Variance in multiplex suspension array assays: A distribution generation machine for multiplex counts

<p>Abstract</p> <p>Background</p> <p>This study attempted to replicate Luminex experimental results for large numbers of beads per classifier using multiplexed assays and routine instrument use conditions.</p> <p>Conclusion</p> <p>Using larger numbers of microspheres per classifier highlights a fundamental stochastic distribution of bead counts issue complicated by other factors. The more classifiers and the higher the count required per classifier there are, the more apparent the distribution of counts per classifier will be, and the more microspheres are required. Additional problems have been identified. Alternate methods of improving precision and reliability are recommended such as intraplexing and multi-well sample replicates to improve precision and confidence.</p

Increasing the Analytical Sensitivity by Oligonucleotides Modified with Para- and Ortho-Twisted Intercalating Nucleic Acids – TINA

The sensitivity and specificity of clinical diagnostic assays using DNA hybridization techniques are limited by the dissociation of double-stranded DNA (dsDNA) antiparallel duplex helices. This situation can be improved by addition of DNA stabilizing molecules such as nucleic acid intercalators. Here, we report the synthesis of a novel ortho-Twisted Intercalating Nucleic Acid (TINA) amidite utilizing the phosphoramidite approach, and examine the stabilizing effect of ortho- and para-TINA molecules in antiparallel DNA duplex formation. In a thermal stability assay, ortho- and para-TINA molecules increased the melting point (Tm) of Watson-Crick based antiparallel DNA duplexes. The increase in Tm was greatest when the intercalators were placed at the 5′ and 3′ termini (preferable) or, if placed internally, for each half or whole helix turn. Terminally positioned TINA molecules improved analytical sensitivity in a DNA hybridization capture assay targeting the Escherichia coli rrs gene. The corresponding sequence from the Pseudomonas aeruginosa rrs gene was used as cross-reactivity control. At 150 mM ionic strength, analytical sensitivity was improved 27-fold by addition of ortho-TINA molecules and 7-fold by addition of para-TINA molecules (versus the unmodified DNA oligonucleotide), with a 4-fold increase retained at 1 M ionic strength. Both intercalators sustained the discrimination of mismatches in the dsDNA (indicated by ΔTm), unless placed directly adjacent to the mismatch – in which case they partly concealed ΔTm (most pronounced for para-TINA molecules). We anticipate that the presented rules for placement of TINA molecules will be broadly applicable in hybridization capture assays and target amplification systems

Identifying bereaved subjects at risk of complicated grief: Predictive value of questionnaire items in a cohort study

<p>Abstract</p> <p>Background</p> <p>Bereavement is a condition which most people experience several times during their lives. A small but noteworthy proportion of bereaved individuals experience a syndrome of prolonged psychological distress in relation to bereavement. The aim of the study was to develop a clinical tool to identify bereaved individuals who had a prognosis of complicated grief and to propose a model for a screening tool to identify those at risk of complicated grief applicable among bereaved patients in general practice and palliative care.</p> <p>Methods</p> <p>We examined the responses of 276 newly bereaved individuals to a variety of standardised and ad hoc questionnaire items eight weeks post loss. Inventory of Complicated Grief (ICG-R) was used as a gold standard of distress at six months after bereavement. Receiver operating characteristic (ROC) curves analysis was performed for all scales and items regarding ICG-R score. Sensitivity, specificity and area under curve (AUC) were calculated for scales and items with the most promising ROC curve analyses.</p> <p>Results</p> <p>Beck's Depression Inventory (BDI) was the scale with the highest AUC (0.83) and adding a single item question ('Even while my relative was dying, I felt a sense of purpose in my life') gave a sensitivity of 80% and specificity of 75%. The positive/negative predictive values for this combination of questions were 70% and 85%, respectively. With this screening tool bereaved people could be categorized into three groups where group 1 had 7%, group 2 had 23% and group 3 had 64% propensity of suffering from complicated grief six months post loss.</p> <p>Conclusions</p> <p>This study shows that the BDI in combination with a single item question eight weeks post loss may be used for clinical screening for risk of developing complicated grief after six months. The feasibility and clinical implications of the screening tool has to be tested in a clinical setting.</p

ExprTarget: An Integrative Approach to Predicting Human MicroRNA Targets

Variation in gene expression has been observed in natural populations and associated with complex traits or phenotypes such as disease susceptibility and drug response. Gene expression itself is controlled by various genetic and non-genetic factors. The binding of a class of small RNA molecules, microRNAs (miRNAs), to mRNA transcript targets has recently been demonstrated to be an important mechanism of gene regulation. Because individual miRNAs may regulate the expression of multiple gene targets, a comprehensive and reliable catalogue of miRNA-regulated targets is critical to understanding gene regulatory networks. Though experimental approaches have been used to identify many miRNA targets, due to cost and efficiency, current miRNA target identification still relies largely on computational algorithms that aim to take advantage of different biochemical/thermodynamic properties of the sequences of miRNAs and their gene targets. A novel approach, ExprTarget, therefore, is proposed here to integrate some of the most frequently invoked methods (miRanda, PicTar, TargetScan) as well as the genome-wide HapMap miRNA and mRNA expression datasets generated in our laboratory. To our knowledge, this dataset constitutes the first miRNA expression profiling in the HapMap lymphoblastoid cell lines. We conducted diagnostic tests of the existing computational solutions using the experimentally supported targets in TarBase as gold standard. To gain insight into the biases that arise from such an analysis, we investigated the effect of the choice of gold standard on the evaluation of the various computational tools. We analyzed the performance of ExprTarget using both ROC curve analysis and cross-validation. We show that ExprTarget greatly improves miRNA target prediction relative to the individual prediction algorithms in terms of sensitivity and specificity. We also developed an online database, ExprTargetDB, of human miRNA targets predicted by our approach that integrates gene expression profiling into a broader framework involving important features of miRNA target site predictions

The joint influence of marital status, interpregnancy interval, and neighborhood on small for gestational age birth: a retrospective cohort study

<p>Abstract</p> <p>Background</p> <p>Interpregnancy interval (IPI), marital status, and neighborhood are independently associated with birth outcomes. The joint contribution of these exposures has not been evaluated. We tested for effect modification between IPI and marriage, controlling for neighborhood.</p> <p>Methods</p> <p>We analyzed a cohort of 98,330 live births in Montréal, Canada from 1997–2001 to assess IPI and marital status in relation to small for gestational age (SGA) birth. Births were categorized as subsequent-born with <it>short </it>(<12 months), <it>intermediate </it>(12–35 months), or <it>long </it>(36+ months) IPI, or as firstborn. The data had a 2-level hierarchical structure, with births nested in 49 neighborhoods. We used multilevel logistic regression to obtain adjusted effect estimates.</p> <p>Results</p> <p>Marital status modified the association between IPI and SGA birth. Being unmarried relative to married was associated with SGA birth for all IPI categories, particularly for subsequent births with <it>short </it>(odds ratio [OR] 1.60, 95% confidence interval [CI] 1.31–1.95) and <it>intermediate </it>(OR 1.48, 95% CI 1.26–1.74) IPIs. Subsequent births had a lower likelihood of SGA birth than firstborns. <it>Intermediate </it>IPIs were more protective for married (OR 0.50, 95% CI 0.47–0.54) than unmarried mothers (OR 0.65, 95% CI 0.56–0.76).</p> <p>Conclusion</p> <p>Being unmarried increases the likelihood of SGA birth as the IPI shortens, and the protective effect of <it>intermediate </it>IPIs is reduced in unmarried mothers. Marital status should be considered in recommending particular IPIs as an intervention to improve birth outcomes.</p

Association of liver enzymes with incident type 2 diabetes: A nested case control study in an Iranian population

<p>Abstract</p> <p>Background</p> <p>To investigate the association of Aspartate aminotransferase (AST), Alanin aminotranferase (ALT) and Gamma glutamyl transferase (GGT) with incident type 2 diabetes.</p> <p>Methods</p> <p>In a nested case-control study, AST, ALT, GGT as well as classic diabetes risk factors, insulin and C-reactive protein (CRP) were measured in 133 non-diabetic subjects at baseline of which 68 were cases and 65 were controls. Incident diabetes was defined by the WHO 1999 criteria. Conditional logistic regression was used to calculate the odds ratio (OR) of incident diabetes associated with different hepatic markers. We used factor analysis for clustering of classic diabetes risk factors.</p> <p>Results</p> <p>In Univariate analysis both ALT and GGT were associated with diabetes with ORs of 3.07(1.21–7.79) and 2.91(1.29–6.53) respectively. After adjustment for CRP and insulin, ALT and GGT were still predictive of incident diabetes. When the model was further adjusted for anthropometric, blood pressure and metabolic factors, only ALT was independently associated with diabetes [OR = 3.18 (1.02–9.86)]. No difference was found between the area under the receiver operating characteristic curves of the models with and without ALT (0.820 and 0.802 respectively, P = 0.4)</p> <p>Conclusion</p> <p>ALT is associated with incident type 2 diabetes independent of classic risk factors. However, its addition to the classic risk factors does not improve the prediction of diabetes.</p

Latent physiological factors of complex human diseases revealed by independent component analysis of clinarrays

<p>Abstract</p> <p>Background</p> <p>Diagnosis and treatment of patients in the clinical setting is often driven by known symptomatic factors that distinguish one particular condition from another. Treatment based on noticeable symptoms, however, is limited to the types of clinical biomarkers collected, and is prone to overlooking dysfunctions in physiological factors not easily evident to medical practitioners. We used a vector-based representation of patient clinical biomarkers, or clinarrays, to search for latent physiological factors that underlie human diseases directly from clinical laboratory data. Knowledge of these factors could be used to improve assessment of disease severity and help to refine strategies for diagnosis and monitoring disease progression.</p> <p>Results</p> <p>Applying Independent Component Analysis on clinarrays built from patient laboratory measurements revealed both known and novel concomitant physiological factors for asthma, types 1 and 2 diabetes, cystic fibrosis, and Duchenne muscular dystrophy. Serum sodium was found to be the most significant factor for both type 1 and type 2 diabetes, and was also significant in asthma. TSH3, a measure of thyroid function, and blood urea nitrogen, indicative of kidney function, were factors unique to type 1 diabetes respective to type 2 diabetes. Platelet count was significant across all the diseases analyzed.</p> <p>Conclusions</p> <p>The results demonstrate that large-scale analyses of clinical biomarkers using unsupervised methods can offer novel insights into the pathophysiological basis of human disease, and suggest novel clinical utility of established laboratory measurements.</p

Influence of packing density and stress on the dynamic response of granular materials

Laboratory geophysics tests including bender elements and acoustic emission measure the speed of propagation of stress or sound waves in granular materials to derive elastic stiffness parameters. This contribution builds on earlier studies to assess whether the received signal characteristics can provide additional information about either the material’s behaviour or the nature of the material itself. Specifically it considers the maximum frequency that the material can transmit; it also assesses whether there is a simple link between the spectrum of the received signal and the natural frequencies of the sample. Discrete element method (DEM) simulations of planar compression wave propagation were performed to generate the data for the study. Restricting consideration to uniform (monodisperse) spheres, the material fabric was varied by considering face-centred cubic lattice packings as well as random configurations with different packing densities. Supplemental analyses, in addition to the DEM simulations, were used to develop a more comprehensive understanding of the system dynamics. The assembly stiffness and mass matrices were extracted from the DEM model and these data were used in an eigenmode analysis that provided significant insight into the observed overall dynamic response. The close agreement of the wave velocities estimated using eigenmode analysis with the DEM results confirms that DEM wave propagation simulations can reliably be used to extract material stiffness data. The data show that increasing either stress or density allows higher frequencies to propagate through the media, but the low-pass wavelength is a function of packing density rather than stress level. Prior research which had hypothesised that there is a simple link between the spectrum of the received signal and the natural sample frequencies was not substantiated

FISim: A new similarity measure between transcription factor binding sites based on the fuzzy integral

Background Regulatory motifs describe sets of related transcription factor binding sites (TFBSs) and can be represented as position frequency matrices (PFMs). De novo identification of TFBSs is a crucial problem in computational biology which includes the issue of comparing putative motifs with one another and with motifs that are already known. The relative importance of each nucleotide within a given position in the PFMs should be considered in order to compute PFM similarities. Furthermore, biological data are inherently noisy and imprecise. Fuzzy set theory is particularly suitable for modeling imprecise data, whereas fuzzy integrals are highly appropriate for representing the interaction among different information sources.Results We propose FISim, a new similarity measure between PFMs, based on the fuzzy integral of the distance of the nucleotides with respect to the information content of the positions. Unlike existing methods, FISim is designed to consider the higher contribution of better conserved positions to the binding affinity. FISim provides excellent results when dealing with sets of randomly generated motifs, and outperforms the remaining methods when handling real datasets of related motifs. Furthermore, we propose a new cluster methodology based on kernel theory together with FISim to obtain groups of related motifs potentially bound by the same TFs, providing more robust results than existing approaches.Conclusion FISim corrects a design flaw of the most popular methods, whose measures favour similarity of low information content positions. We use our measure to successfully identify motifs that describe binding sites for the same TF and to solve real-life problems. In this study the reliability of fuzzy technology for motif comparison tasks is proven.This work has been carried out as part of projects P08-TIC-4299 of J. A., Sevilla and TIN2006-13177 of DGICT, Madrid

Stratification of the severity of critically ill patients with classification trees

<p>Abstract</p> <p>Background</p> <p>Development of three classification trees (CT) based on the CART (<it>Classification and Regression Trees</it>), CHAID (<it>Chi-Square Automatic Interaction Detection</it>) and C4.5 methodologies for the calculation of probability of hospital mortality; the comparison of the results with the APACHE II, SAPS II and MPM II-24 scores, and with a model based on multiple logistic regression (LR).</p> <p>Methods</p> <p>Retrospective study of 2864 patients. Random partition (70:30) into a Development Set (DS) n = 1808 and Validation Set (VS) n = 808. Their properties of discrimination are compared with the ROC curve (AUC CI 95%), Percent of correct classification (PCC CI 95%); and the calibration with the Calibration Curve and the Standardized Mortality Ratio (SMR CI 95%).</p> <p>Results</p> <p>CTs are produced with a different selection of variables and decision rules: CART (5 variables and 8 decision rules), CHAID (7 variables and 15 rules) and C4.5 (6 variables and 10 rules). The common variables were: inotropic therapy, Glasgow, age, (A-a)O2 gradient and antecedent of chronic illness. In VS: all the models achieved acceptable discrimination with AUC above 0.7. CT: CART (0.75(0.71-0.81)), CHAID (0.76(0.72-0.79)) and C4.5 (0.76(0.73-0.80)). PCC: CART (72(69-75)), CHAID (72(69-75)) and C4.5 (76(73-79)). Calibration (SMR) better in the CT: CART (1.04(0.95-1.31)), CHAID (1.06(0.97-1.15) and C4.5 (1.08(0.98-1.16)).</p> <p>Conclusion</p> <p>With different methodologies of CTs, trees are generated with different selection of variables and decision rules. The CTs are easy to interpret, and they stratify the risk of hospital mortality. The CTs should be taken into account for the classification of the prognosis of critically ill patients.</p