Abstract
ObjectivesIntra-uterine growth retardation is often of unknown origin, and is of great interest as a “Fetal Origin of Adult Disease” has been now well recognized. We built a benchmark based upon a previously analysed data set related to Intrauterine Growth Retardation with 46 subjects described by 14 variables, related with the insulin-like growth factor system and pro-inflammatory cytokines, namely interleukin -6 and tumor necrosis factor -α.Design and MethodsWe used new algorithms for optimal information sorting based on the combination of two neural network algorithms: Auto-contractive Map and Activation and Competition System. Auto-Contractive Map spatializes the relationships among variables or records by constructing a suitable embedding space where ‘closeness’ among variables or records reflects accurately their associations. The Activation and Competition System algorithm instead works as a dynamic non linear associative memory on the weight matrices of other algorithms, and is able to produce a prototypical variable profile of a given target.ResultsClassical statistical analysis, proved to be unable to distinguish intrauterine growth retardation from appropriate-for-gestational age (AGA) subjects due to the high non-linearity of underlying functions. Auto-contractive map succeeded in clustering and differentiating completely the conditions under study, while Activation and Competition System allowed to develop the profile of variables which discriminated the two conditions under study better than any other previous form of attempt. In particular, Activation and Competition System showed that ppropriateness for gestational age was explained by IGF-2 relative gene expression, and by IGFBP-2 and TNF-α placental contents. IUGR instead was explained by IGF-I, IGFBP-1, IGFBP-2 and IL-6 gene expression in placenta.ConclusionThis further analysis provided further insight into the placental key-players of fetal growth within the insulin-like growth factor and cytokine systems. Our previous published analysis could identify only which variables were predictive of fetal growth in general, and identified only some relationships.
Highlights
Most of the data concerning determinants of fetal growth restriction or intrauterine growth retardation (IUGR) come from traditional statistical analysis, which is unable to grasp complex interactions among variables when the underlying functions are non linear
Activation and Competition System showed that ppropriateness for gestational age was explained by insulin-like growth factor (IGF)-2 relative gene
IUGR instead was explained by IGF-I, IGF binding proteins (IGFBP)-1, IGFBP-2 and IL-6 gene expression in placenta
Summary
Most of the data concerning determinants of fetal growth restriction or intrauterine growth retardation (IUGR) come from traditional statistical analysis, which is unable to grasp complex interactions among variables when the underlying functions are non linear. The interest in IUGR has grown because approximately 13% of these subjects do not present a catch-up growth [2], and in recent years, the concept of a “Fetal Origin of Adult Disease” has been introduced to describe modifications in utero that can influence adult life [3]. The IGF system is recognized to be crucial for fetal growth, as experiments in knockout mice have shown [5,6,7,8]. It is well known that IGF-I and IGF-2 are both synthesized in the placenta [9,10,11]. IGFBP-1, IGFBP-2, IGFBP-4 and IGFBP-6 are expressed by all placenta cell types while IGFBP-3 and IGFBP-5 are expressed only by some [12]
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