Tin Oxide Nanorod Array-Based Electrochemical Hydrogen Peroxide Biosensor

SnO2 nanorod array grown directly on alloy substrate has been employed as the working electrode of H2O2 biosensor. Single-crystalline SnO2 nanorods provide not only low isoelectric point and enough void spaces for facile horseradish peroxidase (HRP) immobilization but also numerous conductive channels for electron transport to and from current collector; thus, leading to direct electrochemistry of HRP. The nanorod array-based biosensor demonstrates high H2O2 sensing performance in terms of excellent sensitivity (379 μA mM−1 cm−2), low detection limit (0.2 μM) and high selectivity with the apparent Michaelis–Menten constant estimated to be as small as 33.9 μM. Our work further demonstrates the advantages of ordered array architecture in electrochemical device application and sheds light on the construction of other high-performance enzymatic biosensors

Analytic philosophy for biomedical research: the imperative of applying yesterday's timeless messages to today's impasses

The mantra that "the best way to predict the future is to invent it" (attributed to the computer scientist Alan Kay) exemplifies some of the expectations from the technical and innovative sides of biomedical research at present. However, for technical advancements to make real impacts both on patient health and genuine scientific understanding, quite a number of lingering challenges facing the entire spectrum from protein biology all the way to randomized controlled trials should start to be overcome. The proposal in this chapter is that philosophy is essential in this process. By reviewing select examples from the history of science and philosophy, disciplines which were indistinguishable until the mid-nineteenth century, I argue that progress toward the many impasses in biomedicine can be achieved by emphasizing theoretical work (in the true sense of the word 'theory') as a vital foundation for experimental biology. Furthermore, a philosophical biology program that could provide a framework for theoretical investigations is outlined

Single-cell analysis tools for drug discovery and development

The genetic, functional or compositional heterogeneity of healthy and diseased tissues presents major challenges in drug discovery and development. Such heterogeneity hinders the design of accurate disease models and can confound the interpretation of biomarker levels and of patient responses to specific therapies. The complex nature of virtually all tissues has motivated the development of tools for single-cell genomic, transcriptomic and multiplex proteomic analyses. Here, we review these tools and assess their advantages and limitations. Emerging applications of single cell analysis tools in drug discovery and development, particularly in the field of oncology, are discussed

The association between maternal 25-hydroxyvitamin D concentration during gestation and early childhood cardio-metabolic outcomes: is there interaction with pre-pregnancy BMI?

Both maternal 25-hydroxyvitamin D (25OHD) status and pre-pregnancy BMI (pBMI) may influence offspring cardio-metabolic outcomes. Lower 25OHD concentrations have been observed in women with both low and high pBMIs, but the combined influence of pBMI and 25OHD on offspring cardio-metabolic outcomes is unknown. Therefore, this study investigated the role of pBMI in the association between maternal 25OHD concentration and cardio-metabolic outcomes in 5-6 year old children. Data were obtained from the ABCD cohort study and 1882 mother-child pairs were included. The offspring outcomes investigated were systolic and diastolic blood pressure, heart rate, BMI, body fat percentage (%BF), waist-to-height ratio, total cholesterol, LDL cholesterol, HDL cholesterol, triglycerides, glucose, C-peptide, and insulin resistance (HOMA2-IR). 62% of the C-peptide samples were below the detection limit and were thus imputed using survival analysis. Models were corrected for maternal and offspring covariates and tested for interaction with pBMI. Interaction with pBMI was observed in the associations with insulin resistance markers: in offspring of overweight mothers (≥25.0 kg/m2), a 10 nmol/L increase in maternal 25OHD was associated with a 0.007(99%CI:-0.01,-0.001) nmol/L decrease in C-peptide and a 0.02(99%CI:-0.03,-0.004) decrease in HOMA2-IR. When only non-imputed data were analyzed, there was a trend for interaction in the relationship but the results lost significance. Interaction with pBMI was not observed for the other outcomes. A 10 nmol/L increase in maternal 25OHD was significantly associated with a 0.13%(99%CI:-0.3,-0.003) decrease in %BF after correction for maternal and child covariates. Thus, intrauterine exposure to both low 25OHD and maternal overweight may be associated with increased insulin resistance in offspring, while exposure to low 25OHD in utero may be associated with increased offspring %BF with no interactive effects from pBMI. Due to the limitations of this study, these results are not conclusive, however the observations of this study pose important research questions for future studies to investigate

Multi-phenotype analyses of hemostatic traits with cardiovascular events reveal novel genetic associations

Background: Multi-phenotype analysis of genetically correlated phenotypes can increase the statistical power to detect loci associated with multiple traits, leading to the discovery of novel loci. This is the first study to date to comprehensively analyze the shared genetic effects within different hemostatic traits, and between these and their associated disease outcomes. Objectives: To discover novel genetic associations by combining summary data of correlated hemostatic traits and disease events. Methods: Summary statistics from genome wide-association studies (GWAS) from seven hemostatic traits (factor VII [FVII], factor VIII [FVIII], von Willebrand factor [VWF] factor XI [FXI], fibrinogen, tissue plasminogen activator [tPA], plasminogen activator inhibitor 1 [PAI-1]) and three major cardiovascular (CV) events (venous thromboembolism [VTE], coronary artery disease [CAD], ischemic stroke [IS]), were combined in 27 multi-trait combinations using metaUSAT. Genetic correlations between phenotypes were calculated using Linkage Disequilibrium Score Regression (LDSC). Newly associated loci were investigated for colocalization. We considered a significance threshold of 1.85 × 10−9 obtained after applying Bonferroni correction for the number of multi-trait combinations performed (n = 27). Results: Across the 27 multi-trait analyses, we found 4 novel pleiotropic loci (XXYLT1, KNG1, SUGP1/MAU2, TBL2/MLXIPL) that were not significant in the original individual datasets, were not described in previous GWAS for the individual traits, and that presented a common associated variant between the studied phenotypes. Conclusions: The discovery of four novel loci contributes to the understanding of the relationship between hemostasis and CV events and elucidate common genetic factors between these traits