Role of nitric oxide and endocannabinoids in synaptic plasticity in the perirhinal cortex and in visual recognition memory

Introduction and aims of the research Nitric oxide (NO) and endocannabinoids (eCBs) are major retrograde messengers, involved in synaptic plasticity (long-term potentiation, LTP, and long-term depression, LTD) in many brain areas (including hippocampus and neocortex), as well as in learning and memory processes. NO is synthesized by NO synthase (NOS) in response to increased cytosolic Ca2+ and mainly exerts its functions through soluble guanylate cyclase (sGC) and cGMP production. The main target of cGMP is the cGMP-dependent protein kinase (PKG). Activity-dependent release of eCBs in the CNS leads to the activation of the Gαi/o-coupled cannabinoid receptor 1 (CB1) at both glutamatergic and inhibitory synapses. The perirhinal cortex (Prh) is a multimodal associative cortex of the temporal lobe, critically involved in visual recognition memory. LTD is proposed to be the cellular correlate underlying this form of memory. Cholinergic neurotransmission has been shown to play a critical role in both visual recognition memory and LTD in Prh. Moreover, visual recognition memory is one of the main cognitive functions impaired in the early stages of Alzheimer’s disease. The main aim of my research was to investigate the role of NO and ECBs in synaptic plasticity in rat Prh and in visual recognition memory. Part of this research was dedicated to the study of synaptic transmission and plasticity in a murine model (Tg2576) of Alzheimer’s disease. Methods Field potential recordings. Extracellular field potential recordings were carried out in horizontal Prh slices from Sprague-Dawley or Dark Agouti juvenile (p21-35) rats. LTD was induced with a single train of 3000 pulses delivered at 5 Hz (10 min), or via bath application of carbachol (Cch; 50 μM) for 10 min. LTP was induced by theta-burst stimulation (TBS). In addition, input/output curves and 5Hz-LTD were carried out in Prh slices from 3 month-old Tg2576 mice and littermate controls. Behavioural experiments. The spontaneous novel object exploration task was performed in intra-Prh bilaterally cannulated adult Dark Agouti rats. Drugs or vehicle (saline) were directly infused into the Prh 15 min before training to verify the role of nNOS and CB1 in visual recognition memory acquisition. Object recognition memory was tested at 20 min and 24h after the end of the training phase. Results Electrophysiological experiments in Prh slices from juvenile rats showed that 5Hz-LTD is due to the activation of the NOS/sGC/PKG pathway, whereas Cch-LTD relies on NOS/sGC but not PKG activation. By contrast, NO does not appear to be involved in LTP in this preparation. Furthermore, I found that eCBs are involved in LTP induction, but not in basal synaptic transmission, 5Hz-LTD and Cch-LTD. Behavioural experiments demonstrated that the blockade of nNOS impairs rat visual recognition memory tested at 24 hours, but not at 20 min; however, the blockade of CB1 did not affect visual recognition memory acquisition tested at both time points specified. In three month-old Tg2576 mice, deficits in basal synaptic transmission and 5Hz-LTD were observed compared to littermate controls. Conclusions The results obtained in Prh slices from juvenile rats indicate that NO and CB1 play a role in the induction of LTD and LTP, respectively. These results are confirmed by the observation that nNOS, but not CB1, is involved in visual recognition memory acquisition. The preliminary results obtained in the murine model of Alzheimer’s disease indicate that deficits in synaptic transmission and plasticity occur very early in Prh; further investigations are required to characterize the molecular mechanisms underlying these deficits.

Deep learning and embeddings for problems of computational biology

The development of Next Generation Sequencing promotes Biology in the Big Data era. The ever-increasing gap between proteins with known sequences and those with a complete functional annotation requires computational methods for automatic structure and functional annotation. My research has been focusing on proteins and led so far to the development of three novel tools, DeepREx, E-SNPs&GO and ISPRED-SEQ, based on Machine and Deep Learning approaches. DeepREx computes the solvent exposure of residues in a protein chain. This problem is relevant for the definition of structural constraints regarding the possible folding of the protein. DeepREx exploits Long Short-Term Memory layers to capture residue-level interactions between positions distant in the sequence, achieving state-of-the-art performances. With DeepRex, I conducted a large-scale analysis investigating the relationship between solvent exposure of a residue and its probability to be pathogenic upon mutation. E-SNPs&GO predicts the pathogenicity of a Single Residue Variation. Variations occurring on a protein sequence can have different effects, possibly leading to the onset of diseases. E-SNPs&GO exploits protein embeddings generated by two novel Protein Language Models (PLMs), as well as a new way of representing functional information coming from the Gene Ontology. The method achieves state-of-the-art performances and is extremely time-efficient when compared to traditional approaches. ISPRED-SEQ predicts the presence of Protein-Protein Interaction sites in a protein sequence. Knowing how a protein interacts with other molecules is crucial for accurate functional characterization. ISPRED-SEQ exploits a convolutional layer to parse local context after embedding the protein sequence with two novel PLMs, greatly surpassing the current state-of-the-art. All methods are published in international journals and are available as user-friendly web servers. They have been developed keeping in mind standard guidelines for FAIRness (FAIR: Findable, Accessible, Interoperable, Reusable) and are integrated into the public collection of tools provided by ELIXIR, the European infrastructure for Bioinformatics.

Interaction between APOE4 genotype and environmental risk factors in Alzheimer's disease

Alzheimer's disease (AD) is probably caused by both genetic and environmental risk factors. The major genetic risk factor is the E4 variant of apolipoprotein E gene called apoE4. Several risk factors for developing AD have been identified including lifestyle, such as dietary habits. The mechanisms behind the AD pathogenesis and the onset of cognitive decline in the AD brain are presently unknown. In this study we wanted to characterize the effects of the interaction between environmental risk factors and apoE genotype on neurodegeneration processes, with particular focus on behavioural studies and neurodegenerative processes at molecular level. Towards this aim, we used 6 months-old apoE4 and apoE3 Target Replacement (TR) mice fed on different diets (high intake of cholesterol and high intake of carbohydrates). These mice were evaluated for learning and memory deficits in spatial reference (Morris Water Maze (MWM)) and contextual learning (Passive Avoidance) tasks, which involve the hippocampus and the amygdala, respectively. From these behavioural studies we found that the initial cognitive impairments manifested as a retention deficit in apoE4 mice fed on high carbohydrate diet. Thus, the genetic risk factor apoE4 genotype associated with a high carbohydrate diet seems to affect cognitive functions in young mice, corroborating the theory that the combination of genetic and environmental risk factors greatly increases the risk of developing AD and leads to an earlier onset of cognitive deficits. The cellular and molecular bases of the cognitive decline in AD are largely unknown. In order to determine the molecular changes for the onset of the early cognitive impairment observed in the behavioural studies, we performed molecular studies, with particular focus on synaptic integrity and Tau phosphorylation. The most relevant finding of our molecular studies showed a significant decrease of Brain-derived Neurotrophic Factor (BDNF) in apoE4 mice fed on high carbohydrate diet. Our results may suggest that BDNF decrease found in apoE4 HS mice could be involved in the earliest impairment in long-term reference memory observed in behavioural studies. The second aim of this thesis was to study possible involvement of leptin in AD. There is growing evidence that leptin has neuroprotective properties in the Central Nervous System (CNS). Recent evidence has shown that leptin and its receptors are widespread in the CNS and may provide neuronal survival signals. However, there are still numerous questions, regarding the molecular mechanism by which leptin acts, that remain unanswered. Thus, given to the importance of the involvement of leptin in AD, we wanted to clarify the function of leptin in the pathogenesis of AD and to investigate if apoE genotype affect leptin levels through studies in vitro, in mice and in human. Our findings suggest that apoE4 TR mice showed an increase of leptin in the brain. Leptin levels are also increased in the cerebral spinal fluid of AD patients and apoE4 carriers with AD have higher levels of leptin than apoE3 carriers. Moreover, leptin seems to be expressed by reactive glial cells in AD brains. In vitro, ApoE4 together with Amyloid beta increases leptin production by microglia and astrocytes. Taken together, all these findings suggest that leptin replacement might not be a good strategy for AD therapy. Our results show that high leptin levels were found in AD brains. These findings suggest that, as high leptin levels do not promote satiety in obese individuals, it might be possible that they do not promote neuroprotection in AD patients. Therefore, we hypothesized that AD brain could suffer from leptin resistance. Further studies will be critical to determine whether or not the central leptin resistance in SNC could affect its potential neuroprotective effects.

Social learning and action understanding in human observers: contributions of sensori-motor constraints and prior information

The aim of this thesis was to investigate the respective contribution of prior information and sensorimotor constraints to action understanding, and to estimate their consequences on the evolution of human social learning. Even though a huge amount of literature is dedicated to the study of action understanding and its role in social learning, these issues are still largely debated. Here, I critically describe two main perspectives. The first perspective interprets faithful social learning as an outcome of a fine-grained representation of others’ actions and intentions that requires sophisticated socio-cognitive skills. In contrast, the second perspective highlights the role of simpler decision heuristics, the recruitment of which is determined by individual and ecological constraints. The present thesis aims to show, through four experimental works, that these two contributions are not mutually exclusive. A first study investigates the role of the inferior frontal cortex (IFC), the anterior intraparietal area (AIP) and the primary somatosensory cortex (S1) in the recognition of other people’s actions, using a transcranial magnetic stimulation adaptation paradigm (TMSA). The second work studies whether, and how, higher-order and lower-order prior information (acquired from the probabilistic sampling of past events vs. derived from an estimation of biomechanical constraints of observed actions) interacts during the prediction of other people’s intentions. Using a single-pulse TMS procedure, the third study investigates whether the interaction between these two classes of priors modulates the motor system activity. The fourth study tests the extent to which behavioral and ecological constraints influence the emergence of faithful social learning strategies at a population level. The collected data contribute to elucidate how higher-order and lower-order prior expectations interact during action prediction, and clarify the neural mechanisms underlying such interaction. Finally, these works provide/open promising perspectives for a better understanding of social learning, with possible extensions to animal models.

Biomolecular studies in Alzheimer’s Disease models: investigations in vitro and in vivo

The Alzheimer’s disease (AD), the most prevalent form of age-related dementia, is a multifactorial and heterogeneous neurodegenerative disease. The molecular mechanisms underlying the pathogenesis of AD are yet largely unknown. However, the etiopathogenesis of AD likely resides in the interaction between genetic and environmental risk factors. Among the different factors that contribute to the pathogenesis of AD, amyloid-beta peptides and the genetic risk factor apoE4 are prominent on the basis of genetic evidence and experimental data. ApoE4 transgenic mice have deficits in spatial learning and memory associated with inflammation and brain atrophy. Evidences suggest that apoE4 is implicated in amyloid-beta accumulation, imbalance of cellular antioxidant system and in apoptotic phenomena. The mechanisms by which apoE4 interacts with other AD risk factors leading to an increased susceptibility to the dementia are still unknown. The aim of this research was to provide new insights into molecular mechanisms of AD neurodegeneration, investigating the effect of amyloid-beta peptides and apoE4 genotype on the modulation of genes and proteins differently involved in cellular processes related to aging and oxidative balance such as PIN1, SIRT1, PSEN1, BDNF, TRX1 and GRX1. In particular, we used human neuroblastoma cells exposed to amyloid-beta or apoE3 and apoE4 proteins at different time-points, and selected brain regions of human apoE3 and apoE4 targeted replacement mice, as in vitro and in vivo models, respectively. All genes and proteins studied in the present investigation are modulated by amyloid-beta and apoE4 in different ways, suggesting their involvement in the neurodegenerative mechanisms underlying the AD. Finally, these proteins might represent novel potential diagnostic and therapeutic targets in AD.

Modeling Alzheimer disease with iPSCs and mouse model for the identification of risk factors, new targets, and potential therapeutical strategies

Alzheimer's disease (AD) is the most common neurodegenerative disease in elderly. Donepezil is the first-line drug used for AD. In section one, the experimental activity was oriented to evaluate and characterize molecular and cellular mechanisms that contribute to neurodegeneration induced by the Aβ1-42 oligomers (Aβ1-42O) and potential neuroprotective effects of the hybrids feruloyl-donepezil compound called PQM130. The effects of PQM130 were compared to donepezil in a murine AD model, obtained by intracerebroventricular (i.c.v.) injection of Aβ1-42O. The intraperitoneal administration of PQM130 (0.5-1 mg/kg) after i.c.v. Aβ1-42O injection improved learning and memory, protecting mice against spatial cognition decline. Moreover, it reduced oxidative stress, neuroinflammation and neuronal apoptosis, induced cell survival and protein synthesis in mice hippocampus. PQM130 modulated different pathways than donepezil, and it is more effective in counteracting Aβ1-42O damage. The section two of the experimental activity was focused on studying a loss of function variants of ABCA7. GWA studies identified mutations in the ABCA7 gene as a risk factor for AD. The mechanism through which ABCA7 contributes to AD is not clear. ABCA7 regulates lipid metabolism and critically controls phagocytic function. To investigate ABCA7 functions, CRISPR/Cas9 technology was used to engineer human iPSCs and to carry the genetic variant Y622*, which results in a premature stop codon, causing ABCA7 loss-of-function. From iPSCs, astrocytes were generated. This study revealed the effects of ABCA7 loss in astrocytes. ABCA7 Y622* mutation induced dysfunctional endocytic trafficking, impairing Aβ clearance, lipid dysregulation and cell homeostasis disruption, alterations that could contribute to AD. Though further studies are needed to confirm the PQM130 neuroprotective role and ABCA7 function in AD, the provided results showed a better understanding of AD pathophysiology, a new therapeutic approach to treat AD, and illustrated an innovative methodology for studying the disease.

Methods for integrating machine learning and constrained optimization

In the framework of industrial problems, the application of Constrained Optimization is known to have overall very good modeling capability and performance and stands as one of the most powerful, explored, and exploited tool to address prescriptive tasks. The number of applications is huge, ranging from logistics to transportation, packing, production, telecommunication, scheduling, and much more. The main reason behind this success is to be found in the remarkable effort put in the last decades by the OR community to develop realistic models and devise exact or approximate methods to solve the largest variety of constrained or combinatorial optimization problems, together with the spread of computational power and easily accessible OR software and resources. On the other hand, the technological advancements lead to a data wealth never seen before and increasingly push towards methods able to extract useful knowledge from them; among the data-driven methods, Machine Learning techniques appear to be one of the most promising, thanks to its successes in domains like Image Recognition, Natural Language Processes and playing games, but also the amount of research involved. The purpose of the present research is to study how Machine Learning and Constrained Optimization can be used together to achieve systems able to leverage the strengths of both methods: this would open the way to exploiting decades of research on resolution techniques for COPs and constructing models able to adapt and learn from available data. In the first part of this work, we survey the existing techniques and classify them according to the type, method, or scope of the integration; subsequently, we introduce a novel and general algorithm devised to inject knowledge into learning models through constraints, Moving Target. In the last part of the thesis, two applications stemming from real-world projects and done in collaboration with Optit will be presented.

Temporary teams learning and performance: the case of 3D printing technology in orthopaedic surgery

This dissertation contributes to the scholarly debate on temporary teams by exploring team interactions and boundaries.The fundamental challenge in temporary teams originates from temporary participation in the teams. First, as participants join the team for a short period of time, there is not enough time to build trust, share understanding, and have effective interactions. Consequently, team outputs and practices built on team interactions become vulnerable. Secondly, as team participants move on and off the teams, teams’ boundaries become blurred over time. It leads to uncertainty among team participants and leaders about who is/is not identified as a team member causing collective disagreement within the team. Focusing on the above mentioned challenges, we conducted this research in healthcare organisations since the use of temporary teams in healthcare and hospital setting is prevalent. In particular, we focused on orthopaedic teams that provide personalised treatments for patients using 3D printing technology. Qualitative and quantitative data were collected using interviews, observations, questionnaires and archival data at Rizzoli Orthopaedic Institute, Bologna, Italy. This study provides the following research outputs. The first is a conceptual study that explores temporary teams’ literature using bibliometric analysis and systematic literature review to highlight research gaps. The second paper qualitatively studies temporary relationships within the teams by collecting data using group interviews and observations. The results highlighted the role of short-term dyadic relationships as a ground to share and transfer knowledge at the team level. Moreover, hierarchical structure of the teams facilitates knowledge sharing by supporting dyadic relationships within and beyond the team meetings. The third paper investigates impact of blurred boundaries on temporary teams’ performance. Using quantitative data collected through questionnaires and archival data, we concluded that boundary blurring in terms of fluidity, overlap and dispersion differently impacts team performance at high and low levels of task complexity.

Applied deep learning and data science with a human-centric and data-centric approach

The term Artificial intelligence acquired a lot of baggage since its introduction and in its current incarnation is synonymous with Deep Learning. The sudden availability of data and computing resources has opened the gates to myriads of applications. Not all are created equal though, and problems might arise especially for fields not closely related to the tasks that pertain tech companies that spearheaded DL. The perspective of practitioners seems to be changing, however. Human-Centric AI emerged in the last few years as a new way of thinking DL and AI applications from the ground up, with a special attention at their relationship with humans. The goal is designing a system that can gracefully integrate in already established workflows, as in many real-world scenarios AI may not be good enough to completely replace its humans. Often this replacement may even be unneeded or undesirable. Another important perspective comes from, Andrew Ng, a DL pioneer, who recently started shifting the focus of development from “better models” towards better, and smaller, data. He defined his approach Data-Centric AI. Without downplaying the importance of pushing the state of the art in DL, we must recognize that if the goal is creating a tool for humans to use, more raw performance may not align with more utility for the final user. A Human-Centric approach is compatible with a Data-Centric one, and we find that the two overlap nicely when human expertise is used as the driving force behind data quality. This thesis documents a series of case-studies where these approaches were employed, to different extents, to guide the design and implementation of intelligent systems. We found human expertise proved crucial in improving datasets and models. The last chapter includes a slight deviation, with studies on the pandemic, still preserving the human and data centric perspective.

Computational creativity: an interdisciplinary approach to sequential learning and creative generations

Creativity seems mysterious; when we experience a creative spark, it is difficult to explain how we got that idea, and we often recall notions like ``inspiration" and ``intuition" when we try to explain the phenomenon. The fact that we are clueless about how a creative idea manifests itself does not necessarily imply that a scientific explanation cannot exist. We are unaware of how we perform certain tasks, such as biking or language understanding, but we have more and more computational techniques that can replicate and hopefully explain such activities. We should understand that every creative act is a fruit of experience, society, and culture. Nothing comes from nothing. Novel ideas are never utterly new; they stem from representations that are already in mind. Creativity involves establishing new relations between pieces of information we had already: then, the greater the knowledge, the greater the possibility of finding uncommon connections, and the more the potential to be creative. In this vein, a beneficial approach to a better understanding of creativity must include computational or mechanistic accounts of such inner procedures and the formation of the knowledge that enables such connections. That is the aim of Computational Creativity: to develop computational systems for emulating and studying creativity. Hence, this dissertation focuses on these two related research areas: discussing computational mechanisms to generate creative artifacts and describing some implicit cognitive processes that can form the basis for creative thoughts.

Sustainable futuring. Learning and unlearning the past A critical discourse study of ‘fashion sustainability’-related discursive formations in Vogue Italia (1965-2021)

Recent scholarly works on the relationship between ‘fashion’ and ‘sustainability’ have identified a need for a systemic transition towards fashion media ‘for sustaianbility’. Nevertheless, the academic research on the topic is still limited and rather circumscribed to the analysis of marketing practices, while only recently some more systemic and critical analyses of the symbolic production of sustainability through fashion media have been undertaken. Responding to this need for an in-depth investigation of ‘sustainability’-related media production, my research focuses on the ‘fashion sustainability’-related discursive formations in the context of one of the most influential fashion magazines today – Vogue Italia. In order to investigate the ways in which the ‘sustainability’ discourse was formed and has evolved, the study considered the entire Vogue Italia archive from 1965 to 2021. The data collection was carried out in two phases, and the individualised relevant discursive units were then in-depth and critically analysed to allow for a grounded assessment of the media giant’s position. The Discourse-Historical Approach provided a methodological base for the analysis, which took into consideration the various levels of context: the immediate textual and intertextual, but also the broader socio-cultural context of the predominant, over-production oriented and capital-led fashion system. The findings led to a delineation of the evolution of the ‘fashion sustainability’ discourse, unveiling how despite Vogue Italia’s auto-determination as attentive to ‘sustainability’-related topics, the magazine is systemically employing discursive strategies which significantly mitigate the meaning of the ‘sustainable commitment’ and thus the meaning of ‘fashion sustainability’.

Cardiac functional and structural abnormalities in a mouse model of CDKL5 deficiency disorder

CDKL5 (cyclin-dependent kinase-like 5) deficiency disorder (CDD) is a rare and severe neurodevelopmental disease that mostly affects girls who are heterozygous for mutations in the X-linked CDKL5 gene. The lack of CDKL5 protein expression or function leads to the appearance of numerous clinical features, including early-onset seizures, marked hypotonia, autistic features, and severe neurodevelopmental impairment. Mouse models of CDD, Cdkl5 KO mice, exhibit several behavioral phenotypes that mimic CDD features, such as impaired learning and memory, social interaction, and motor coordination. CDD symptomatology, along with the high CDKL5 expression levels in the brain, underscores the critical role that CDKL5 plays in proper brain development and function. Nevertheless, the improvement of the clinical overview of CDD in the past few years has defined a more detailed phenotypic spectrum; this includes very common alterations in peripheral organ and tissue function, such as gastrointestinal problems, irregular breathing, hypotonia, and scoliosis, suggesting that CDKL5 deficiency compromises not only CNS function but also that of other organs/tissues. Here we report, for the first time, that a mouse model of CDD, the heterozygous Cdkl5 KO (Cdkl5 +/-) female mouse, exhibits cardiac functional and structural abnormalities. The mice also showed QTc prolongation and increased heart rate. These changes correlate with a marked decrease in parasympathetic activity to the heart and in the expression of the Scn5a and Hcn4 voltage-gated channels. Moreover, the Cdkl5 +/- heart shows typical signs of heart aging, including increased fibrosis, mitochondrial dysfunctions, and increased ROS production. Overall, our study not only contributes to the understanding of the role of CDKL5 in heart structure/function but also documents a novel preclinical phenotype for future therapeutic investigation.

Deep learning and spatial transcriptomics to investigate thyroid tumors

There are many diseases that affect the thyroid gland, and among them are carcinoma. Thyroid cancer is the most common endocrine neoplasm and the second most frequent cancer in the 0-49 age group. This thesis deals with two studies I conducted during my PhD. The first concerns the development of a Deep Learning model to be able to assist the pathologist in screening of thyroid cytology smears. This tool created in collaboration with Prof. Diciotti, affiliated with the DEI-UNIBO "Guglielmo Marconi" Department of Electrical Energy and Information Engineering, has an important clinical implication in that it allows patients to be stratified between those who should undergo surgery and those who should not. The second concerns the application of spatial transcriptomics on well-differentiated thyroid carcinomas to better understand their invasion mechanisms and thus to better comprehend which genes may be involved in the proliferation of these tumors. This project specifically was made possible through a fruitful collaboration with the Gustave Roussy Institute in Paris. Studying thyroid carcinoma deeply is essential to improve patient care, increase survival rates, and enhance the overall understanding of this prevalent cancer. It can lead to more effective prevention, early detection, and treatment strategies that benefit both patients and the healthcare system.

Sindrome di Down e fattori di rischio nel declino neurocognitivo

Down syndrome (DS) or Trisomy 21, occurring in 1/700 and 1/1000 livebirths, is the most common genetic disorder, characterized by a third copy of the human chromosome 21 (Hsa21). DS is associated with various defects, including congenital heart diseases, craniofacial abnormalities, immune system dysfunction, mental retardation (MR), learning and memory deficiency. The phenotypic features in DS are a direct consequence of overexpression of genes located within the triplicated region on Hsa21. In addition to developmental brain abnormalities and disabilities, people with DS by the age of 30-40 have a greatly increased risk of early-onset of Alzheimer’s disease (AD) and an apparent tendency toward premature aging. Many of the immunological anomalies in DS can be enclosed in the spectrum of multiple signs of early senescence. People with DS have an increased vulnerability to oxidative damage and many factors, including amyloid beta protein (Abeta), genotype ApoE4, oxidative stress, mutations in mitochondrial DNA (mtDNA), impairment of antioxidant enzymes, accelerated neuronal cell apoptosis, are related to neuronal degeneration and early aging in DS. SUBJECTS and METHODS: Since 2007 a population of 50 adolescents and adults with DS, 26 males and 24 females (sex-ratio: M/F = 1.08), has been evaluated for the presence of neurological features, biomarkers and genetic factors correlated with neuronal degeneration and premature aging. The control group was determined by the mother and the siblings of the patients. A neuropsychiatric evaluation was obtained from all patients. The levels of thyroid antibodies (antiTg and antiTPO) and of some biochemical markers of oxidative stress, including homocysteine (tHcy), uric acid, cobalamin, folate were measured. All patients, the mother and the siblings were genotyped for ApoE gene. RESULTS: 40% of patients, with a mild prevalence of females aged between 19 and 30 years, showed increased levels of antiTg and antiTPO. The levels of tHcy were normal in 52% patients and mildly increased in 40%; hyperomocysteinemia was associated with normal levels of thyroid antibodies in 75% of patients (p<0.005). The levels of uric acid were elevated in 26%. Our study showed a prevalence of severe MR in patients aged between 1-18 years and over 30 years. Only 3 patients, 2 females and one male, over 30 years of age, showed dementia. According to the literature, the rate of Down left-handers was high (25%) compared to the rest of population and the laterality was associated with increased levels of thyroid antibodies (70%). 21.5% of patients were ApoE4 positive (ApoE4+) with a mean/severe MR. CONCLUSIONS: Until now no biochemical evidence of oxidative damage and no deficiency or alteration of antioxidant function in our patients with DS were found. mtDNA sequencing could show some mutations age-related and associated with oxidative damage and neurocognitive decline in the early aging of DS. The final aim is found predictive markers of early-onset dementia and a target strategy for the prevention and the treatment of diseases caused by oxidative stress. REFERENCES: 1) Rachidi M, Lopes C: “Mental retardation and associated neurological dysfunctions in Down syndrome: a consequence of dysregulation in critical chromosome 21 genes and associated molecular pathways.” - Eur J Paediatr Neurol. May;12(3):168-82 (2008). 2) Lott IT, Head E: “Down syndrome and Alzheimer's disease: a link between development and aging.” - Ment Retard Dev Disabil Res Rev, 7(3):172-8 (2001). 3) Lee HC, Wei YH: “Oxidative Stress, Mitochondrial DNA Mutation, and Apoptosis in Aging.” - Exp Biol Med (Maywood), May;232(5):592-606 (2007).