We present a model that quantitatively describes the performance of microfabricated electrophoretic devices filled with linear polyacrylamide as replaceable sieving material for single-stranded DNA analyses. The dependence of resolution on various separation parameters such as selectivity, diffusion, injector size, device length, and channel folding was investigated. A previously predicted dependence of longitudinal diffusion coefficient on electric field strength has been verified. We have used this model to develop and optimize microfabricated electrophoretic devices for DNA analyses. For single-color DNA sequencing mixtures, we routinely achieve separations of 400 bases in under 14 min at 200 V/cm, and separation of 350 bases in only 7 min at 400 V/cm, with a minimum resolution of R = 0.5. Our results also indicate reduced fragment biasing and efficient sample stacking for DNA sample loading on microfabricated devices.

Background— In several cross-sectional analyses, circulating baseline levels of galectin-3, a protein involved in myocardial fibrosis and remodeling, have been associated with increased risk for morbidity and mortality in patients with heart failure (HF). The importance and clinical use of repeated measurements of galectin-3 have not yet been reported. Methods and Results— Plasma galectin-3 was measured at baseline and at 3 months in patients enrolled in the Controlled Rosuvastatin Multinational Trial in Heart Failure (CORONA) trial (n=1329), and at baseline and at 6 months in patients enrolled in the Coordinating Study Evaluating Outcomes of Advising and Counseling Failure (COACH) trial (n=324). Patient results were analyzed by categorical and percentage changes in galectin-3 level. A threshold value of 17.8 ng/mL or 15% change from baseline was used to categorize patients. Increasing galectin-3 levels over time, from a low to high galectin-3 category, were associated with significantly more HF hospitalization and mortality compared with stable or decreasing galectin-3 levels (hazard ratio in CORONA, 1.60; 95% confidence interval, 1.13–2.25; P =0.007; hazard ratio in COACH, 2.38; 95% confidence interval, 1.02–5.55; P =0.046). In addition, patients whose galectin-3 increased by >15% between measurements had a 50% higher relative hazard of adverse event than those whose galectin-3 stayed within ±15% of the baseline value, independent of age, sex, diabetes mellitus, left ventricular ejection fraction, renal function, medication (β-blocker, angiotensin converting enzyme inhibitor, and angiotensin receptor blocker), and N-terminal probrain natriuretic peptide (hazard ratio in CORONA, 1.50; 95% confidence interval, 1.17–1.92; P =0.001). The impact of changing galectin-3 levels on other secondary end points was comparable. Conclusions— In 2 large cohorts of patients with chronic and acute decompensated HF, repeated measurements of galectin-3 level provided important and significant prognostic value in identifying patients with HF at elevated risk for subsequent HF morbidity and mortality.

Systems biology has developed in recent years from a technology-driven enterprise to a new strategic tool in Life Sciences, particularly for innovative drug discovery and drug development. Combining the ultimate in systems phenotyping with in-depth investigations of biomolecular mechanisms will enable a revolution in our understanding of disease pathology and will advance translational medicine, combination therapies, integrative medicine, and personalized medicine. A prerequisite for deriving the benefits of such a systems approach is a reliable and well-validated bioanalytical platform across complementary measurement modalities, especially transcriptomics, proteomics, and metabolomics, that operates in concert with a megavariate integrative biostatistical/bioinformatics platform. The applicable bioanalytical methodologies must undergo an intense development trajectory to reach an optimal level of reliable performance and quantitative reproducibility in daily practice. Moreover, to generate such enabling systems information, it is essential to design experiments based on an understanding of the complexity and statistical characteristics of the large data sets created. Novel insights into biology and system science can be obtained by evaluating the molecular connectivity within a system through correlation networks, by monitoring the dynamics of a system, or by measuring the system responses to perturbations such as drug administration or challenge tests. In addition, cross-compartment communication and control/feed-back mechanisms can be studied via correlation network analyses. All these data analyses depend critically upon the generation of high-quality bioanalytical platform data sets. The emphasis of this paper is on the characteristics of a bioanalytical platform that we have developed to generate such data sets. The broad applicability of Systems Biology in pharmaceutical research and development is discussed with examples in disease biomarker research, in pharmacology using system response monitoring, and in cross-compartment system toxicology assessment.

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AimsThis study was conducted to determine whether galectin-3, a b-galactoside-binding lectin, plays a role in the pathogenesis of heart failure (HF). Methods and resultsGalectin-3 was measured at baseline (n ¼ 1650), after 4 months (n ¼ 1346), and after 12 months (n ¼ 1097) in the Valsartan Heart Failure Trial (Val-HeFT). Galectin-3 levels at baseline ranged from 4.8 to 53 ng/mL. Higher levels were associated with features of worse HF. In a fully adjusted Cox regression model comprising 23 other prognostic variables, baseline galectin-3 was not associated with the risks of all-cause mortality, the composite of the first morbid event, or hospitalization for HF. However, when changes in galectin-3 over time were examined, the increases in galectin-3 between baseline and 4 months were independently and significantly associated with the risks of subsequent all-cause mortality, first morbid event, and hospitalizations for HF, even after adjusting for all baseline and concurrent changes in all variables including estimated glomerular filtration rate (eGFR) and NT-proBNP. The strongest correlate of galectin-3 levels was eGFR, which accounted for 20% of the variability in galectin-3 levels at baseline. There was a significant interaction (P ¼ 0.03) between baseline galectin-3 and the effect of valsartan on hospitalizations for HF. Valsartan caused a significant 44% reduction in hospitalizations for HF in patients with galectin-3 levels below the median level of 16.2 ng/mL, but not in patients with levels above the median. ConclusionsGalectin-3 levels are elevated in a substantial proportion of patients with HF, particularly those with more severe HF and renal dysfunction. Galectin-3 increased over time in this cohort, and the increase was independently associated with worse outcomes. Valsartan use was associated with a reduction in hospitalizations for HF in patients with low galectin-3, but not in those with higher levels of galectin-