of viral cross-protection remains an important unsolved problem. For foot-and-mouth disease virus (FMDV) research in particular, improved methods for predicting this cross-protection are critical for predicting the severity of outbreaks within endemic settings where multiple serotypes and subtypes commonly co-circulate, as well as for deciding whether appropriate vaccine(s) exist and how much they could mitigate the effects of any outbreak. To identify antigenic relationships and their predictors, we used linear mixed effects models to account for variation in pairwise cross-neutralization titres using only viral sequences and structural data. We identified those substitutions in surface-exposed structural proteins that are correlates of loss of cross-reactivity. These allowed prediction of both the best vaccine match for any single virus and the breadth of coverage of new vaccine candidates from their capsid sequences as effectively as or better than serology. Sub-sequences chosen by the model-building process all contained sites that are known epitopes on other serotypes. Furthermore, for the SAT1 serotype, for which epitopes have never previously been identified, we provide strong evidence - by controlling for phylogenetic structure - for the presence of three epitopes across a panel of viruses and quantify the relative significance of some individual residues in determining cross-neutralization. Identifying and quantifying the importance of sites that predict viral strain cross-reactivity not just for single viruses but across entire serotypes can help in the design of vaccines with better targeting and broader coverage. These techniques can be generalized to any infectious agents where cross-reactivity assays have been carried out. As the parameterization uses pre-existing datasets, this approach quickly and cheaply increases both our understanding of antigenic relationships and our power to control disease

Homocysteine, Grey Matter and Cognitive Function in Adults with Cardiovascular Disease

Background: Elevated total plasma homocysteine (tHcy) has been associated with cognitive impairment, vascular disease and brain atrophy. Methods: We investigated 150 volunteers to determine if the association between high tHcy and cerebral grey matter volume and cognitive function is independent of cardiovascular disease. Results: Participants with high tHcy ($15 mmol/L) showed a widespread relative loss of grey matter compared with people with normal tHcy, although differences between the groups were minimal once the analyses were adjusted for age, gender, diabetes, hypertension, smoking and prevalent cardiovascular disease. Individuals with high tHcy had worse cognitive scores across a range of domains and less total grey matter volume, although these differences were not significant in the adjusted models. Conclusions: Our results suggest that the association between high tHcy and loss of cerebral grey matter volume and decline in cognitive function is largely explained by increasing age and cardiovascular diseases and indicate that th

Integrative bioinformatics analysis of transcriptional regulatory programs in breast cancer cells

<p>Abstract</p> <p>Background</p> <p>Microarray technology has unveiled transcriptomic differences among tumors of various phenotypes, and, especially, brought great progress in molecular understanding of phenotypic diversity of breast tumors. However, compared with the massive knowledge about the transcriptome, we have surprisingly little knowledge about regulatory mechanisms underling transcriptomic diversity.</p> <p>Results</p> <p>To gain insights into the transcriptional programs that drive tumor progression, we integrated regulatory sequence data and expression profiles of breast cancer into a Bayesian Network, and searched for <it>cis</it>-regulatory motifs statistically associated with given histological grades and prognosis. Our analysis found that motifs bound by ELK1, E2F, NRF1 and NFY are potential regulatory motifs that positively correlate with malignant progression of breast cancer.</p> <p>Conclusion</p> <p>The results suggest that these 4 motifs are principal regulatory motifs driving malignant progression of breast cancer. Our method offers a more concise description about transcriptome diversity among breast tumors with different clinical phenotypes.</p

Fasting blood glucose, glycaemic control and prostate cancer risk in the Finnish Randomized Study of Screening for Prostate Cancer

BACKGROUND: Diabetic men have lowered overall risk of prostate cancer (PCa), but the role of hyperglycaemia is unclear. In this cohort study, we estimated PCa risk among men with diabetic fasting blood glucose level. METHODS: Participants of the Finnish Randomized Study of Screening for Prostate Cancer (FinRSPC) were linked to laboratory database for information on glucose measurements since 1978. The data were available for 17,860 men. Based on the average yearly level, the men were categorised as normoglycaemic, prediabetic, or diabetic. Median follow-up was 14.7 years. Multivariable-adjusted Cox regression was used to calculate hazard ratios (HRs) and 95% confidence intervals (95% CIs) for prostate cancer overall and separately by Gleason grade and metastatic stage. RESULTS: In total 1,663 PCa cases were diagnosed. Compared to normoglycaemic men, those men with diabetic blood glucose level had increased risk of PCa (HR 1.52; 95% CI 1.31-1.75). The risk increase was observed for all tumour grades, and persisted for a decade afterwards. Antidiabetic drug use removed the risk association. Limitations include absence of information on lifestyle factors and limited information on BMI. CONCLUSIONS: Untreated diabetic fasting blood glucose level may be a prostate cancer risk factor.Peer reviewe

Treatment of glenohumeral instability in rugby players

Rugby is a high-impact collision sport, with impact forces. Shoulder injuries are common and result in the longest time off sport for any joint injury in rugby. The most common injuries are to the glenohumeral joint with varying degrees of instability. The degree of instability can guide management. The three main types of instability presentations are: (1) frank dislocation, (2) subluxations and (3) subclinical instability with pain and clicking. Understanding the exact mechanism of injury can guide diagnosis with classical patterns of structural injuries. The standard clinical examination in a large, muscular athlete may be normal, so specific tests and techniques are needed to unearth signs of pathology. Taking these factors into consideration, along with the imaging, allows a treatment strategy. However, patient and sport factors need to be also considered, particularly the time of the season and stage of sporting career. Surgery to repair the structural damage should include all lesions found. In chronic, recurrent dislocations with major structural lesions, reconstruction procedures such as the Latarjet procedure yields better outcomes. Rehabilitation should be safe, goal-driven and athlete- specific. Return to sport is dependent on a number of factors, driven by the healing process, sport requirements and extrinsic pressures

Claims about the Misuse of Insecticide-Treated Mosquito Nets: Are These Evidence-Based?

Thomas Eisele and colleagues dispute reports in the media and elsewhere that insecticide-treated nets are not widely used, or are misused, and say that such misconceptions are not evidence-based and are damaging to malaria control efforts

TRY plant trait database - enhanced coverage and open access

Plant traits-the morphological, anatomical, physiological, biochemical and phenological characteristics of plants-determine how plants respond to environmental factors, affect other trophic levels, and influence ecosystem properties and their benefits and detriments to people. Plant trait data thus represent the basis for a vast area of research spanning from evolutionary biology, community and functional ecology, to biodiversity conservation, ecosystem and landscape management, restoration, biogeography and earth system modelling. Since its foundation in 2007, the TRY database of plant traits has grown continuously. It now provides unprecedented data coverage under an open access data policy and is the main plant trait database used by the research community worldwide. Increasingly, the TRY database also supports new frontiers of trait-based plant research, including the identification of data gaps and the subsequent mobilization or measurement of new data. To support this development, in this article we evaluate the extent of the trait data compiled in TRY and analyse emerging patterns of data coverage and representativeness. Best species coverage is achieved for categorical traits-almost complete coverage for 'plant growth form'. However, most traits relevant for ecology and vegetation modelling are characterized by continuous intraspecific variation and trait-environmental relationships. These traits have to be measured on individual plants in their respective environment. Despite unprecedented data coverage, we observe a humbling lack of completeness and representativeness of these continuous traits in many aspects. We, therefore, conclude that reducing data gaps and biases in the TRY database remains a key challenge and requires a coordinated approach to data mobilization and trait measurements. This can only be achieved in collaboration with other initiatives

Adjustment for time-invariant and time-varying confounders in ‘unexplained residuals’ models for longitudinal data within a causal framework and associated challenges

‘Unexplained residuals’ models have been used within lifecourse epidemiology to model an exposure measured longitudinally at several time points in relation to a distal outcome. It has been claimed that these models have several advantages, including: the ability to estimate multiple total causal effects in a single model, and additional insight into the effect on the outcome of greater-than-expected increases in the exposure compared to traditional regression methods. We evaluate these properties and prove mathematically how adjustment for confounding variables must be made within this modelling framework. Importantly, we explicitly place unexplained residual models in a causal framework using directed acyclic graphs. This allows for theoretical justification of appropriate confounder adjustment and provides a framework for extending our results to more complex scenarios than those examined in this paper. We also discuss several interpretational issues relating to unexplained residual models within a causal framework. We argue that unexplained residual models offer no additional insights compared to traditional regression methods, and, in fact, are more challenging to implement; moreover, they artificially reduce estimated standard errors. Consequently, we conclude that unexplained residual models, if used, must be implemented with great care