White matter microstructure in early onset Obsessive-Compulsive Disorder and Tourette Syndrome. A diffusion tensor imaging study in a population of drug-naïve children and adolescents with long-term clinical follow-up

Background and Objective Early onset obsessive-compulsive disorder (OCD) and Tourette syndrome (TS) are frequently associated conditions. Beside the evidence of their high epidemiological cross-prevalence supported by a common genetic liability (Huisman-van Dijk et al., 2016; Yu et al., 2015), little is known on the nature of their close relationship on a pathophysiological level. By analyzing white matter (WM) microstructure through diffusion tensor imaging (DTI), the present study aimed to characterize and compare primary pathophysiological changes in drug-naïve children and adolescents with OCD, TS, and TS+OCD. Methods Fifty-one participants (mean age 10.2 2.0 years), including N=10 with OCD, N=16 with pure TS, N=14 with TS+OCD, and 11 age-matched controls were studied cross-sectionally through 3T MRI. We performed tractography and extracted DTI metrics in five WM tracts of interest, i.e., the cortico-spinal tract (CST), the anterior thalamic radiations (ATR), the inferior longitudinal fasciculus (ILF), the corpus callosum (CC), and the cingulum. Relationship between DTI changes and clinical severity was examined through correlational analyses. A clinical follow-up at mean 7.6 years after MRI examination was performed to evaluate clinical outcomes and association to neuroimaging findings. Results Significant between-group differences emerged in DTI metrics, specifically in fractional anisotropy (FA), an index of myelination and organization of axon fibers (Johansen-Berg & Rushworth, 2009; Toga et al., 2006). All analyzed tracts of interest except for the cingulum revealed a differential microstructure at group comparisons. The OCD group showed decreased FA within CST, ATR, ILF, and CC in respect to controls. A negative correlation was found between obsessive-compulsive symptoms and FA values in OCD, indicating that more severe clinical phenotypes are likely underpinned by less organized WM. Compared to controls, TS and TS+OCD groups both displayed remarkably different correlates from OCD and opposite DTI changes, i.e., increased FA in CST, ATR, ILF, and CC. Moreover, TS and TS+OCD had comparable DTI changes within all the investigated WM tracts and FA showed negative correlation with tic severity, revealing a shared pattern of WM organization in TS/TS+OCD with inverse relationship to symptom expression. At follow-up, no significant associations were found between FA values at baseline and long-term outcomes. Substantial symptom remission was achieved in 58.3% of TS, 63.6% of TS+OCD, and 70% of OCD patients, although a significant proportion of patient developed additional psychiatric disorders such as anxiety or depression. Conclusion The study highlights differential white matter involvement in pediatric OCD as opposed to TS/TS+OCD. Compared to neurotypical population, children with TS/TS+OCD showed an early increase in axons, fiber density, and/or myelination in WM bundles linking the frontal, occipital, and temporal cortices with each other and with the thalamus. Conversely, children with OCD showed widespread reduced organization of callosal, temporo-occipital, and fronto-thalamic WM tracts. Correlational analysis suggests that DTI changes in TS may reflect a compensatory reorganization in response to the disease pathophysiology, while in OCD they may represent a marker of the overall disease severity deriving from delay or damage to white matter development. Confirmation of these possibilities awaits longitudinal studies. The observation of shared DTI correlates of TS and TS+OCD strengthens the concept that at least some forms of OCD are etiologically related to TS and might therefore be a variant expression of the same etiologic factors that are important for the expression of tics (i.e., TS+OCD as a peculiar subtype of TS). By characterizing and differentiating early-stage neural underpinnings of OCD and TS, future targeted and neuroimaging-informed interventions may be developed

Network-based approaches to explore complex biological systems towards network medicine

Network medicine relies on different types of networks: from the molecular level of protein–protein interactions to gene regulatory network and correlation studies of gene expression. Among network approaches based on the analysis of the topological properties of protein–protein interaction (PPI) networks, we discuss the widespread DIAMOnD (disease module detection) algorithm. Starting from the assumption that PPI networks can be viewed as maps where diseases can be identified with localized perturbation within a specific neighborhood (i.e., disease modules), DIAMOnD performs a systematic analysis of the human PPI network to uncover new disease-associated genes by exploiting the connectivity significance instead of connection density. The past few years have witnessed the increasing interest in understanding the molecular mechanism of post-transcriptional regulation with a special emphasis on non-coding RNAs since they are emerging as key regulators of many cellular processes in both physiological and pathological states. Recent findings show that coding genes are not the only targets that microRNAs interact with. In fact, there is a pool of different RNAs—including long non-coding RNAs (lncRNAs) —competing with each other to attract microRNAs for interactions, thus acting as competing endogenous RNAs (ceRNAs). The framework of regulatory networks provides a powerful tool to gather new insights into ceRNA regulatory mechanisms. Here, we describe a data-driven model recently developed to explore the lncRNA-associated ceRNA activity in breast invasive carcinoma. On the other hand, a very promising example of the co-expression network is the one implemented by the software SWIM (switch miner), which combines topological properties of correlation networks with gene expression data in order to identify a small pool of genes—called switch genes—critically associated with drastic changes in cell phenotype. Here, we describe SWIM tool along with its applications to cancer research and compare its predictions with DIAMOnD disease genes

conformational redistribution of honey components following different storage conditions

The present study aims at the investigation of the changes in water distribution among the organic components of selected honey samples following honey storage at different temperatures. Results, achieved by application of fast field cycling NMR relaxometry, revealed that the organic constituents were homogeneously distributed within the whole samples stored at room temperature. Conversely, after four months of refrigeration at 4°C, the organic systems were included in persistent clusters, as a consequence of the water release due to the larger stability of the intramolecular interactions over the intermolecular ones. The new conformational arrangements of the honey constituents entailed enhancement of honey moisture content. For this reason, it can be suggested that honey refrigeration prior to storage at room temperature may be detrimental for its long-term storage. In fact, higher risk of fermentation may occur once the sample is warmed after the first refrigeration step

How Social Norms Can Make the World More Regular and Better

Is there any difference between social norms and mere regularities emerging spontaneously from the behaviours of entities that have no norm-based cognition? And if so, which effects do we expect to observe in a world in which agents are endowed with such a type of cognition? The agent-based simulations presented here are aimed to understand what would happen in a world populated by normative agents, able to recognize norms and to reason upon them, compared to other, cognitively, less complex agents, following only their own individual goals

Emergence In the Loop: Simulating the two way dynamics of norm innovation

In this paper we will present the EMIL project, "EMergence In the Loop: Simulating the two-way dynamics of norm innovation", a three-year project funded by the European Commission (Sixth Framework Programme -Information Society and Technologies) in the framework of the initiative "Simulating Emergent Properties in Complex Systems". The EMIL project intends to contribute to the study of social complex systems by modelling norm innovation as a phenomenon implying interrelationships among multiple levels. It shall endeavour to point out that social dynamics in societies of intelligent agents is necessarily bi-directional, which adds complexity to the emergence processes. The micro-macro link will be modelled and observed in the emergence of properties at the macro-level and their immergence into the micro-level units. The main scientific aim of the EMIL project is to construct a simulator for exploring and experimenting norm-innovation

oxidative potential of selected pm components

The role of the single PM components in inducing the catalytic generation of reactive oxygen species (ROS), has not yet been clarified. Different a-cellular assay are currently used in the literature for the determination of the PM oxidative potential (OP), which is considered as a predictive index of its capacity to generate ROS in biological organisms. In order to better understand the existing correlations between PO and PM generated by specific emission sources, the water soluble and insoluble fractions of seven dust coming from specific sources were chemically characterised and analysed by three PO assays: the dithiothreitol (DTT, the acid ascorbic (AA) and the 2′,7′-dichlorofluorescin (DCFH) assays. PO and chemical data were elaborated by principal constituent analysis. The three methods responded in a very different way to each dust; they are then no-interchangeable and probably none of them is able to correctly predict the ROS generation in biological organisms. DTT was particularly sensitive to organic compounds, while AA was mostly influenced by inorganic components. DCFH results are more difficult to interpret and need to be further deepened. Furthermore, the results confirmed the important role played by the insoluble components of dusts in generating oxidative processes

SAveRUNNER: a network-based algorithm for drug repurposing and its application to COVID-19

The novelty of new human coronavirus COVID-19/SARS-CoV-2 and the lack of effective drugs and vaccines gave rise to a wide variety of strategies employed to fight this worldwide pandemic. Many of these strategies rely on the repositioning of existing drugs that could shorten the time and reduce the cost compared to de novo drug discovery. In this study, we presented a new network-based algorithm for drug repositioning, called SAveRUNNER (Searching off-lAbel dRUg aNd NEtwoRk), which predicts drug-disease associations by quantifying the interplay between the drug targets and the disease-specific proteins in the human interactome via a novel network-based similarity measure that prioritizes associations between drugs and diseases locating in the same network neighborhoods. Specifically, we applied SAveRUNNER on a panel of 14 selected diseases with a consolidated knowledge about their disease-causing genes and that have been found to be related to COVID-19 for genetic similarity, comorbidity, or for their association to drugs tentatively repurposed to treat COVID-19. Focusing specifically on SARS subnetwork, we identified 282 repurposable drugs, including some the most rumored off-label drugs for COVID-19 treatments, as well as a new combination therapy of 5 drugs, actually used in clinical practice. Furthermore, to maximize the efficiency of putative downstream validation experiments, we prioritized 24 potential anti-SARS-CoV repurposable drugs based on their network-based similarity values. These top-ranked drugs include ACE-inhibitors, monoclonal antibodies, and thrombin inhibitors. Finally, our findings were in-silico validated by performing a gene set enrichment analysis, which confirmed that most of the network-predicted repurposable drugs may have a potential treatment effect against human coronavirus infections.Comment: 42 pages, 9 figure

From anarchy to monopoly : how competition and protection shaped mafia's behavior

Mafia-like organizations are highly dynamic and organized criminal groups characterized by their extortive activities that impact societies and economies in different modes and magnitudes. This renders the understanding of how these organizations evolved an objective of both scientific and applicationoriented interests. We propose an agent-based simulation model - the Extortion Racket System model - aimed at understanding the factors and processes explaining the successful settlement of the Sicilian Mafia in Southern Italy, and which may more generally account for the transition from an anarchical situation of uncoordinated extortion to a monopolistic social order. Our results show that in situations of anarchy, these organizations do not last long. This indicates that a monopolistic situation shall be preferred over anarchical ones. Competition is a necessary and sufficient condition for the emergence of a monopolistic situation. However, when competition is combined with protection, the resulting monopolistic regime presents features that make it even more preferable and sustainable for the targets