GC-MS-based metabolomic analysis of the urinary metabolome suggests to have the required sensitivity and specificity to gain insight into ASD phenotypes and aid a personalized network-based medicine approach.

We postulate that the increase in plasma and consequently urinary inositol may constitute a marker of altered glucose metabolism during fetal development in both IUGR and LGA newborns.

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Physiological measures are widely studied from a medical point of view. Most applications lie in the field of diagnosis of heart attacks, as regards the ECG, or the detection of epileptic events, in the case of the EEG. In the last ten years, these signals are being investigated also from a biometric point of view, in order to exploit the discriminative capability provided by these measures in recognizing individuals. The present work proposes a multimodal biometric recognition system based on the fusion of the first lead (i) of the electrocardiogram (ECG) with six different bands of the electroencephalogram (EEG). The proposed approach is based on the extraction of fiducial features (peaks) from the ECG combined with spectrum features of the EEG. A dataset has been created, by composing the signals of two well-known databases. The results, reported by means of EER values, AUC values and ROC curves, show good recognition performances