The D1822V APC polymorphism interacts with fat, calcium, and fiber intakes in modulating the risk of colorectal cancer in Portuguese persons

Background - Both genetic and environmental factors affect the risk of colorectal cancer (CRC). Objective - We aimed to examine the interaction between the D1822V polymorphism of the APC gene and dietary intake in persons with CRC. Design - Persons with CRC (n = 196) and 200 healthy volunteers, matched for age and sex in a case-control study, were evaluated with respect to nutritional status and lifestyle factors and for the D1822V polymorphism. Results - No significant differences were observed in energy and macronutrient intakes. Cases had significantly (P < 0.05) lower intakes of carotenes, vitamins C and E, folate, and calcium than did controls. Fiber intake was significantly (P = 0.004) lower in cases than in controls, whereas alcohol consumption was associated with a 2-fold risk of CRC. In addition, cases were significantly (P = 0.001) more likely than were controls to be sedentary. The homozygous variant for the APC gene (VV) was found in 4.6% of cases and in 3.5% of controls. Examination of the potential interactions between diet and genotype found that a high cholesterol intake was associated with a greater risk of colorectal cancer only in noncarriers (DD) of the D1822V APC allele (odds ratio: 1.66; 95% CI: 1.00, 2.76). In contrast, high fiber and calcium intakes were more markedly associated with a lower risk of CRC in patients carrying the polymorphic allele (DV/VV) (odds ratio: 0.50; 95% CI: 0.27, 0.94 for fiber; odds ratio: 0.51; 95% CI: 0.28, 0.93 for calcium) than in those without that allele. Conclusion - These results suggest a significant interaction between the D1822V polymorphism and the dietary intakes of cholesterol, calcium, and fiber for CRC risk.

Mass spectrometry improvement on an high current ion implanter

The development of accurate mass spectrometry, enabling the identification of all the ions extracted from the ion source in a high current implanter is described. The spectrometry system uses two signals (x-y graphic), one proportional to the magnetic field (x-axes), taken from the high-voltage potential with an optic fiber system, and the other proportional to the beam current intensity (y-axes), taken from a beam-stop. The ion beam mass register in a mass spectrum of all the elements magnetically analyzed with the same radius and defined by a pair of analyzing slits as a function of their beam intensity is presented. The developed system uses a PC to control the displaying of the extracted beam mass spectrum, and also recording of all data acquired for posterior analysis. The operator uses a LabView code that enables the interfacing between an I/O board and the ion implanter. The experimental results from an ion implantation experiment are shown. (C) 2011 Elsevier B.V. All rights reserved.

In Situ Near-Infrared (NIR) Versus High-Throughput Mid- Infrared (MIR) Spectroscopy to Monitor Biopharmaceutical Production

The development of biopharmaceutical manufacturing processes presents critical constraints, with the major constraint being that living cells synthesize these molecules, presenting inherent behavior variability due to their high sensitivity to small fluctuations in the cultivation environment. To speed up the development process and to control this critical manufacturing step, it is relevant to develop high-throughput and in situ monitoring techniques, respectively. Here, high-throughput mid-infrared (MIR) spectral analysis of dehydrated cell pellets and in situ near-infrared (NIR) spectral analysis of the whole culture broth were compared to monitor plasmid production in recombinant Escherichia coil cultures. Good partial least squares (PLS) regression models were built, either based on MIR or NIR spectral data, yielding high coefficients of determination (R-2) and low predictive errors (root mean square error, or RMSE) to estimate host cell growth, plasmid production, carbon source consumption (glucose and glycerol), and by-product acetate production and consumption. The predictive errors for biomass, plasmid, glucose, glycerol, and acetate based on MIR data were 0.7 g/L, 9 mg/L, 0.3 g/L, 0.4 g/L, and 0.4 g/L, respectively, whereas for NIR data the predictive errors obtained were 0.4 g/L, 8 mg/L, 0.3 g/L, 0.2 g/L, and 0.4 g/L, respectively. The models obtained are robust as they are valid for cultivations conducted with different media compositions and with different cultivation strategies (batch and fed-batch). Besides being conducted in situ with a sterilized fiber optic probe, NIR spectroscopy allows building PLS models for estimating plasmid, glucose, and acetate that are as accurate as those obtained from the high-throughput MIR setup, and better models for estimating biomass and glycerol, yielding a decrease in 57 and 50% of the RMSE, respectively, compared to the MIR setup. However, MIR spectroscopy could be a valid alternative in the case of optimization protocols, due to possible space constraints or high costs associated with the use of multi-fiber optic probes for multi-bioreactors. In this case, MIR could be conducted in a high-throughput manner, analyzing hundreds of culture samples in a rapid and automatic mode.