The mental health disaster in conflict settings: Can scientific research help?

Neuner F, Elbert T. The mental health disaster in conflict settings: Can scientific research help? BMC Public Health. 2007;7(1): 275

Quantifying the efficiency of hydroxyapatite mineralising peptides

We present a non-destructive analytical calibration tool to allow quantitative assessment of individual calcium phosphates such as hydroxyapatite (HAP) from mixtures including brushite. Many experimental approaches are used to evaluate the mineralising capabilities of biomolecules including peptides. However, it is difficult to quantitatively compare the efficacy of peptides in the promotion of mineralisation when inseparable mixtures of different minerals are produced. To address this challenge, a series of hydroxyapatite and brushite mixtures were produced as a percent/weight (0–100%) from pure components and multiple (N=10) XRD patterns were collected for each mixture. A linear relationship between the ratio of selected peak heights and the molar ratio was found. Using this method, the mineralising capabilities of three known hydroxyapatite binding peptides, CaP(S) STLPIPHEFSRE, CaP(V) VTKHLNQISQSY and CaP(H) SVSVGMKPSPRP, was compared. All three directed mineralisation towards hydroxyapatite in a peptide concentration dependent manner. CaP(V) was most effective at inducing hydroxyapatite formation at higher reagent levels (Ca2+ = 200mM), as also seen with peptide-silk chimeric materials, whereas CaP(S) was most effective when lower concentrations of calcium (20mM) and phosphate were used. The approach can be extended to investigate HAP mineralisation in the presence of any number of mineralisation promoters or inhibitors

Genotype by environment interaction for 450-day weight of Nelore cattle analyzed by reaction norm models

Genotype by environment interactions (GEI) have attracted increasing attention in tropical breeding programs because of the variety of production systems involved. In this work, we assessed GEI in 450-day adjusted weight (W450) Nelore cattle from 366 Brazilian herds by comparing traditional univariate single-environment model analysis (UM) and random regression first order reaction norm models for six environmental variables: standard deviations of herd-year (RRMw) and herd-year-season-management (RRMw-m) groups for mean W450, standard deviations of herd-year (RRMg) and herd-year-season-management (RRMg-m) groups adjusted for 365-450 days weight gain (G450) averages, and two iterative algorithms using herd-year-season-management group solution estimates from a first RRMw-m and RRMg-m analysis (RRMITw-m and RRMITg-m, respectively). The RRM results showed similar tendencies in the variance components and heritability estimates along environmental gradient. Some of the variation among RRM estimates may have been related to the precision of the predictor and to correlations between environmental variables and the likely components of the weight trait. GEI, which was assessed by estimating the genetic correlation surfaces, had values < 0.5 between extreme environments in all models. Regression analyses showed that the correlation between the expected progeny differences for UM and the corresponding differences estimated by RRM was higher in intermediate and favorable environments than in unfavorable environments (p < 0.0001)

Genetic and Anatomic Determinants of Enzootic Venezuelan Equine Encephalitis Virus Infection of Culex (Melanoconion) taeniopus

Venezuelan equine encephalitis (VEE) is a re-emerging, mosquito-borne viral disease with the potential to cause fatal encephalitis in both humans and equids. Recently, detection of endemic VEE caused by enzootic strains has escalated in Mexico, Peru, Bolivia, Colombia and Ecuador, emphasizing the importance of understanding the enzootic transmission cycle of the etiologic agent, VEE virus (VEEV). The majority of work examining the viral determinants of vector infection has been performed in the epizootic mosquito vector, Aedes (Ochlerotatus) taeniorhynchus. Based on the fundamental differences between the epizootic and enzootic cycles, we hypothesized that the virus-vector interaction of the enzootic cycle is fundamentally different from that of the epizootic model. We therefore examined the determinants for VEEV IE infection in the enzootic vector, Culex (Melanoconion) taeniopus, and determined the number and susceptibility of midgut epithelial cells initially infected and their distribution compared to the epizootic virus-vector interaction. Using chimeric viruses, we demonstrated that the determinants of infection for the enzootic vector are different than those observed for the epizootic vector. Similarly, we showed that, unlike A. taeniorhynchus infection with subtype IC VEEV, C. taeniopus does not have a limited subpopulation of midgut cells susceptible to subtype IE VEEV. These findings support the hypothesis that the enzootic VEEV relationship with C. taeniopus differs from the epizootic virus-vector interaction in that the determinants appear to be found in both the nonstructural and structural regions, and initial midgut infection is not limited to a small population of susceptible cells

The effect on mental health of a large scale psychosocial intervention for survivors of mass violence: A quasi-experimental study in Rwanda

Background: War has serious and prolonged mental health consequences. It is argued that post-emergency mental health interventions should not only focus on psychological factors but also address the social environment. No controlled trials of such interventions exist. We studied the effect on mental health of a large scale p

Post-conflict mental health needs: a cross-sectional survey of trauma, depression and associated factors in Juba, Southern Sudan

BACKGROUND: The signing of the Comprehensive Peace Agreement in January 2005 marked the end of the civil conflict in Sudan lasting over 20 years. The conflict was characterised by widespread violence and large-scale forced migration. Mental health is recognised as a key public health issue for conflict-affected populations. Studies revealed high levels of post-traumatic stress disorder (PTSD) amongst populations from Southern Sudan during the conflict. However, no studies have been conducted on mental health in post-war Southern Sudan. The objective of this study was to measure PTSD and depression in the population in the town of Juba in Southern Sudan; and to investigate the association ofdemographic, displacement, and past and recent trauma exposure variables, on the outcomes of PTSD and depression. METHODS: A cross-sectional, random cluster survey with a sample of 1242 adults (aged over 18 years) was conducted in November 2007 in the town of Juba, the capital of Southern Sudan. Levels of exposure to traumatic events and PTSD were measured using the Harvard Trauma Questionnaire (original version), and levels of depression measured using the Hopkins Symptom Checklist-25. Multivariate logistic regression was used to analyse the association ofdemographic, displacement and trauma exposure variables on the outcomes of PTSD and depression. Multivariate logistic regression was also conducted to investigate which demographic and displacement variables were associated with exposure to traumatic events. RESULTS: Over one third (36%) of respondents met symptom criteria for PTSD and half (50%) of respondents met symptom criteria for depression. The multivariate logistic regression analysis showed strong associations of gender, marital status, forced displacement, and trauma exposure with outcomes of PTSD and depression. Men, IDPs, and refugees and persons displaced more than once were all significantly more likely to have experienced eight or more traumatic events. CONCLUSION: This study provides evidence of high levels of mental distress in the population of Juba Town, and associated risk-factors. Comprehensive social and psychological assistance is urgently required in Juba

Accurate and efficient gp120 V3 loop structure based models for the determination of HIV-1 co-receptor usage

<p>Abstract</p> <p>Background</p> <p>HIV-1 targets human cells expressing both the CD4 receptor, which binds the viral envelope glycoprotein gp120, as well as either the CCR5 (R5) or CXCR4 (X4) co-receptors, which interact primarily with the third hypervariable loop (V3 loop) of gp120. Determination of HIV-1 affinity for either the R5 or X4 co-receptor on host cells facilitates the inclusion of co-receptor antagonists as a part of patient treatment strategies. A dataset of 1193 distinct gp120 V3 loop peptide sequences (989 R5-utilizing, 204 X4-capable) is utilized to train predictive classifiers based on implementations of random forest, support vector machine, boosted decision tree, and neural network machine learning algorithms. An <it>in silico </it>mutagenesis procedure employing multibody statistical potentials, computational geometry, and threading of variant V3 sequences onto an experimental structure, is used to generate a feature vector representation for each variant whose components measure environmental perturbations at corresponding structural positions.</p> <p>Results</p> <p>Classifier performance is evaluated based on stratified 10-fold cross-validation, stratified dataset splits (2/3 training, 1/3 validation), and leave-one-out cross-validation. Best reported values of sensitivity (85%), specificity (100%), and precision (98%) for predicting X4-capable HIV-1 virus, overall accuracy (97%), Matthew's correlation coefficient (89%), balanced error rate (0.08), and ROC area (0.97) all reach critical thresholds, suggesting that the models outperform six other state-of-the-art methods and come closer to competing with phenotype assays.</p> <p>Conclusions</p> <p>The trained classifiers provide instantaneous and reliable predictions regarding HIV-1 co-receptor usage, requiring only translated V3 loop genotypes as input. Furthermore, the novelty of these computational mutagenesis based predictor attributes distinguishes the models as orthogonal and complementary to previous methods that utilize sequence, structure, and/or evolutionary information. The classifiers are available online at <url>http://proteins.gmu.edu/automute</url>.</p