Haem iron intake and risk of lung cancer in the European Prospective Investigation into Cancer and Nutrition (EPIC) cohort

Background Epidemiological studies suggest that haem iron, which is found predominantly in red meat and increases endogenous formation of carcinogenic N-nitroso compounds, may be positively associated with lung cancer. The objective was to examine the relationship between haem iron intake and lung cancer risk using detailed smoking history data and serum cotinine to control for potential confounding. Methods In the European Prospective Investigation into Cancer and Nutrition (EPIC), 416,746 individuals from 10 countries completed demographic and dietary questionnaires at recruitment. Cox proportional hazards models were used to estimate hazard ratios (HRs) and 95% confidence intervals (CIs) for incident lung cancer (n = 3731) risk relative to haem iron, non-haem iron, and total dietary iron intake. A corresponding analysis was conducted among a nested subset of 800 lung cancer cases and 1489 matched controls for whom serum cotinine was available. Results Haem iron was associated with lung cancer risk, including after adjustment for details of smoking history (time since quitting, number of cigarettes per day): as a continuous variable (HR per 0.3 mg/1000 kcal 1.03, 95% CI 1.00-1.07), and in the highest versus lowest quintile (HR 1.16, 95% CI 1.02-1.32; trend across quintiles: P = 0.035). In contrast, non-haem iron intake was related inversely with lung cancer risk; however, this association attenuated after adjustment for smoking history. Additional adjustment for serum cotinine did not considerably alter the associations detected in the nested case-control subset. Conclusions Greater haem iron intake may be modestly associated with lung cancer risk.Peer reviewe

Synthesis and phase transfer of monodisperse iron oxide (Fe3O4) nanocubes

Cube-shaped magnetic iron oxide nanoparticles were synthesised and studied with the aim to achieve superior magnetic properties. This study describes a straightforward and simple synthesis method for preparing monodisperse 11–14-nm superparamagnetic iron oxide nanocubes via an ‘effective monomer’ growth mechanism. The as-synthesised nanoparticles are insoluble in water. However, substitution of the non-polar ligands of the particles using a new method that involved an ionic compound generated colloidally stable and water dispersible cube-shaped particles with a very small hydrodynamic diameter. The cubes displayed superior magnetic properties over spherical particles.

A TiO2 Nanofiber-Carbon Nanotube-Composite Photoanode for Improved Efficiency in Dye-Sensitized Solar Cells

A light-scattering layer fabricated from electrospun titanium dioxide nanofibers (TiO2-NFs) and single-walled carbon nanotubes (SWCNTs) formed a fiber-based photoanode. The nanocomposite scattering layer had a lawn-like structure and integration of carbon nanotubes into the NF photoanodes increased the power conversion efficiency from 2.9% to 4.8% under 1Sun illumination. Under reduced light intensity (0.25Sun), TiO2-NF and TiO2-NF/SWCNT-based DSSCs reached PCE values of up to 3.7% and 6.6%, respectively

Correlating diameters to characteristic fluorescence lifetimes.

<p>(A) Diameter histograms (<i>n</i> = 95, 127, 88, and 106) for the four samples (blue, green, yellow, and red) extracted from transmission electron microscopy and the model fits (solid lines). (B) Fluorescence lifetime measurements and the model fits (black solid lines, overlayed on top of the experimental data) for the four samples (blue, green, yellow, and red, vertical rescaling has been performed to avoid occlusion). (C) Estimated joint distributions of diameters and characteristic lifetimes with marginal diameter distributions at the top and marginal characteristic lifetime distributions at the right.</p

Correlating diameters to emission wavelengths.

<p>(A) Diameter histograms (<i>n</i> = 95, 127, 88, and 106) for the four samples (blue, green, yellow, and red) extracted from transmission electron microscopy and the model fits (solid lines). (B) Steady-state emission spectra (dashed lines) and the model fits (solid lines) for the four samples (blue, green, yellow, and red). (C) Estimated joint distributions of diameters and wavelengths with marginal diameter distributions at the top and marginal wavelength distributions at the right.</p

Monofunctionalization and Dimerization of Nanoparticles Using Coordination Chemistry

This paper describes a strategy for controlled nanoparticle dimerization by using a solid support approach. Two types of nanoparticles have been linked by using a 5-([2,2':6',2"-terpyridine]-4'-yloxy)pentan-1-amine (terpy-amine) iron complex. The strategy includes two major steps: first, the monofunctionalization of individual nanoparticles with terpy-amine ligand molecules on a solid support, followed by release of monofunctionalized particles and subsequent dimerization. The versatility of the approach was demonstrated by dimerizing two different types of nanoparticles: spherical gold and cube-shaped iron oxide nanoparticles

The power of heterogeneity : Parameter relationships from distributions

Complex scientific data is becoming the norm, many disciplines are growing immensely data-rich, and higher-dimensional measurements are performed to resolve complex relationships between parameters. Inherently multi-dimensional measurements can directly provide information on both the distributions of individual parameters and the relationships between them, such as in nuclear magnetic resonance and optical spectroscopy. However, when data originates from different measurements and comes in different forms, resolving parameter relationships is a matter of data analysis rather than experiment. We present a method for resolving relationships between parameters that are distributed individually and also correlated. In two case studies, we model the relationships between diameter and luminescence properties of quantum dots and the relationship between molecular weight and diffusion coefficient for polymers. Although it is expected that resolving complicated correlated relationships require inherently multi-dimensional measurements, our method constitutes a useful contribution to the modelling of quantitative relationships between correlated parameters and measurements. We emphasise the general applicability of the method in fields where heterogeneity and complex distributions of parameters are obstacles to scientific insight