Involvement of SNPs in miR-3117 and miR-3689d2 in childhood acute lymphoblastic leukemia risk

Acute lymphoblastic leukemia (ALL) is the most common cancer in children. Numerous studies have shown that microRNAs (miRNAs) could play a role in this disease. Nowadays, more than 2500 miRNAs have been described, that regulate more than 50% of genes, including those involved in B-cell maturation, differentiation and proliferation. Genetic variants in miRNAs can alter their own levels or function, affecting their target gene expression, and then, may affect ALL risk. Therefore, the aim of this study was to determine the role of miRNA genetic variants in B-ALL susceptibility. We analyzed all variants in pre-miRNAs (MAF > 1%) in two independent cohorts from Spain and Slovenia and inferred their functional effect by in silico analysis. SNPs rs12402181 in miR-3117 and rs62571442 in miR-3689d2 were associated with ALL risk in both cohorts, possibly through their effect on MAPK signalling pathway. These SNPs could be novel markers for ALL susceptibility

Role of Surface Energy and Nano-Roughness in the Removal Efficiency of Bacterial Contamination by Nonwoven Wipes from Frequently Touched Surfaces

Healthcare associated infections (HCAIs) are responsible for substantial patient morbidity, mortality and economic cost. Infection control strategies for reducing rates of transmission include the use of nonwoven wipes to remove pathogenic bacteria from frequently touched surfaces. Wiping is a dynamic process that involves physicochemical mechanisms to detach and transfer bacteria to fibre surfaces within the wipe. The purpose of this study was to determine the extent to which systematic changes in fibre surface energy and nano-roughness influence removal of bacteria from an abiotic polymer surface in dry wiping conditions, without liquid detergents or disinfectants. Nonwoven wipe substrates composed of two commonly used fibre types, lyocell (cellulosic) and polypropylene, with different surface energies and nano-roughnesses, were manufactured using pilot-scale nonwoven facilities to produce samples of comparable structure and dimensional properties. The surface energy and nano-roughness of some lyocell substrates were further adjusted by either oxygen (O2) or hexafluoroethane (C2F6) gas plasma treatment. Static adpression wiping of an inoculated surface under dry conditions produced removal efficiencies of between 9.4% and 15.7%, with no significant difference (p < 0.05) in the relative removal efficiencies of Escherichia coli, Staphylococcus aureus or Enterococcus faecalis. However, dynamic wiping markedly increased peak wiping efficiencies to over 50%, with a minimum increase in removal efficiency of 12.5% and a maximum increase in removal efficiency of 37.9% (all significant at p < 0.05) compared with static wiping, depending on fibre type and bacterium. In dry, dynamic wiping conditions, nonwoven wipe substrates with a surface energy closest to that of the contaminated surface produced the highest E. coli removal efficiency, while the associated increase in fibre nano-roughness abrogated this trend with S. aureus and E. faecalis. Plasma modification of the nano-roughness and surface energy of fibres in nonwoven wipes was found to influence the relative removal efficiencies of common bacterial pathogens from model healthcare surfaces under dynamic wiping conditions

Use of Gaussian process regression for radiation mapping of a nuclear reactor with a mobile robot

Collection and interpolation of radiation observations is of vital importance to support routine operations in the nuclear sector globally, as well as for completing surveys during crisis response. To reduce exposure to ionizing radiation that human workers can be subjected to during such surveys, there is a strong desire to utilise robotic systems. Previous approaches to interpolate measurements taken from nuclear facilities to reconstruct radiological maps of an environment cannot be applied accurately to data collected from a robotic survey as they are unable to cope well with irregularly spaced, noisy, low count data. In this work, a novel approach to interpolating radiation measurements collected from a robot is proposed that overcomes the problems associated with sparse and noisy measurements. The proposed method integrates an appropriate kernel, benchmarked against the radiation transport code MCNP6, into the Gaussian Process Regression technique. The suitability of the proposed technique is demonstrated through its application to data collected from a bespoke robotic system used to conduct a survey of the Joz̆ef Stefan Institute TRIGA Mark II nuclear reactor during steady state operation, where it is shown to successfully reconstruct gamma dosimetry estimates in the reactor hall and aid in identifying sources of ionizing radiation

Radiation hardness studies and detector characterisation at the JSI TRIGA reactor

The JSI TRIGA reactor features several in-core and ex-core irradiation facilities, each having different properties, such as neutron/gamma flux intensity, spectra and irradiation volume. A series of experiments and calculations was performed in order to characterise radiation fields in irradiation channel thus allowing users to perform irradiations in a well characterised environment. Since 2001 the reactor has been heavily used for radiation hardness studies for components used at accelerators such as the Large Hadron Collider (LHC) at CERN. Since 2010 it has been extensively used for testing of new detectors and innovative data acquisition systems and methods developed and used by the CEA. Recently, several campaigns were initiated to characterise the gamma field in the reactor and use the experimental data for improvement of the treatment of delayed gammas in Monte Carlo particle transport codes. In the future it is planned to extend the testing options by employing pulse mode operation, installation of a high energy gamma ray irradiation facility and allow irradiation of larger samples at elevated temperature