Marco Piangerelli

Pyrethroid Pesticide Metabolite in Urine and Microelements in Hair of Children Affected by Autism Spectrum Disorders: A Preliminary Investigation

The number of children affected by Autism Spectrum Disorders (ASD) is dramatically increasing as well as the studies aimed at understanding the risk factors associated with the development of ASD. Since the etiology of ASD is partly genetic and partly environmental, factors (i.e., heavy metals, pesticides) as well as lifestyle seem to have a key role in the development of the disease. ASD and Control (CTR) children, aged 5–12 years, were compared. Gas chromatography coupled with trap mass detector was used to measure the level of 3-PBA, the main pyrethroid metabolite in urine in a group of ASD patients, while optical emission spectrometry analysis was employed to estimate the level of metals and microelements in hair in a different group of ASD children. The presence of 3-PBA in urine seems to be independent of age in ASD children, while a positive correlation between 3-PBA and age was observed in the control group of the same age range. Urine concentration of 3-BPA in ASD children had higher values than in the control group, which were marginally significant (p = 0.054). Mg results were significantly decreased in ASD with respect to controls, while V, S, Zn, and Ca/Mg were marginally increased, without reaching statistical significance. Results of Principal Component (PC) analysis of metals and microelements in hair were not associated with either age or health status. In conclusion, 3-PBA in urine and Mg in hair were changed in ASD children relative to control ones.

A Fully Integrated Wireless System for Intracranial Direct Cortical Stimulation, Real-Time Electrocorticography Data Transmission, and Smart Cage for Wireless Battery Recharge

Wireless transmission of cortical signals is an essential step to improve the safety of epilepsy procedures requiring seizure focus localization and to provide chronic recording of brain activity for Brain Computer Interface (BCI) applications. Our group developed a fully implantable and externally rechargeable device, able to provide wireless electrocorticographic (ECoG) recording and cortical stimulation (CS). The first prototype of a wireless multi-channel very low power ECoG system was custom-designed to be implanted on non-human primates. The device, named ECOGIW-16E, is housed in a compact hermetically sealed Polyether ether ketone (PEEK) enclosure, allowing seamless battery recharge. ECOGIW-16E is recharged in a wireless fashion using a special cage designed to facilitate the recharge process in monkeys and developed in accordance with guidelines for accommodation of animals by Council of Europe (ETS123). The inductively recharging cage is made up of nylon and provides a thoroughly novel experimental setting on freely moving animals. The combination of wireless cable-free ECoG and external seamless battery recharge solves the problems and shortcomings caused by the presence of cables leaving the skull, providing a safer and easier way to monitor patients and to perform ECoG recording on primates. Data transmission exploits the newly available Medical Implant Communication Service band (MICS): 402–405 MHz. ECOGIW-16E was implanted over the left sensorimotor cortex of a macaca fascicularis to assess the feasibility of wireless ECoG monitoring and brain mapping through CS. With this device, we were able to record the everyday life ECoG signal from a monkey and to deliver focal brain stimulation with movement elicitation.

Metal and Microelement Biomarkers of Neurodegeneration in Early Life Permethrin-Treated Rats

Hair is a non-invasive biological material useful in the biomonitoring of trace elements because it is a vehicle for substance excretion from the body, and it permits evaluating long-term metal exposure. Here, hair from an animal model of neurodegeneration, induced by early life permethrin treatment from the sixth to 21th day of life, has been analyzed with the aim to assess if metal and microelement content could be used as biomarkers. A hair trace element assay was performed by the ICP-MS technique in six- and 12-month-old rats. A significant increase of As, Mg, S and Zn was measured in the permethrin-treated group at 12 months compared to six months, while Si and Cu/Zn were decreased. K, Cu/Zn and S were increased in the treated group compared to age-matched controls at six and 12 months, respectively. Cr significantly decreased in the treated group at 12 months. PCA analysis showed both a best difference between treated and age-matched control groups at six months. The present findings support the evidence that the Cu/Zn ratio and K, measured at six months, are the best biomarkers for neurodegeneration. This study supports the use of hair analysis to identify biomarkers of neurodegeneration induced by early life permethrin pesticide exposure.

Obesity‐related genetic polymorphisms and adiposity indices in a young Italian population

Pediatric obesity develops when a complex biological predisposition collides with an obesogenic environment. To further elucidate the role of genetics in obesity onset, we performed a candidate‐gene association study in a young and sportive Italian population by testing the association of functional polymorphisms in ACE (rs4646994), FTO (rs9939609), MC4R (rs17782313) and PPARG (rs1801282) genes with body mass index (BMI) and waist‐to‐height ratio (WHtR). We also tested the combinations of identified risk genotypes and epistatic interactions among them to determine the existence of cumulative effects in predicting the predisposition to gain weight. Our results confirm a significant direct influence of MC4R rs17782313 and PPARG rs1801282 on body composition, that is, minor allele homozygotes showed significantly higher BMI (rs17782313, β = 1.258, P = 0.031; rs1801282, β = 6.689, P = 1.2 × 10−4) and WHtR (rs17782313, β = 0.021, P = 0.005; rs1801282, β = 0.069, P = 0.003) values. Moreover, by leveraging multifactor dimensionality reduction and general linear model (GLM) approaches we identified an epistatic interaction between ACE and MC4R, where heterozygosity at ACE rs4646994 seems to protect from the unfavorable predisposition to gain weight given by C/C genotype at MC4R rs17782313 (GLM, P = 0.004). In conclusion, to clarify the role of genetics in multifactorial diseases remains a difficult goal, even for the most investigated polymorphisms and in controlled populations. Further studies on epistasis and gene–gene interaction will help to elucidate this complex scenario.

RNN-based Model for Self-adaptive Systems

The human brain is the self-adaptive system par excellence. We claim that a hierarchical model for self-adaptive system can be built on two levels, the upper structural level S and the lower behavioral level B. The higher order structure naturally emerges from interactions of the system with its environment and it acts as coordinator of local interactions among simple reactive elements. The lower level regards the topology of the network whose elements self-organize to perform the behavior of the system. The adaptivity feature follows the self-organizing principle that supports the entanglement of lower level elements and the higher order structure. The challenging idea in this position paper is to represent the two-level model as a second order Long Short-Term Memory Recurrent Neural Network, a bio-inspired class of artificial neural networks, very powerful for dealing with the dynamics of complex systems and for studying the emergence of brain activities. It is our aim to experiment the model over real Electrocorticographical data (EcoG) for detecting the emergence of long-term neurological disorders such as epileptic seizures.

Topological classifier for detecting the emergence of epileptic seizures

ObjectiveAn innovative method based on topological data analysis is introduced for classifying EEG recordings of patients affected by epilepsy. We construct a topological space from a collection of EEGs signals using Persistent Homology; then, we analyse the space by Persistent entropy, a global topological feature, in order to classify healthy and epileptic signals.ResultsThe performance of the resulting one-feature-based linear topological classifier is tested by analysing the Physionet dataset. The quality of classification is evaluated in terms of the Area Under Curve (AUC) of the receiver operating characteristic curve. It is shown that the linear topological classifier has an AUC equal to

CyberBrain - A Preliminary Experience on Non Human Primate

97.2\%

A novel neural prosthesis providing long-term electrocorticography recording and cortical stimulation for epilepsy and brain-computer interface

97.2% while the performance of a classifier based on Sample Entropy has an AUC equal to 62.0%.