Prevention and modulation of aminoglycoside ototoxicity (Review)

More than 60 years after their isolation and characterization, aminoglycoside (AG) antibiotics remain powerful agents in the treatment of severe gram-negative, enterococcal or mycobacterial infections. However, the clinical use of AGs is hampered by nephrotoxicity and ototoxicity, which often develop as a consequence of prolonged courses of therapy, or of administration of increased doses of these drugs. The discovery of non-ototoxic antibacterial agents, showing a wider spectrum of activity, has gradually decreased the use of AGs as first line antibiotics for many systemic infections. However, AGs are now undergoing an unexpected revival, being increasingly indicated for the treatment of severe emerging infections caused by organisms showing resistance to most first-line agents (e.g., multidrug-resistant tuberculosis, complicated nosocomially-acquired acute urinary tract infections). Increasing adoption of aminoglycosides poses again to scientists and physicians the problem of toxicity directed to the kidneys and to the inner ear. In particular, aminoglycoside-induced deafness can be profound and irreversible, especially in genetically predisposed patients. For this reason, an impressive amount of molecular strategies have been developed in the last decade to counteract the ototoxic effect of aminoglycosides. The present article overviews: i) the molecular mechanisms by which aminoglycosides exert their bactericidal activity, ii) the mechanisms whereby AGs exert their ototoxic activity in genetically-predisposed patients, iii) the drugs and compounds that have so far proven to prevent or modulate AG ototoxicity at the preclinical and/or clinical level, and iv) the dosage regimens that have so far been suggested to decrease the incidence of episodes of AG-induced ototoxicity

Paraoxonase 1 L55M, Q192R and paraoxonase 2 S311C alleles in atherothrombosis

Increased oxidative stress is known to play a role in the pathogenesis of atherosclerosis, and polymorphisms in genes encoding for enzymes involved in modulation of oxidant stress, such as paraoxonases (PONs), provide a potentially powerful approach to study the risk of disease susceptibility. Aim of our study is to investigate the possible association among PONs polymorphisms, clinical and metabolic factors, and atherothrombotic events in an Italian population. We evaluated in 105 subjects, with or without atherosclerotic risk factors, the presence of PON1 L55M, PON1 Q192R, and PON2 S311C genetic variants, as well as lipid profile, the concentration of aminothiols (blood reduced glutathione, plasma total glutathione, homocysteine, cysteine, cysteinyl glycine), and malondialdehyde as markers of lipid peroxidation

New application of intelligent agents in sporadic amyotrophic lateral sclerosis identifies unexpected specific genetic background

<p>Abstract</p> <p>Background</p> <p>Few genetic factors predisposing to the sporadic form of amyotrophic lateral sclerosis (ALS) have been identified, but the pathology itself seems to be a true multifactorial disease in which complex interactions between environmental and genetic susceptibility factors take place. The purpose of this study was to approach genetic data with an innovative statistical method such as artificial neural networks to identify a possible genetic background predisposing to the disease. A DNA multiarray panel was applied to genotype more than 60 polymorphisms within 35 genes selected from pathways of lipid and homocysteine metabolism, regulation of blood pressure, coagulation, inflammation, cellular adhesion and matrix integrity, in 54 sporadic ALS patients and 208 controls. Advanced intelligent systems based on novel coupling of artificial neural networks and evolutionary algorithms have been applied. The results obtained have been compared with those derived from the use of standard neural networks and classical statistical analysis</p> <p>Results</p> <p>Advanced intelligent systems based on novel coupling of artificial neural networks and evolutionary algorithms have been applied. The results obtained have been compared with those derived from the use of standard neural networks and classical statistical analysis. An unexpected discovery of a strong genetic background in sporadic ALS using a DNA multiarray panel and analytical processing of the data with advanced artificial neural networks was found. The predictive accuracy obtained with Linear Discriminant Analysis and Standard Artificial Neural Networks ranged from 70% to 79% (average 75.31%) and from 69.1 to 86.2% (average 76.6%) respectively. The corresponding value obtained with Advanced Intelligent Systems reached an average of 96.0% (range 94.4 to 97.6%). This latter approach allowed the identification of seven genetic variants essential to differentiate cases from controls: apolipoprotein E arg158cys; hepatic lipase -480 C/T; endothelial nitric oxide synthase 690 C/T and glu298asp; vitamin K-dependent coagulation factor seven arg353glu, glycoprotein Ia/IIa 873 G/A and E-selectin ser128arg.</p> <p>Conclusion</p> <p>This study provides an alternative and reliable method to approach complex diseases. Indeed, the application of a novel artificial intelligence-based method offers a new insight into genetic markers of sporadic ALS pointing out the existence of a strong genetic background.</p