Circulating Biomarkers for Laboratory Diagnostics of Atherosclerosis-Literature Review.

Atherosclerosis is still considered a disease burden with long-term damaging processes towards the cardiovascular system. Evaluation of atherosclerotic stages requires the use of independent markers such as those already considered traditional, that remain the main therapeutic target for patients with atherosclerosis, together with emerging biomarkers. The challenge is finding models of predictive markers that are particularly tailored to detect and evaluate the evolution of incipient vascular lesions. Important advances have been made in this field, resulting in a more comprehensible and stronger linkage between the lipidic profile and the continuous inflammatory process. In this paper, we analysed the most recent data from the literature studying the molecular mechanisms of biomarkers and their involvement in the cascade of events that occur in the pathophysiology of atherosclerosis.

Enhanced growth and ß-galactosidase production on Escherichia coli using oxygen vectors.

The addition of n-dodecane (between 1-3%) to the Escherichia coli fermentation broth in a mechanically agitated and aerated bioreactor revealed improved DO (dissolved oxygen) levels induced during fermentation which lead to an increase in biomass productivity and faster glucose consumption. The maximum values for enzyme activity (increased with 43% compared with the control) and k L a (the volumetric mass transfer coefficient) were obtained for the addition of 2% v/v n-dodecane in the bioreactor, due to the fact that oxygen limitation during the exponential growth phase of the bacterium can repress ß-galactosidase production. The oxygen vector addition increased the available dissolved oxygen and activated a redox-sensitive regulation and an elevated intracellular oxidative metabolism that lead to the enhancement in E. coli biomass accumulation and a more accurate protein folding of ß-galactosidase that would increase its activity. In addition to the experimental analysis, a complex model, developed using an improved version of Bacterial Foraging Algorithm and Artificial Neural Networks, was proposed, with a good average absolute value (6.2% in the training phase and 7.28% in the testing phase) between the process dynamic and the predictions generated by the model.