Interrupting Malaria Transmission: Quantifying the Impact of Interventions in Regions of Low to Moderate Transmission

Malaria has been eliminated from over 40 countries with an additional 39 currently planning for, or committed to, elimination. Information on the likely impact of available interventions, and the required time, is urgently needed to help plan resource allocation. Mathematical modelling has been used to investigate the impact of various interventions; the strength of the conclusions is boosted when several models with differing formulation produce similar data. Here we predict by using an individual-based stochastic simulation model of seasonal Plasmodium falciparum transmission that transmission can be interrupted and parasite reintroductions controlled in villages of 1,000 individuals where the entomological inoculation rate is <7 infectious bites per person per year using chemotherapy and bed net strategies. Above this transmission intensity bed nets and symptomatic treatment alone were not sufficient to interrupt transmission and control the importation of malaria for at least 150 days. Our model results suggest that 1) stochastic events impact the likelihood of successfully interrupting transmission with large variability in the times required, 2) the relative reduction in morbidity caused by the interventions were age-group specific, changing over time, and 3) the post-intervention changes in morbidity were larger than the corresponding impact on transmission. These results generally agree with the conclusions from previously published models. However the model also predicted changes in parasite population structure as a result of improved treatment of symptomatic individuals; the survival probability of introduced parasites reduced leading to an increase in the prevalence of sub-patent infections in semi-immune individuals. This novel finding requires further investigation in the field because, if confirmed, such a change would have a negative impact on attempts to eliminate the disease from areas of moderate transmission

Hemoglobin E syndromes in Pakistani population

<p>Abstract</p> <p>Background</p> <p>Hemoglobin E is an important hemoglobin variant with a worldwide distribution. A number of hemoglobinopathies have been reported from Pakistan. However a comprehensive description of hemoglobin E syndromes for the country was never made. This study aimed to describe various hemoglobin E disorders based on hematological parameters and chromatography. The sub-aim was to characterize hemoglobin E at molecular level.</p> <p>Methods</p> <p>This was a hospital based study conducted prospectively for a period of one year extending from January 1 to December 31, 2008. EDTA blood samples were analyzed for completed blood counts and hemoglobin variants through automated hematology analyzer and Bio-Rad beta thalassaemia short program respectively. Six samples were randomly selected to characterize HbE at molecular level through RFLP-PCR utilizing <it>Mnl</it>I restriction enzyme.</p> <p>Results</p> <p>During the study period, 11403 chromatograms were analyzed and Hb E was detected in 41 (or 0.36%) samples. Different hemoglobin E syndromes identified were HbEA (n = 20 or 49%), HbE/β-thalassemia (n = 14 or 34%), HbEE (n = 6 or 15%) and HbE/HbS (n = 1 or 2%). Compound heterozygosity for HbE and beta thalassaemia was found to be the most severely affected phenotype. RFLP-PCR utilizing <it>Mnl</it>I successfully characterized HbE at molecular level in six randomly selected samples.</p> <p>Conclusions</p> <p>Various HbE phenotypes are prevalent in Pakistan with HbEA and HbE/β thalassaemia representing the most common syndromes. Chromatography cannot only successfully identify hemoglobin E but also assist in further characterization into its phenotype including compound heterozygosity. Definitive diagnosis of HbE can easily be achieved through RFLP-PCR.</p

Drug Off-Target Effects Predicted Using Structural Analysis in the Context of a Metabolic Network Model

Recent advances in structural bioinformatics have enabled the prediction of protein-drug off-targets based on their ligand binding sites. Concurrent developments in systems biology allow for prediction of the functional effects of system perturbations using large-scale network models. Integration of these two capabilities provides a framework for evaluating metabolic drug response phenotypes in silico. This combined approach was applied to investigate the hypertensive side effect of the cholesteryl ester transfer protein inhibitor torcetrapib in the context of human renal function. A metabolic kidney model was generated in which to simulate drug treatment. Causal drug off-targets were predicted that have previously been observed to impact renal function in gene-deficient patients and may play a role in the adverse side effects observed in clinical trials. Genetic risk factors for drug treatment were also predicted that correspond to both characterized and unknown renal metabolic disorders as well as cryptic genetic deficiencies that are not expected to exhibit a renal disorder phenotype except under drug treatment. This study represents a novel integration of structural and systems biology and a first step towards computational systems medicine. The methodology introduced herein has important implications for drug development and personalized medicine

Efficient Enrichment of Hepatic Cancer Stem-Like Cells from a Primary Rat HCC Model via a Density Gradient Centrifugation-Centered Method

Background: Because few definitive markers are available for hepatic cancer stem cells (HCSCs), based on physical rather than immunochemical properties, we applied a novel method to enrich HCSCs. Methodology: After hepatic tumor cells (HTCs) were first isolated from diethylinitrosamine-induced F344 rat HCC model using percoll discontinuous gradient centrifugation (PDGC) and purified via differential trypsinization and differential attachment (DTDA), they were separated into four fractions using percoll continuous gradient centrifugation (PCGC) and sequentially designated as fractions I–IV (FI–IV). Morphological characteristics, mRNA and protein levels of stem cell markers, proliferative abilities, induced differentiation, in vitro migratory capacities, in vitro chemo-resistant capacities, and in vivo malignant capacities were determined for the cells of each fraction. Findings: As the density of cells increased, 22.18%, 11.62%, 4.73 % and 61.47 % of primary cultured HTCs were segregated in FI–FIV, respectively. The cells from FIII (density between 1.041 and 1.062 g/ml) displayed a higher nuclear-cytoplasmic ratio and fewer organelles and expressed higher levels of stem cell markers (AFP, EpCAM and CD133) than cells from other fractions (P,0.01). Additionally, in vitro, the cells from FIII showed a greater capacity to self-renew, differentiate into mature HTCs, transit across membranes, close scratches, and carry resistance to chemotherapy than did cells from any other fraction; in vivo, injection of only 1610 4 cells from FIII could generate tumors not only in subcutaneous tissue but also in th

Insulin resistance, lipotoxicity, type 2 diabetes and atherosclerosis: the missing links. The Claude Bernard Lecture 2009

Insulin resistance is a hallmark of type 2 diabetes mellitus and is associated with a metabolic and cardiovascular cluster of disorders (dyslipidaemia, hypertension, obesity [especially visceral], glucose intolerance, endothelial dysfunction), each of which is an independent risk factor for cardiovascular disease (CVD). Multiple prospective studies have documented an association between insulin resistance and accelerated CVD in patients with type 2 diabetes, as well as in non-diabetic individuals. The molecular causes of insulin resistance, i.e. impaired insulin signalling through the phosphoinositol-3 kinase pathway with intact signalling through the mitogen-activated protein kinase pathway, are responsible for the impairment in insulin-stimulated glucose metabolism and contribute to the accelerated rate of CVD in type 2 diabetes patients. The current epidemic of diabetes is being driven by the obesity epidemic, which represents a state of tissue fat overload. Accumulation of toxic lipid metabolites (fatty acyl CoA, diacylglycerol, ceramide) in muscle, liver, adipocytes, beta cells and arterial tissues contributes to insulin resistance, beta cell dysfunction and accelerated atherosclerosis, respectively, in type 2 diabetes. Treatment with thiazolidinediones mobilises fat out of tissues, leading to enhanced insulin sensitivity, improved beta cell function and decreased atherogenesis. Insulin resistance and lipotoxicity represent the missing links (beyond the classical cardiovascular risk factors) that help explain the accelerated rate of CVD in type 2 diabetic patients

Selection of the appropriate method for the assessment of insulin resistance

Insulin resistance is one of the major aggravating factors for metabolic syndrome. There are many methods available for estimation of insulin resistance which range from complex techniques down to simple indices. For all methods of assessing insulin resistance it is essential that their validity and reliability is established before using them as investigations. The reference techniques of hyperinsulinaemic euglycaemic clamp and its alternative the frequently sampled intravenous glucose tolerance test are the most reliable methods available for estimating insulin resistance. However, many simple methods, from which indices can be derived, have been assessed and validated e.g. homeostasis model assessment (HOMA), quantitative insulin sensitivity check index (QUICKI). Given the increasing number of simple indices of IR it may be difficult for clinicians and researchers to select the most appropriate index for their studies. This review therefore provides guidelines and advices which must be considered before proceeding with a study

Real-time numerical forecast of global epidemic spreading: Case study of 2009 A/H1N1pdm

Background Mathematical and computational models for infectious diseases are increasingly used to support public-health decisions; however, their reliability is currently under debate. Real-time forecasts of epidemic spread using data-driven models have been hindered by the technical challenges posed by parameter estimation and validation. Data gathered for the 2009 H1N1 influenza crisis represent an unprecedented opportunity to validate real-time model predictions and define the main success criteria for different approaches. Methods We used the Global Epidemic and Mobility Model to generate stochastic simulations of epidemic spread worldwide, yielding (among other measures) the incidence and seeding events at a daily resolution for 3,362 subpopulations in 220 countries. Using a Monte Carlo Maximum Likelihood analysis, the model provided an estimate of the seasonal transmission potential during the early phase of the H1N1 pandemic and generated ensemble forecasts for the activity peaks in the northern hemisphere in the fall/winter wave. These results were validated against the real-life surveillance data collected in 48 countries, and their robustness assessed by focusing on 1) the peak timing of the pandemic; 2) the level of spatial resolution allowed by the model; and 3) the clinical attack rate and the effectiveness of the vaccine. In addition, we studied the effect of data incompleteness on the prediction reliability. Results Real-time predictions of the peak timing are found to be in good agreement with the empirical data, showing strong robustness to data that may not be accessible in real time (such as pre-exposure immunity and adherence to vaccination campaigns), but that affect the predictions for the attack rates. The timing and spatial unfolding of the pandemic are critically sensitive to the level of mobility data integrated into the model. Conclusions Our results show that large-scale models can be used to provide valuable real-time forecasts of influenza spreading, but they require high-performance computing. The quality of the forecast depends on the level of data integration, thus stressing the need for high-quality data in population-based models, and of progressive updates of validated available empirical knowledge to inform these models

Cancer Biomarker Discovery: The Entropic Hallmark

Background: It is a commonly accepted belief that cancer cells modify their transcriptional state during the progression of the disease. We propose that the progression of cancer cells towards malignant phenotypes can be efficiently tracked using high-throughput technologies that follow the gradual changes observed in the gene expression profiles by employing Shannon's mathematical theory of communication. Methods based on Information Theory can then quantify the divergence of cancer cells' transcriptional profiles from those of normally appearing cells of the originating tissues. The relevance of the proposed methods can be evaluated using microarray datasets available in the public domain but the method is in principle applicable to other high-throughput methods. Methodology/Principal Findings: Using melanoma and prostate cancer datasets we illustrate how it is possible to employ Shannon Entropy and the Jensen-Shannon divergence to trace the transcriptional changes progression of the disease. We establish how the variations of these two measures correlate with established biomarkers of cancer progression. The Information Theory measures allow us to identify novel biomarkers for both progressive and relatively more sudden transcriptional changes leading to malignant phenotypes. At the same time, the methodology was able to validate a large number of genes and processes that seem to be implicated in the progression of melanoma and prostate cancer. Conclusions/Significance: We thus present a quantitative guiding rule, a new unifying hallmark of cancer: the cancer cell's transcriptome changes lead to measurable observed transitions of Normalized Shannon Entropy values (as measured by high-throughput technologies). At the same time, tumor cells increment their divergence from the normal tissue profile increasing their disorder via creation of states that we might not directly measure. This unifying hallmark allows, via the the Jensen-Shannon divergence, to identify the arrow of time of the processes from the gene expression profiles, and helps to map the phenotypical and molecular hallmarks of specific cancer subtypes. The deep mathematical basis of the approach allows us to suggest that this principle is, hopefully, of general applicability for other diseases