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BackgroundLeptin and insulin levels are key factors regulating fetal and neonatal energy homeostasis, development and growth. Both biomarkers are used as predictors of weight gain and obesity during infancy. There are currently no prediction algorithms for cord blood (UCB) hormone levels using Artificial Neural Networks (ANN) that have been directly trained with anthropometric maternal and neonatal data, from neonates exposed to distinct metabolic environments during pregnancy (obese with or without gestational diabetes mellitus or lean women). The aims were: 1) to develop ANN models that simulate leptin and insulin concentrations in UCB based on maternal and neonatal data (ANN perinatal model) or from only maternal data during early gestation (ANN prenatal model); 2) To evaluate the biological relevance of each parameter (maternal and neonatal anthropometric variables).MethodsWe collected maternal and neonatal anthropometric data (n = 49) in normoglycemic healthy lean, obese or obese with gestational diabetes mellitus women, as well as determined UCB leptin and insulin concentrations by ELISA. The ANN perinatal model consisted of an input layer of 12 variables (maternal and neonatal anthropometric and biochemical data from early gestation and at term) while the ANN prenatal model used only 6 variables (maternal anthropometric from early gestation) in the input layer. For both networks, the output layer contained 1 variable to UCB leptin or to UCB insulin concentration.ResultsThe best architectures for the ANN perinatal models estimating leptin and insulin were 12-5-1 while for the ANN prenatal models, 6-5-1 and 6-4-1 were found for leptin and insulin, respectively. ANN models presented an excellent agreement between experimental and simulated values. Interestingly, the use of only prenatal maternal anthropometric data was sufficient to estimate UCB leptin and insulin values. Maternal BMI, weight and age as well as neonatal birth were the most influential parameters for leptin while maternal morbidity was the most significant factor for insulin prediction.ConclusionsLow error percentage and short computing time makes these ANN models interesting in a translational research setting, to be applied for the prediction of neonatal leptin and insulin values from maternal anthropometric data, and possibly the on-line estimation during pregnancy.Electronic supplementary materialThe online version of this article (doi:10.1186/s12884-016-0967-z) contains supplementary material, which is available to authorized users.

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Background/Aims: Clara cell protein (cc-10) has been shown to negatively regulate inflammation, protect pulmonary surfactant from degradation in the lung, and administration of this recombinant protein improves the condition of infant respiratory distress syndrome (iRDS), a disease that occurred mainly in preterm infants. In view of the possibility that altered expression of cc-10 might regulate its protective function, we attempted to characterize this protein in infants with iRDS. Methods: Using bronchotraqueal aspirates from human infants, we analyzed cc-10 in two-dimensional gel electrophoresis (2-DE) by combining immunoprecipitation, carbonyl groups and total protein immunoblotting. Results: Seven forms of cc-10 were detected with western immunoblots in infants with iRDS while only four forms were present in neonates who needed mechanical ventilation for other reasons without any lung disease (control group). The overall levels of cc-10 in iRDS were lower and differences were seen in isoform pattern and distribution. Conclusion: Our demonstration that cc-10 is differentially expressed in infants with iRDS may point the way towards one possible mechanism that potentially involves modifications of the protein structure with its anti-inflammatory and surfactant protective function and could be detrimental for this airway disorder.