No association between polymorphisms in the BDNF gene and age at onset in Huntington disease

BACKGROUND: Recent evidence suggests that brain-derived neurotrophic factor (BDNF) is an attractive candidate for modifying age at onset (AO) in Huntington disease (HD). In particular, the functional Val66Met polymorphism appeared to exert a significant effect. Here we evaluate BDNF variability with respect to AO of HD using markers that represent the entire locus. METHODS: Five selected tagging polymorphisms were genotyped across a 65 kb region comprising the BDNF gene in a well established cohort of 250 unrelated German HD patients. RESULTS: Addition of BDNF genotype variations or one of the marker haplotypes to the effect of CAG repeat lengths did not affect the variance of the AO. CONCLUSION: We were unable to verify a recently reported association between the functional Val66Met polymorphism in the BDNF gene and AO in HD. From our findings, we conclude that neither sequence variations in nor near the gene contribute significantly to the variance of AO

Rating scale analysis with latent class models

latent class analysis, rating models, EM-algorithm, ordered categories,

Optimization of adsorptive removal of α-toluic acid by CaO2 nanoparticles using response surface methodology

The present work addresses the optimization of process parameters for adsorptive removal of α-toluic acid by calcium peroxide (CaO2) nanoparticles using response surface methodology (RSM). CaO2 nanoparticles were synthesized by chemical precipitation method and confirmed by Transmission electron microscopy (TEM) and high-resolution TEM (HRTEM) analysis which shows the CaO2 nanoparticles size range of 5–15 nm. A series of batch adsorption experiments were performed using CaO2 nanoparticles to remove α-toluic acid from the aqueous solution. Further, an experimental based central composite design (CCD) was developed to study the interactive effect of CaO2 adsorbent dosage, initial concentration of α-toluic acid, and contact time on α-toluic acid removal efficiency (response) and optimization of the process. Analysis of variance (ANOVA) was performed to determine the significance of the individual and the interactive effects of variables on the response. The model predicted response showed a good agreement with the experimental response, and the coefficient of determination, (R2) was 0.92. Among the variables, the interactive effect of adsorbent dosage and the initial α-toluic acid concentration was found to have more influence on the response than the contact time. Numerical optimization of process by RSM showed the optimal adsorbent dosage, initial concentration of α-toluic acid, and contact time as 0.03 g, 7.06 g/L, and 34 min respectively. The predicted removal efficiency was 99.50%. The experiments performed under these conditions showed α-toluic acid removal efficiency up to 98.05%, which confirmed the adequacy of the model prediction