Microarray data mining using Bioconductor packages

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Confirmation of quantitative trait loci affecting fatness in chickens

In this report we describe the analysis of an advanced intercross line (AIL) to confirm the quantitative trait locus (QTL) regions found for fatness traits in a previous study. QTL analysis was performed on chromosomes 1, 3, 4, 15, 18, and 27. The AIL was created by random intercrossing in each generation from generation 2 (G2) onwards until generation 9 (G9) was reached. QTL for abdominal fat weight (AFW) and/or percentage abdominal fat (AF%) on chromosomes 1, 3 and 27 were confirmed in the G9 population. In addition, evidence for QTL for body weight at the age of 5 (BW5) and 7 (BW7) weeks and for the percentage of intramuscular fat (IF%) were found on chromosomes 1, 3, 15, and 27. Significant evidence for QTL was detected on chromosome 1 for BW5 and BW7. Suggestive evidence was found on chromosome 1 for AFW, AF% and IF%, on chromosome 15 for BW5, and on chromosome 27 for AF% and IF%. Furthermore, evidence on the chromosome-wise level was found on chromosome 3 for AFW, AF%, and BW7 and on chromosome 27 for BW5. For chromosomes 4 and 18, test statistics did not exceed the significance threshold

Risk factors for childhood malnutrition in Roma settlements in Serbia

<p>Abstract</p> <p>Background</p> <p>Children living in Roma settlements in Central and Eastern Europe face extreme levels of social exclusion and poverty, but their health status has not been well studied. The objective of this study was to elucidate risk factors for malnutrition in children in Roma settlements in Serbia.</p> <p>Methods</p> <p>Anthropometric and sociodemographic measures were obtained for 1192 Roma children under five living in Roma settlements from the 2005 Serbia Multiple Indicator Cluster Survey. Multiple logistic regression was used to relate family and child characteristics to the odds of stunting, wasting, and underweight.</p> <p>Results</p> <p>The prevalence of stunting, wasting, and underweight was 20.1%, 4.3%, and 8.0%, respectively. Nearly all of the children studied fell into the lowest quintile of wealth for the overall population of Serbia. Children in the lowest quintile of wealth were four times more likely to be stunted compared to those in the highest quintile, followed by those in the second lowest quintile (AOR = 2.1) and lastly by those in the middle quintile (AOR = 1.6). Children who were ever left in the care of an older child were almost twice as likely to stunted as those were not. Children living in urban settlements showed a clear disadvantage with close to three times the likelihood of being wasted compared to those living in rural areas. There was a suggestion that maternal, but not paternal, education was associated with stunting, and maternal literacy was significantly associated with wasting. Whether children were ever breastfed, immunized or had diarrhoeal episodes in the past two weeks did not show strong correlations to children malnutrition status in this Roma population.</p> <p>Conclusions</p> <p>There exists a gradient relationship between household wealth and stunting even within impoverished settlements, indicating that among poor and marginalized populations socioeconomic inequities in child health should be addressed. Other areas on which to focus future research and public health intervention include maternal literacy, child endangerment practices, and urban settlements.</p

What Is the Negative Predictive Value of Multiparametric Magnetic Resonance Imaging in Excluding Prostate Cancer at Biopsy? A Systematic Review and Meta-analysis from the European Association of Urology Prostate Cancer Guidelines Panel

Context: It remains unclear whether patients with a suspicion of prostate cancer (PCa) and negative multiparametric magnetic resonance imaging (mpMRI) can safely obviate prostate biopsy. Objective: To systematically review the literature assessing the negative predictive value (NPV) of mpMRI in patients with a suspicion of PCa. Evidence acquisition: The Embase, Medline, and Cochrane databases were searched up to February 2016. Studies reporting prebiopsy mpMRI results using transrectal or transperineal biopsy as a reference standard were included. We further selected for meta-analysis studies with at least 10-core biopsies as the reference standard, mpMRI comprising at least T2-weighted and diffusion-weighted imaging, positive mpMRI defined as a Prostate Imaging Reporting Data System/Likert score of ≥3/5 or ≥4/5, and results reported at patient level for the detection of overall PCa or clinically significant PCa (csPCa) defined as Gleason ≥7 cancer. Evidence synthesis: A total of 48 studies (9613 patients) were eligible for inclusion. At patient level, the median prevalence was 50.4% (interquartile range [IQR], 36.4–57.7%) for overall cancer and 32.9% (IQR, 28.1–37.2%) for csPCa. The median mpMRI NPV was 82.4% (IQR, 69.0–92.4%) for overall cancer and 88.1% (IQR, 85.7–92.3) for csPCa. NPV significantly decreased when cancer prevalence increased, for overall cancer (r = –0.64, p &lt; 0.0001) and csPCa (r = –0.75, p = 0.032). Eight studies fulfilled the inclusion criteria for meta-analysis. Seven reported results for overall PCa. When the overall PCa prevalence increased from 30% to 60%, the combined NPV estimates decreased from 88% (95% confidence interval [95% CI], 77–99%) to 67% (95% CI, 56–79%) for a cut-off score of 3/5. Only one study selected for meta-analysis reported results for Gleason ≥7 cancers, with a positive biopsy rate of 29.3%. The corresponding NPV for a cut-off score of ≥3/5 was 87.9%. Conclusions: The NPV of mpMRI varied greatly depending on study design, cancer prevalence, and definitions of positive mpMRI and csPCa. As cancer prevalence was highly variable among series, risk stratification of patients should be the initial step before considering prebiopsy mpMRI and defining those in whom biopsy may be omitted when the mpMRI is negative. Patient summary This systematic review examined if multiparametric magnetic resonance imaging (MRI) scan can be used to reliably predict the absence of prostate cancer in patients suspected of having prostate cancer, thereby avoiding a prostate biopsy. The results suggest that whilst it is a promising tool, it is not accurate enough to replace prostate biopsy in such patients, mainly because its accuracy is variable and influenced by the prostate cancer risk. However, its performance can be enhanced if there were more accurate ways of determining the risk of having prostate cancer. When such tools are available, it should be possible to use an MRI scan to avoid biopsy in patients at a low risk of prostate cancer