Cardiac function associated with previous, current and repeated depression and anxiety symptoms in a healthy population: the HUNT study.

Objective: Symptoms of anxiety and depression often co-exist with cardiovascular disease (CVD), yet little is known about the association with left ventricular (LV) subclinical dysfunction. We aimed to study the cross-sectional associations of previous, current and repeated depression or anxiety symptoms, with sensitive indices of LV systolic and diastolic function, based on tissue Doppler (TD) and speckle tracking (ST) imaging methods. Methods: A random selection of 1296 individuals free from known CVD, hypertension and diabetes were examined with echocardiography at baseline of the third Nord-Trøndelag Health Study, (HUNT3, 2006–2008). The primary outcomes were LV diastolic function (e′) and LV systolic function (longitudinal global strain). The primary exposures were self-report on the Hospital Anxiety and Depression Scale (HADS). Associations between outcomes and baseline exposures were available for 1034 (80%), and with previous and repeated exposures for 700 participants who also participated in HUNT2 (1995–1997). Results: Previous and repeated depression symptoms, but not current depression, were linearly associated with a reduction in e′. The average sum of two repeated HADS-D scores 10 years apart had the strongest effect on e′ (−8.3%; 95% CI −13.9% to −2.7%) per 5 units. We observed a sex difference between depression symptoms and longitudinal global strain (p for interaction 0.019), where women had a marginal negative effect. Anxiety symptoms, neither previous, current nor repeated were associated with subclinical LV dysfunction. Conclusions: In a healthy sample, confirmed free of CVD, past and repeated depression symptoms were associated with subclinical LV dysfunction. Thus, depression symptoms might represent a modifiable risk factor for future CVD.This is an Open Access article distributed in accordance with the Creative Commons Attribution Non Commercial (CC BY-NC 4.0) license, which permits others to distribute, remix, adapt, build upon this work non-commercially, and license their derivative works on different terms, provided the original work is properly cited and the use is non-commercial. See: http://creativecommons.org/licenses/by-nc/4.0

Measuring Inequalities in the Distribution of Health Workers: The case of Tanzania.

The overall human resource shortages and the distributional inequalities in the health workforce in many developing countries are well acknowledged. However, little has been done to measure the degree of inequality systematically. Moreover, few attempts have been made to analyse the implications of using alternative measures of health care needs in the measurement of health workforce distributional inequalities. Most studies have implicitly relied on population levels as the only criterion for measuring health care needs. This paper attempts to achieve two objectives. First, it describes and measures health worker distributional inequalities in Tanzania on a per capita basis; second, it suggests and applies additional health care needs indicators in the measurement of distributional inequalities. We plotted Lorenz and concentration curves to illustrate graphically the distribution of the total health workforce and the cadre-specific (skill mix) distributions. Alternative indicators of health care needs were illustrated by concentration curves. Inequalities were measured by calculating Gini and concentration indices.\ud There are significant inequalities in the distribution of health workers per capita. Overall, the population quintile with the fewest health workers per capita accounts for only 8% of all health workers, while the quintile with the most health workers accounts for 46%. Inequality is perceptible across both urban and rural districts. Skill mix inequalities are also large. Districts with a small share of the health workforce (relative to their population levels have an even smaller share of highly trained medical personnel. A small share of highly trained personnel is compensated by a larger share of clinical officers (a middle-level cadre) but not by a larger share of untrained health workers. Clinical officers are relatively equally distributed. Distributional inequalities tend to be more pronounced when under-five deaths are used as an indicator of health care needs. Conversely, if health care needs are measured by HIV prevalence, the distributional inequalities appear to decline. The measure of inequality in the distribution of the health workforce may depend strongly on the underlying measure of health care needs. In cases of a non-uniform distribution of health care needs across geographical areas, other measures of health care needs than population levels may have to be developed in order to ensure a more meaningful measurement of distributional inequalities of the health workforce

The importance of temperature fluctuations in understanding mosquito population dynamics and malaria risk

Temperature is a key environmental driver of Anopheles mosquito population dynamics; understanding its central role is important for these malaria vectors. Mosquito population responses to temperature fluctuations, though important across the life history, are poorly understood at a population level. We used stage-structured, temperature-dependent delay-differential equations to conduct a detailed exploration of the impacts of diurnal and annual temperature fluctuations on mosquito population dynamics. The model allows exploration of temperature-driven temporal changes in adult age structure, giving insights into the population’s capacity to vector malaria parasites. Because of temperature-dependent shifts in age structure, the abundance of potentially infectious mosquitoes varies temporally, and does not necessarily mirror the dynamics of the total adult population. In addition to conducting the first comprehensive theoretical exploration of fluctuating temperatures on mosquito population dynamics, we analysed observed temperatures at four locations in Africa covering a range of environmental conditions. We found both temperature and precipitation are needed to explain the observed malaria season in these locations, enhancing our understanding of the drivers of malaria seasonality and how temporal disease risk may shift in response to temperature changes. This approach, tracking both mosquito abundance and age structure, may be a powerful tool for understanding current and future malaria risk

The dynamics of measles in sub-Saharan Africa.

Although vaccination has almost eliminated measles in parts of the world, the disease remains a major killer in some high birth rate countries of the Sahel. On the basis of measles dynamics for industrialized countries, high birth rate regions should experience regular annual epidemics. Here, however, we show that measles epidemics in Niger are highly episodic, particularly in the capital Niamey. Models demonstrate that this variability arises from powerful seasonality in transmission-generating high amplitude epidemics-within the chaotic domain of deterministic dynamics. In practice, this leads to frequent stochastic fadeouts, interspersed with irregular, large epidemics. A metapopulation model illustrates how increased vaccine coverage, but still below the local elimination threshold, could lead to increasingly variable major outbreaks in highly seasonally forced contexts. Such erratic dynamics emphasize the importance both of control strategies that address build-up of susceptible individuals and efforts to mitigate the impact of large outbreaks when they occur

A proposed method to investigate reliability throughout a questionnaire

<p>Abstract</p> <p>Background</p> <p>Questionnaires are used extensively in medical and health care research and depend on validity and reliability. However, participants may differ in interest and awareness throughout long questionnaires, which can affect reliability of their answers. A method is proposed for "screening" of systematic change in random error, which could assess changed reliability of answers.</p> <p>Methods</p> <p>A simulation study was conducted to explore whether systematic change in reliability, expressed as changed random error, could be assessed using unsupervised classification of subjects by cluster analysis (CA) and estimation of intraclass correlation coefficient (ICC). The method was also applied on a clinical dataset from 753 cardiac patients using the Jalowiec Coping Scale.</p> <p>Results</p> <p>The simulation study showed a relationship between the systematic change in random error throughout a questionnaire and the slope between the estimated ICC for subjects classified by CA and successive items in a questionnaire. This slope was proposed as an awareness measure - to assessing if respondents provide only a random answer or one based on a substantial cognitive effort. Scales from different factor structures of Jalowiec Coping Scale had different effect on this awareness measure.</p> <p>Conclusions</p> <p>Even though assumptions in the simulation study might be limited compared to real datasets, the approach is promising for assessing systematic change in reliability throughout long questionnaires. Results from a clinical dataset indicated that the awareness measure differed between scales.</p

Relations between lipoprotein(a) concentrations, LPA genetic variants, and the risk of mortality in patients with established coronary heart disease: a molecular and genetic association study

Background: Lipoprotein(a) concentrations in plasma are associated with cardiovascular risk in the general population. Whether lipoprotein(a) concentrations or LPA genetic variants predict long-term mortality in patients with established coronary heart disease remains less clear. Methods: We obtained data from 3313 patients with established coronary heart disease in the Ludwigshafen Risk and Cardiovascular Health (LURIC) study. We tested associations of tertiles of lipoprotein(a) concentration in plasma and two LPA single-nucleotide polymorphisms ([SNPs] rs10455872 and rs3798220) with all-cause mortality and cardiovascular mortality by Cox regression analysis and with severity of disease by generalised linear modelling, with and without adjustment for age, sex, diabetes diagnosis, systolic blood pressure, BMI, smoking status, estimated glomerular filtration rate, LDL-cholesterol concentration, and use of lipid-lowering therapy. Results for plasma lipoprotein(a) concentrations were validated in five independent studies involving 10 195 patients with established coronary heart disease. Results for genetic associations were replicated through large-scale collaborative analysis in the GENIUS-CHD consortium, comprising 106 353 patients with established coronary heart disease and 19 332 deaths in 22 studies or cohorts. Findings: The median follow-up was 9·9 years. Increased severity of coronary heart disease was associated with lipoprotein(a) concentrations in plasma in the highest tertile (adjusted hazard radio [HR] 1·44, 95% CI 1·14–1·83) and the presence of either LPA SNP (1·88, 1·40–2·53). No associations were found in LURIC with all-cause mortality (highest tertile of lipoprotein(a) concentration in plasma 0·95, 0·81–1·11 and either LPA SNP 1·10, 0·92–1·31) or cardiovascular mortality (0·99, 0·81–1·2 and 1·13, 0·90–1·40, respectively) or in the validation studies. Interpretation: In patients with prevalent coronary heart disease, lipoprotein(a) concentrations and genetic variants showed no associations with mortality. We conclude that these variables are not useful risk factors to measure to predict progression to death after coronary heart disease is established. Funding: Seventh Framework Programme for Research and Technical Development (AtheroRemo and RiskyCAD), INTERREG IV Oberrhein Programme, Deutsche Nierenstiftung, Else-Kroener Fresenius Foundation, Deutsche Stiftung für Herzforschung, Deutsche Forschungsgemeinschaft, Saarland University, German Federal Ministry of Education and Research, Willy Robert Pitzer Foundation, and Waldburg-Zeil Clinics Isny

Cell cycle-specific UNG2 phosphorylations regulate protein turnover, activity and association with RPA

Human UNG2 is a multifunctional glycosylase that removes uracil near replication forks and in non-replicating DNA, and is important for affinity maturation of antibodies in B cells. How these diverse functions are regulated remains obscure. Here, we report three new phosphoforms of the non-catalytic domain that confer distinct functional properties to UNG2. These are apparently generated by cyclin-dependent kinases through stepwise phosphorylation of S23, T60 and S64 in the cell cycle. Phosphorylation of S23 in late G1/early S confers increased association with replication protein A (RPA) and replicating chromatin and markedly increases the catalytic turnover of UNG2. Conversely, progressive phosphorylation of T60 and S64 throughout S phase mediates reduced binding to RPA and flag UNG2 for breakdown in G2 by forming a cyclin E/c-myc-like phosphodegron. The enhanced catalytic turnover of UNG2 p-S23 likely optimises the protein to excise uracil along with rapidly moving replication forks. Our findings may aid further studies of how UNG2 initiates mutagenic rather than repair processing of activation-induced deaminase-generated uracil at Ig loci in B cells

Multivariate Statistical Analyses Demonstrate Unique Host Immune Responses to Single and Dual Lentiviral Infection

Feline immunodeficiency virus (FIV) and human immunodeficiency virus (HIV) are recently identified lentiviruses that cause progressive immune decline and ultimately death in infected cats and humans. It is of great interest to understand how to prevent immune system collapse caused by these lentiviruses. We recently described that disease caused by a virulent FIV strain in cats can be attenuated if animals are first infected with a feline immunodeficiency virus derived from a wild cougar. The detailed temporal tracking of cat immunological parameters in response to two viral infections resulted in high-dimensional datasets containing variables that exhibit strong co-variation. Initial analyses of these complex data using univariate statistical techniques did not account for interactions among immunological response variables and therefore potentially obscured significant effects between infection state and immunological parameters.Here, we apply a suite of multivariate statistical tools, including Principal Component Analysis, MANOVA and Linear Discriminant Analysis, to temporal immunological data resulting from FIV superinfection in domestic cats. We investigated the co-variation among immunological responses, the differences in immune parameters among four groups of five cats each (uninfected, single and dual infected animals), and the "immune profiles" that discriminate among them over the first four weeks following superinfection. Dual infected cats mount an immune response by 24 days post superinfection that is characterized by elevated levels of CD8 and CD25 cells and increased expression of IL4 and IFNgamma, and FAS. This profile discriminates dual infected cats from cats infected with FIV alone, which show high IL-10 and lower numbers of CD8 and CD25 cells.Multivariate statistical analyses demonstrate both the dynamic nature of the immune response to FIV single and dual infection and the development of a unique immunological profile in dual infected cats, which are protected from immune decline