Multi-Scale Simulation Modeling for Prevention and Public Health Management of Diabetes in Pregnancy and Sequelae

Diabetes in pregnancy (DIP) is an increasing public health priority in the Australian Capital Territory, particularly due to its impact on risk for developing Type 2 diabetes. While earlier diagnostic screening results in greater capacity for early detection and treatment, such benefits must be balanced with the greater demands this imposes on public health services. To address such planning challenges, a multi-scale hybrid simulation model of DIP was built to explore the interaction of risk factors and capture the dynamics underlying the development of DIP. The impact of interventions on health outcomes at the physiological, health service and population level is measured. Of particular central significance in the model is a compartmental model representing the underlying physiological regulation of glycemic status based on beta-cell dynamics and insulin resistance. The model also simulated the dynamics of continuous BMI evolution, glycemic status change during pregnancy and diabetes classification driven by the individual-level physiological model. We further modeled public health service pathways providing diagnosis and care for DIP to explore the optimization of resource use during service delivery. The model was extensively calibrated against empirical data.Comment: 10 pages, SBP-BRiMS 201

Association between foot types defined by static and dynamic measures, and the centre of pressure during gait

Foot types (e.g. pronated, supinated foot) are used for clinical reasoning [1] and widely assumed to be related to centre of pressure (COP) patterns [2, 3]. Specifically, a pronated foot will demonstrate a medially deviated COP. It follows that COP could be a measure of foot type and inferences about function extrapolated from it. The purpose of this study was to investigate whether COP parameters differ between foot types

Rapid Assembly of Multiple-Exon cDNA Directly from Genomic DNA

Backgrouud. Polymerase chain reaction (PCR) is extensively applied in gene cloning. But due to the existence of introns, low copy number of particular genes and high complexity of the eukaryotic genome, it is usually impossible to amplify and clone a gene as a full-length sequence directly from the genome by ordinary PCR based techniques. Cloning of cDNA instead of genomic DNA involves multiple steps: harvest of tissues that express the gene of interest, RNA isolation, cDNA synthesis (reverse transcription), and PCR amplification. To simplify the cloning procedures and avoid the problems caused by ubiquitously distributed durable RNases, we have developed a novel strategy allowing the cloning of any cDNA or open reading frame (ORF) with wild type sequence in any spliced form from a single genomic DNA preparation. Methodology. Our Genomic DNA Splicing technique contains the following steps: first, all exons of the gene are amplified from a genomic DNA preparation, using software-optimized, highly efficient primers residing in flanking introns. Next, the tissue-specific exon sequences are assembled into one full-length sequence by overlapping PCR with deliberately designed primers located at the splicing sites. Finally, software-optimized outmost primers are exploited for efficient amplification of the assembled full-length products. Conclusions. The Genomic DNA Splicing protocol avoids RNA preparation and reverse transcription steps, and the entire assembly process can be finished within hours, Since genamic DNA is more stable than RNA, it may be a more practical cloning strategy for many genes, especially the ones that are very large and difficult to generate a full length cDNA using oligo-dT primed reverse transcription. With this technique, we successfully doned the full-length wild type coding sequence of human polymeric immunoglobulin receptor, which is 2295 bp in length and composed of 10 exons. © 2007 An et al.published_or_final_versio

Synthesis and Characterization of Core-shell ZrO2/PAAEM/PS Nanoparticles

This work demonstrates the synthesis of core-shell ZrO2/PAAEM/PS nanoparticles through a combination of sol–gel method and emulsifier-free emulsion polymerizaiton. By this method, the modified nanometer ZrO2cores were prepared by chemical modification at a molecular level of zirconium propoxide with monomer of acetoacetoxyethylmethacrylate (AAEM), and then copolymerized with vinyl monomer to form uniform-size hybrid nanoparticles with diameter of around 250 nm. The morphology, composition, and thermal stability of the core-shell particles were characterized by various techniques including transmission electron microscopy (TEM), X-ray diffractometer (XRD), Fourier transform infrared spectroscopy (FTIR), X-ray photoelectron spectroscopy (XPS), and thermal-gravimetry analyzer (TGA). The results indicate that the inorganic–organic nanocomposites exhibit good thermal stability with the maximum decomposition temperature of ~447 °C. This approach would be useful for the synthesis of other inorganic–organic nanocomposites with desired functionalities

Heritable genetic variants in key cancer genes link cancer risk with anthropometric traits

Background Height and other anthropometric measures are consistently found to associate with differential cancer risk. However, both genetic and mechanistic insights into these epidemiological associations are notably lacking. Conversely, inherited genetic variants in tumour suppressors and oncogenes increase cancer risk, but little is known about their influence on anthropometric traits. Methods By integrating inherited and somatic cancer genetic data from the Genome-Wide Association Study Catalog, expression Quantitative Trait Loci databases and the Cancer Gene Census, we identify SNPs that associate with different cancer types and differential gene expression in at least one tissue type, and explore the potential pleiotropic associations of these SNPs with anthropometric traits through SNP-wise association in a cohort of 500,000 individuals. Results We identify three regulatory SNPs for three important cancer genes, FANCA, MAP3K1 and TP53 that associate with both anthropometric traits and cancer risk. Of particular interest, we identify a previously unrecognised strong association between the rs78378222[C] SNP in the 3' untranslated region (3'-UTR) of TP53 and both increased risk for developing non-melanomatous skin cancer (OR=1.36 (95% 1.31 to 1.41), adjusted p=7.62E−63), brain malignancy (OR=3.12 (2.22 to 4.37), adjusted p=1.43E−12) and increased standing height (adjusted p=2.18E−24, beta=0.073±0.007), lean body mass (adjusted p=8.34E−37, beta=0.073±0.005) and basal metabolic rate (adjusted p=1.13E−31, beta=0.076±0.006), thus offering a novel genetic link between these anthropometric traits and cancer risk. Conclusion Our results clearly demonstrate that heritable variants in key cancer genes can associate with both differential cancer risk and anthropometric traits in the general population, thereby lending support for a genetic basis for linking these human phenotypes

Can modeling of HIV treatment processes improve outcomes? Capitalizing on an operations research approach to the global pandemic

<p>Abstract</p> <p>Background</p> <p>Mathematical modeling has been applied to a range of policy-level decisions on resource allocation for HIV care and treatment. We describe the application of classic operations research (OR) techniques to address logistical and resource management challenges in HIV treatment scale-up activities in resource-limited countries.</p> <p>Methods</p> <p>We review and categorize several of the major logistical and operational problems encountered over the last decade in the global scale-up of HIV care and antiretroviral treatment for people with AIDS. While there are unique features of HIV care and treatment that pose significant challenges to effective modeling and service improvement, we identify several analogous OR-based solutions that have been developed in the service, industrial, and health sectors.</p> <p>Results</p> <p>HIV treatment scale-up includes many processes that are amenable to mathematical and simulation modeling, including forecasting future demand for services; locating and sizing facilities for maximal efficiency; and determining optimal staffing levels at clinical centers. Optimization of clinical and logistical processes through modeling may improve outcomes, but successful OR-based interventions will require contextualization of response strategies, including appreciation of both existing health care systems and limitations in local health workforces.</p> <p>Conclusion</p> <p>The modeling techniques developed in the engineering field of operations research have wide potential application to the variety of logistical problems encountered in HIV treatment scale-up in resource-limited settings. Increasing the number of cross-disciplinary collaborations between engineering and public health will help speed the appropriate development and application of these tools.</p

Service workload patterns for QoS-driven cloud resource management

Cloud service providers negotiate SLAs for customer services they offer based on the reliability of performance and availability of their lower-level platform infrastructure. While availability management is more mature, performance management is less reliable. In order to support a continuous approach that supports the initial static infrastructure configuration as well as dynamic reconfiguration and auto-scaling, an accurate and efficient solution is required. We propose a prediction technique that combines a workload pattern mining approach with a traditional collaborative filtering solution to meet the accuracy and efficiency requirements. Service workload patterns abstract common infrastructure workloads from monitoring logs and act as a part of a first-stage high-performant configuration mechanism before more complex traditional methods are considered. This enhances current reactive rule-based scalability approaches and basic prediction techniques by a hybrid prediction solution. Uncertainty and noise are additional challenges that emerge in multi-layered, often federated cloud architectures. We specifically add log smoothing combined with a fuzzy logic approach to make the prediction solution more robust in the context of these challenges

Improving gene-set enrichment analysis of RNA-Seq data with small replicates

Deregulated pathways identified from transcriptome data of two sample groups have played a key role in many genomic studies. Gene-set enrichment analysis (GSEA) has been commonly used for pathway or functional analysis of microarray data, and it is also being applied to RNA-seq data. However, most RNA-seq data so far have only small replicates. This enforces to apply the gene-permuting GSEA method (or preranked GSEA) which results in a great number of false positives due to the inter-gene correlation in each gene-set. We demonstrate that incorporating the absolute gene statistic in one-tailed GSEA considerably improves the false-positive control and the overall discriminatory ability of the gene-permuting GSEA methods for RNA-seq data. To test the performance, a simulation method to generate correlated read counts within a gene-set was newly developed, and a dozen of currently available RNA-seq enrichment analysis methods were compared, where the proposed methods outperformed others that do not account for the inter-gene correlation. Analysis of real RNA-seq data also supported the proposed methods in terms of false positive control, ranks of true positives and biological relevance. An efficient R package (AbsFilterG- SEA) coded with C++ (Rcpp) is available from CRAN.open

Severe anaemia is associated with a higher risk for preeclampsia and poor perinatal outcomes in Kassala hospital, eastern Sudan

<p>Abstract</p> <p>Background</p> <p>Anaemia during pregnancy is major health problem. There is conflicting literature regarding the association between anaemia and its severity and maternal and perinatal outcomes.</p> <p>Methods</p> <p>This is a retrospective case-control study conducted at Kassala hospital, eastern Sudan. Medical files of pregnant women with severe anaemia (haemoglobin (Hb) < 7 g/dl, n = 303) who delivered from January 2008 to December 2010 were reviewed. Socio-demographic and obstetric data were analysed and compared with a similar number of women with mild/moderate anaemia (Hb = 7-10.9 g/dl, n = 303) and with no anaemia (Hb > 11 g/dl, n = 303). Logistic regression analysis was performed separately for each of the outcome measures: preeclampsia, eclampsia, preterm birth, low birth weight (LBW) and stillbirth.</p> <p>Results</p> <p>There were 9578 deliveries at Kassala hospital, 4012 (41.8%) women had anaemia and 303 (3.2%) had severe anaemia. The corrected risk for preeclampsia increased only in severe anaemia (OR = 3.6, 95% CI: 1.4-9.1, <it>P </it>= 0.007). Compared with women with no anaemia, the risk of LBW was 2.5 times higher in women with mild/moderate anaemia (95% CI: 1.1-5.7), and 8.0 times higher in women with severe anaemia (95% CI: 3.8-16.0). The risk of preterm delivery increased significantly with the severity of anaemia (OR = 3.2 for women with mild/moderate anaemia and OR = 6.6 for women with severe anaemia, compared with women with no anaemia). The corrected risk for stillbirth increased only in severe anaemia (OR = 4.3, 95% CI: 1.9-9.1, <it>P </it>< 0.001).</p> <p>Conclusions</p> <p>The greater the severity of the anaemia during pregnancy, the greater the risk of preeclampsia, preterm delivery, LBW and stillbirth. Preventive measures should be undertaken to decrease the prevalence of anaemia in pregnancy.</p