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1.  TNF-α gene expression is increased following zinc supplementation in type 2 diabetes mellitus 
Genes & Nutrition  2014;10(1):440.
Chronic low-grade inflammation in type 2 diabetes mellitus (DM) can elicit changes in whole-body zinc metabolism. The interaction among the expression of inflammatory cytokines, zinc transporter and metallothionein (MT) genes in peripheral blood mononuclear cells in type 2 DM remains unclear. In a 12-week randomized controlled trial, the effects of zinc (40 mg/day) supplementation on the gene expression of cytokines, zinc transporters and MT in women with type 2 DM were examined. In the zinc-supplemented group, gene expression of tumour necrosis factor (TNF)-α tended to be upregulated by 27 ± 10 % at week 12 compared to baseline (P = 0.053). TNF-α fold change in the zinc-treated group was higher than in those without zinc supplementation (P < 0.05). No significant changes were observed in the expression or fold change of interleukin (IL)-1β or IL-6. Numerous bivariate relationships were observed between the fold changes of cytokines and zinc transporters, including ZnT7 with IL-1β (P < 0.01), IL-6 (P < 0.01) and TNF-α (P < 0.01). In multiple regression analysis, IL-1β expression was predicted by the expression of all zinc transporters and MT measured at baseline (r2 = 0.495, P < 0.05) and at week 12 (r2 = 0.532, P < 0.03). The current study presents preliminary evidence that zinc supplementation increases cytokine gene expression in type 2 DM. The relationships found among zinc transporters, MT and cytokines suggest close  interactions between zinc homeostasis and inflammation.
Electronic supplementary material
The online version of this article (doi:10.1007/s12263-014-0440-4) contains supplementary material, which is available to authorized users.
doi:10.1007/s12263-014-0440-4
PMCID: PMC4235802  PMID: 25403095
Inflammation; Cytokines; Zinc transporters; Metallothionein; Gene expression; Type 2 diabetes mellitus
2.  Automating HIV Drug Resistance Genotyping with RECall, a Freely Accessible Sequence Analysis Tool 
Journal of Clinical Microbiology  2012;50(6):1936-1942.
Genotypic HIV drug resistance testing is routinely used to guide clinical decisions. While genotyping methods can be standardized, a slow, labor-intensive, and subjective manual sequence interpretation step is required. We therefore performed external validation of our custom software RECall, a fully automated sequence analysis pipeline. HIV-1 drug resistance genotyping was performed on 981 clinical samples at the Stanford Diagnostic Virology Laboratory. Sequencing trace files were first interpreted manually by a laboratory technician and subsequently reanalyzed by RECall, without intervention. The relative performances of the two methods were assessed by determination of the concordance of nucleotide base calls, identification of key resistance-associated substitutions, and HIV drug resistance susceptibility scoring by the Stanford Sierra algorithm. RECall is freely available at http://pssm.cfenet.ubc.ca. In total, 875 of 981 sequences were analyzed by both human and RECall interpretation. RECall analysis required minimal hands-on time and resulted in a 25-fold improvement in processing speed (∼150 technician-hours versus ∼6 computation-hours). Excellent concordance was obtained between human and automated RECall interpretation (99.7% agreement for >1,000,000 bases compared). Nearly all discordances (99.4%) were due to nucleotide mixtures being called by one method but not the other. Similarly, 98.6% of key antiretroviral resistance-associated mutations observed were identified by both methods, resulting in 98.5% concordance of resistance susceptibility interpretations. This automated sequence analysis tool provides both standardization of analysis and a significant improvement in data workflow. The time-consuming, error-prone, and dreadfully boring manual sequence analysis step is replaced with a fully automated system without compromising the accuracy of reported HIV drug resistance data.
doi:10.1128/JCM.06689-11
PMCID: PMC3372133  PMID: 22403431
3.  The use of syndromic surveillance for decision-making during the H1N1 pandemic: A qualitative study 
BMC Public Health  2012;12:929.
Background
Although an increasing number of studies are documenting uses of syndromic surveillance by front line public health, few detail the value added from linking syndromic data to public health decision-making. This study seeks to understand how syndromic data informed specific public health actions during the 2009 H1N1 pandemic.
Methods
Semi-structured telephone interviews were conducted with participants from Ontario’s public health departments, the provincial ministry of health and federal public health agency to gather information about syndromic surveillance systems used and the role of syndromic data in informing specific public health actions taken during the pandemic. Responses were compared with how the same decisions were made by non-syndromic surveillance users.
Results
Findings from 56 interviews (82% response) show that syndromic data were most used for monitoring virus activity, measuring impact on the health care system and informing the opening of influenza assessment centres in several jurisdictions, and supporting communications and messaging, rather than its intended purpose of early outbreak detection. Syndromic data had limited impact on decisions that involved the operation of immunization clinics, school closures, sending information letters home with school children or providing recommendations to health care providers. Both syndromic surveillance users and non-users reported that guidance from the provincial ministry of health, communications with stakeholders and vaccine availability were driving factors in these public health decisions.
Conclusions
Syndromic surveillance had limited use in decision-making during the 2009 H1N1 pandemic in Ontario. This study provides insights into the reasons why this occurred. Despite this, syndromic data were valued for providing situational awareness and confidence to support public communications and recommendations. Developing an understanding of how syndromic data are utilized during public health events provides valuable evidence to support future investments in public health surveillance.
doi:10.1186/1471-2458-12-929
PMCID: PMC3539916  PMID: 23110473
Decision making; Pandemic influenza; Public health; Surveillance; Syndromic surveillance
4.  SIGMA: A System for Integrative Genomic Microarray Analysis of Cancer Genomes 
BMC Genomics  2006;7:324.
Background
The prevalence of high resolution profiling of genomes has created a need for the integrative analysis of information generated from multiple methodologies and platforms. Although the majority of data in the public domain are gene expression profiles, and expression analysis software are available, the increase of array CGH studies has enabled integration of high throughput genomic and gene expression datasets. However, tools for direct mining and analysis of array CGH data are limited. Hence, there is a great need for analytical and display software tailored to cross platform integrative analysis of cancer genomes.
Results
We have created a user-friendly java application to facilitate sophisticated visualization and analysis such as cross-tumor and cross-platform comparisons. To demonstrate the utility of this software, we assembled array CGH data representing Affymetrix SNP chip, Stanford cDNA arrays and whole genome tiling path array platforms for cross comparison. This cancer genome database contains 267 profiles from commonly used cancer cell lines representing 14 different tissue types.
Conclusion
In this study we have developed an application for the visualization and analysis of data from high resolution array CGH platforms that can be adapted for analysis of multiple types of high throughput genomic datasets. Furthermore, we invite researchers using array CGH technology to deposit both their raw and processed data, as this will be a continually expanding database of cancer genomes. This publicly available resource, the System for Integrative Genomic Microarray Analysis (SIGMA) of cancer genomes, can be accessed at .
doi:10.1186/1471-2164-7-324
PMCID: PMC1764892  PMID: 17192189

Results 1-4 (4)