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The S100B is a member of the S100 family of "E" helix-loop-"F" helix structure (EF) hand calcium-binding proteins expressed in diverse glial, selected neuronal, and various peripheral cells, exerting differential effects. In particular, this review compiles descriptions of the detection of S100B in different brain cells localized in specific regions during the development of humans, mice, and rats. Then, it summarizes S100B's actions on the differentiation, growth, and maturation of glial and neuronal cells in humans and rodents. Particular emphasis is placed on S100B regulation of the differentiation and maturation of astrocytes, oligodendrocytes (OL), and the stimulation of dendritic development in serotoninergic and cerebellar neurons during embryogenesis. We also summarized reports that associate morphological alterations (impaired neurite outgrowth, neuronal migration, altered radial glial cell morphology) of specific neural cell groups during neurodevelopment and functional disturbances (slower rate of weight gain, impaired spatial learning) with changes in the expression of S100B caused by different conditions and stimuli as exposure to stress, ethanol, cocaine and congenital conditions such as Down's Syndrome. Taken together, this evidence highlights the impact of the expression and early actions of S100B in astrocytes, OL, and neurons during brain development, which is reflected in the alterations in differentiation, growth, and maturation of these cells. This allows the integration of a spatiotemporal panorama of S100B actions in glial and neuronal cells in the developing brain. [ABSTRACT FROM AUTHOR]

Copyright of Ginecología y Obstetricia de México is the property of Federacion Mexicana de Ginecologia y Obstetricia and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Copyright of Ginecología y Obstetricia de México is the property of Federacion Mexicana de Ginecologia y Obstetricia and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Maternal obesity has been related to adverse neonatal outcomes and fetal programming. Oxidative stress and adipokines are potential biomarkers in such pregnancies; thus, the measurement of these molecules has been considered critical. Therefore, we developed artificial neural network (ANN) models based on maternal weight status and clinical data to predict reliable maternal blood concentrations of these biomarkers at the end of pregnancy. Adipokines (adiponectin, leptin, and resistin), and DNA, lipid and protein oxidative markers (8-oxo-2'-deoxyguanosine, malondialdehyde and carbonylated proteins, respectively) were assessed in blood of normal weight, overweight and obese women in the third trimester of pregnancy. A Back-propagation algorithm was used to train ANN models with four input variables (age, pre-gestational body mass index (p-BMI), weight status and gestational age). ANN models were able to accurately predict all biomarkers with regression coefficients greater than R² = 0.945. P-BMI was the most significant variable for estimating adiponectin and carbonylated proteins concentrations (37%), while gestational age was the most relevant variable to predict resistin and malondialdehyde (34%). Age, gestational age and p-BMI had the same significance for leptin values. Finally, for 8-oxo-2'-deoxyguanosine prediction, the most significant variable was age (37%). These models become relevant to improve clinical and nutrition interventions in prenatal care. [ABSTRACT FROM AUTHOR]