Effect of coffee combining green coffee bean constituents with typical roasting products on the Nrf2/ARE pathway in vitro and in vivo

This study investigated Nrf2-activating properties of a coffee blend combining raw coffee bean constituents with 5-O-caffeoylquinic acid (CGA) as a lead component with typical roasting products such as N-methylpyridinium (NMP). In cell culture (HT29) the respective coffee extract (CN-CE) increased nuclear Nrf2 translocation and enhanced the transcription of ARE-dependent genes as exemplified for NAD(P)H:quinone oxidoreductase and glutathione-S-transferase (GST)A1, reflected in the protein level by an increase in GST enzyme activity. In a pilot human intervention study (29 healthy volunteers), daily consumption of 750 mL of CN-coffee for 4 weeks increased Nrf2 transcription in peripheral blood lymphocytes on average. However, the transcriptional response pattern of Nrf2/ARE-dependent genes showed substantial interindividual variations. The presence of SNPs in the Nrf2-promoter, reported recently, as well as the detection of GSTT1*0 (null) genotypes in the study collective strengthens the hypothesis that coffee acts as a modulator of Nrf2-dependent gene response in humans, but genetic polymorphisms play an important role in the individual response pattern.

Ergonomic assessment of hospital bed moving using DHM Siemens JACK

While the indirect and direct cost of occupational musculoskeletal disorders (MSD) causes a significant burden on the health system, lower back pain (LBP) is associated with a significant portion of MSD. In Australia, the highest prevalence of MSD exists for health care workers, such as nurses. The digital human model (DHM) Siemens JACK was used to investigate if hospital bed pushing, a simple task and hazard that is commonly associated with LBP, can be simulated and ergonomically assessed in a virtual environment. It was found that while JACK has implemented a range of common physical work assessment methods, the simulation of dynamic bed pushing remains a challenge due to the complex interface between the floor and wheels, which can only be insufficiently modelle

Particle number and mass emission factors from combustion of wood samples collected from Queensland forest

Knowledge of particle emission characteristics associated with forest fires and in general, biomass burning, is becoming increasingly important due to the impact of these emissions on human health. Of particular importance is developing a better understanding of the size distribution of particles generated from forest combustion under different environmental conditions, as well as provision of emission factors for different particle size ranges. This study was aimed at quantifying particle emission factors from four types of wood found in South East Queensland forests: Spotted Gum (Corymbia citriodora), Red Gum (Eucalypt tereticornis), Blood Gum (Eucalypt intermedia), and Iron bark (Eucalypt decorticans); under controlled laboratory conditions. The experimental set up included a modified commercial stove connected to a dilution system designed for the conditions of the study. Measurements of particle number size distribution and concentration resulting from the burning of woods with a relatively homogenous moisture content (in the range of 15 to 26 %) and for different rates of burning were performed using a TSI Scanning Mobility Particle Sizer (SMPS) in the size range from 10 to 600 nm and a TSI Dust Trak for PM2.5. The results of the study in terms of the relationship between particle number size distribution and different condition of burning for different species show that particle number emission factors and PM2.5 mass emission factors depend on the type of wood and the burning rate; fast burning or slow burning. The average particle number emission factors for fast burning conditions are in the range of 3.3 x 1015 to 5.7 x 1015 particles/kg, and for PM2.5 are in the range of 139 to 217 mg/kg.