New insights on dimethylaminoethanol (DMAE) features as a free radical scavenger.

Recently, a number of synthetic drugs used in a variety of therapeutic indications have been reported to have antiaging effects. Among them, Dimethylaminoethanol (DMAE), an anologue of dietylaminoethanol, is a precursor of choline, which in turn allows the brain to optimize the production of acetylcholine that is a primary neurotransmitter involved in learning and memory. The data presented here includes new information on the ability of the compound to scavenge specific free radicals, assessed by Electron Spectroscopic Resonance (EPR), to further analyze the role of DMAE as an antioxidant.

Application of response surface methodology and artificial neural networks for optimization of recombinant Oryza sativa non-symbiotic hemoglobin 1 production by Escherichia coli in medium containing byproduct glycerol.

Production of recombinant Oryza sativa non-symbiotic hemoglobin 1 (OsHb1) by Escherichia coli was maximized in shake-flask cultures in media containing tryptone, yeast extract, sodium chloride and byproduct glycerol from biodiesel production. Response surface methodology (RSM) and artificial neural networks (ANNs), followed by multiple response optimization through a desirability function were applied to evaluate the amount of OsHb1 produced. The results obtained by the application of ANNs were more reliable since better statistical parameters were obtained.

Application of response surface methodology and artificial neural networks for optimization of recombinant Oryza sativa non-symbiotic hemoglobin 1 production by Escherichia coli in medium containing byproduct glycerol.

Production of recombinant Oryza sativa non-symbiotic hemoglobin 1 (OsHb1) by Escherichia coli was maximized in shake-flask cultures in media containing tryptone, yeast extract, sodium chloride and byproduct glycerol from biodiesel production. Response surface methodology (RSM) and artificial neural networks (ANNs), followed by multiple response optimization through a desirability function were applied to evaluate the amount of OsHb1 produced. The results obtained by the application of ANNs were more reliable since better statistical parameters were obtained.