No abstract

IntroductionDiabetes is a worldwide health problem that is associated with severe complications. Advanced Glycation End products (AGEs) such as Nε-(carboxymethyl)lysine, which result from chronic hyperglycemia, accumulate in the skin of patients with diabetes. The effect of AGEs on fibroblast functionality and their impact on wound healing are still poorly understood.Research design and methodsTo investigate this, we treated cultured human fibroblasts with 0.6 mM glyoxal to induce acute glycation. The behavior of fibroblasts was analyzed by time-lapse monolayer wounding healing assay, seahorse technology and atomic force microscopy. Production of extracellular matrix was studied by transmission electronic microscopy and western blot. Lipid metabolism was investigated by staining of lipid droplets (LDs) with BODIPY 493/503.ResultsWe found that the proliferative and migratory capacities of the cells were greatly reduced by glycation, which could be explained by an increase in fibroblast tensile strength. Measurement of the cellular energy balance did not indicate that there was a change in the rate of oxygen consumption of the fibroblasts. Assessment of collagen I revealed that glyoxal did not influence type I collagen secretion although it did disrupt collagen I maturation and it prevented its deposition in the extracellular matrix. We noted a pronounced increase in the number of LDs after glyoxal treatment. AMPK phosphorylation was reduced by glyoxal treatment but it was not responsible for the accumulation of LDs.ConclusionGlyoxal promotes a change in fibroblast behavior in favor of lipogenic activity that could be involved in delaying wound healing.

No abstract

From a training set of 7200 chemicals, a back-propagation neural network (BNN) model was developed for calculating the 1-octanol/water partition coefficient (log P) of molecules containing nitrogen, oxygen, halogen, phosphorus, and/or sulfur atoms. Chemicals were described by means of autocorrelation vectors encoding hydrophobicity, molar refractivity, H-bonding acceptor ability, and H-bonding donor ability. A 35/32/1 composite network composed of four configurations was selected as the final model (root-mean-square error (RMS) = 0.37, r = 0.97) because it provided the best simulation results (RMS = 0.39, r = 0.98) on an external testing set of 519 molecules. This final model compared favorably with a recently published BNN model using variables (atoms and bonds) derived from connection matrices.