EXplainable Artificial Intelligence: enabling AI in neurosciences and beyond

The adoption of AI models in medicine and neurosciences has the potential to play a significant role not only in bringing scientific advancements but also in clinical decision-making. However, concerns mounts due to the eventual biases AI could have which could result in far-reaching consequences particularly in a critical field like biomedicine. It is challenging to achieve usable intelligence because not only it is fundamental to learn from prior data, extract knowledge and guarantee generalization capabilities, but also to disentangle the underlying explanatory factors in order to deeply understand the variables leading to the final decisions. There hence has been a call for approaches to open the AI `black box' to increase trust and reliability on the decision-making capabilities of AI algorithms. Such approaches are commonly referred to as XAI and are starting to be applied in medical fields even if not yet fully exploited. With this thesis we aim at contributing to enabling the use of AI in medicine and neurosciences by taking two fundamental steps: (i) practically pervade AI models with XAI (ii) Strongly validate XAI models. The first step was achieved on one hand by focusing on XAI taxonomy and proposing some guidelines specific for the AI and XAI applications in the neuroscience domain. On the other hand, we faced concrete issues proposing XAI solutions to decode the brain modulations in neurodegeneration relying on the morphological, microstructural and functional changes occurring at different disease stages as well as their connections with the genotype substrate. The second step was as well achieved by firstly defining four attributes related to XAI validation, namely stability, consistency, understandability and plausibility. Each attribute refers to a different aspect of XAI ranging from the assessment of explanations stability across different XAI methods, or highly collinear inputs, to the alignment of the obtained explanations with the state-of-the-art literature. We then proposed different validation techniques aiming at practically fulfilling such requirements. With this thesis, we contributed to the advancement of the research into XAI aiming at increasing awareness and critical use of AI methods opening the way to real-life applications enabling the development of personalized medicine and treatment by taking a data-driven and objective approach to healthcare

What PLS can still do for Imaging Genetics in Alzheimer's disease

In this work we exploited Partial Least Squares (PLS) model for analyzing the genetic underpinning of grey matter atrophy in Alzheimer's Disease (AD). To this end, 42 features derived from T1-weighted Magnetic Resonance Imaging, including cortical thicknesses and subcortical volumes were considered to describe the imaging phenotype, while the genotype information consisted of 14 recently proposed AD related Polygenic Risk Scores (PRS), calculated by including Single Nucleotide Polymorphism passing different significance thresholds. The PLS model was applied on a large study cohort obtained from the Alzheimer's Disease Neuroimaging Initiative (ADNI) database including both healthy individuals and AD patients, and validated on an independent ADNI Mild Cognitive Impairment (MCI) cohort, including Early (EMCI) and Late MCI (LMCI). The experimental results confirm the existence of a joint dynamics between brain atrophy and genotype data in AD, while providing important generalization results when tested on a clinically heterogeneous cohort. In particular, less AD specific PRS scores were negatively correlated with cortical thicknesses, while highly AD specific PRSs showed a peculiar correlation pattern among specific subcortical volumes and cortical thicknesses. While the first outcome is in line with the well known neurodegeneration process in AD, the second could be revealing of different AD subtypes

Vaginal microbiome and metabolome highlight specific signatures of bacterial vaginosis

In this study, we sought to find novel bacterial and metabolic hallmarks for bacterial vaginosis (BV). We studied the vaginal microbiome and metabolome of vaginal fluids from BV-affected patients (n = 43) and healthy controls (n = 37) by means of an integrated approach based on quantitative polymerase chain reaction (qPCR) and proton nuclear magnetic resonance ((1)H-NMR). The correlations between the clinical condition and vaginal bacterial communities were investigated by principal component analysis (PCA). To define the metabolomics signatures of BV, 100 discriminant analysis by projection on latent structure (PLS-DA) models were calculated. Bacterial signatures distinguishing the health condition and BV were identified by qPCR. Lactobacillus crispatus strongly featured the healthy vagina, while increased concentrations of Prevotella, Atopobium and Mycoplasma hominis specifically marked the infection. (1)H-NMR analysis has led to the identification and quantification of 17 previously unreported molecules. BV was associated with changes in the concentration of metabolites belonging to the families of amines, organic acids, short chain fatty acids, amino acids, nitrogenous bases and monosaccharides. In particular, maltose, kynurenine and NAD(+) primarily characterised the healthy status, while nicotinate, malonate and acetate were the best metabolic hallmarks of BV. This study helps to better understand the role of the vaginal microbiota and metabolome in the development of BV infection. We propose a molecular approach for the diagnosis of BV based on quantitative detection in the vaginal fluids of Atopobium, Prevotella and M. hominis, and nicotinate, malonate and acetate by combining qPCR and (1)H-NMR

MiR200 and MiR302: Two big families influencing stem cell behavior

In this review, we described different factors that modulate pluripotency in stem cells, in particular we aimed at following the steps of two large families of miRNAs: the miR-200 family and the miR-302 family. We analyzed some factors tuning stem cells behavior as TGF-\uce\ub2, which plays a pivotal role in pluripotency inhibition together with specific miRNAs, reactive oxygen species (ROS), but also hypoxia, and physical stimuli, such as ad hoc conveyed electromagnetic fields. TGF-\uce\ub2 plays a crucial role in the suppression of pluripotency thus influencing the achievement of a specific phenotype. ROS concentration can modulate TGF-\uce\ub2 activation that in turns down regulates miR-200 and miR-302. These two miRNAs are usually requested to maintain pluripotency, while they are down-regulated during the acquirement of a specific cellular phenotype. Moreover, also physical stimuli, such as extremely-low frequency electromagnetic fields or high-frequency electromagnetic fields conveyed with a radioelectric asymmetric conveyer (REAC), and hypoxia can deeply influence stem cell behavior by inducing the appearance of specific phenotypes, as well as a direct reprogramming of somatic cells. Unraveling the molecular mechanisms underlying the complex interplay between externally applied stimuli and epigenetic events could disclose novel target molecules to commit stem cell fate

Impact of a synbiotic food on the gut microbial ecology and metabolic profiles

<p>Abstract</p> <p>Background</p> <p>The human gut harbors a diverse community of microorganisms which serve numerous important functions for the host wellbeing. Functional foods are commonly used to modulate the composition of the gut microbiota contributing to the maintenance of the host health or prevention of disease. In the present study, we characterized the impact of one month intake of a synbiotic food, containing fructooligosaccharides and the probiotic strains <it>Lactobacillus helveticus </it>Bar13 and <it>Bifidobacterium longum </it>Bar33, on the gut microbiota composition and metabolic profiles of 20 healthy subjects.</p> <p>Results</p> <p>The synbiotic food did not modify the overall structure of the gut microbiome, as indicated by Polymerase Chain Reaction-Denaturing Gradient Gel Electrophoresis (PCR-DGGE). The ability of the probiotic <it>L. helveticus </it>and <it>B. longum </it>strains to pass through the gastrointestinal tract was hypothesized on the basis of real-time PCR data. In spite of a stable microbiota, the intake of the synbiotic food resulted in a shift of the fecal metabolic profiles, highlighted by the Gas Chromatography Mass Spectrometry Solid Phase Micro-Extraction (GC-MS/SPME) analysis. The extent of short chain fatty acids (SCFA), ketones, carbon disulfide and methyl acetate was significantly affected by the synbiotic food consumption. Furthermore, the Canonical discriminant Analysis of Principal coordinates (CAP) of GC-MS/SPME profiles allowed a separation of the stool samples recovered before and after the consumption of the functional food.</p> <p>Conclusion</p> <p>In this study we investigated the global impact of a dietary intervention on the gut ecology and metabolism in healthy humans. We demonstrated that the intake of a synbiotic food leads to a modulation of the gut metabolic activities with a maintenance of the gut biostructure. In particular, the significant increase of SCFA, ketones, carbon disulfide and methyl acetate following the feeding period suggests potential health promoting effects of the synbiotic food.</p

Study of the Synthetic Approach Influence in Ni/CeO2-Based Catalysts for Methane Dry Reforming

This study focuses on the synthetic approach influence in morphostructural features and catalytic performances for Ni/CeO2 catalysts. Incipient wetness impregnation, coprecipitation and nitrate combustion were studied as catalyst preparation approaches, and the materials were then tested at 700 C for methane dry reforming (MDR). The morphostructural properties of the materials were deeply studied using several techniques, such as temperature programmed reduction (TPR), to investigate reducibility and support-metal interaction, N2 physisorption to evaluate the porosity and the surface area, scanning electron microscopy (SEM) and X-ray diffraction (XRD) to estimate Ni dispersion, and temperature programmed oxidation (TPO) to identify the type and amount of coke formed on catalysts’ surface after reaction. From the data obtained, coprecipitation turned out to be the most suitable technique for this application because this catalyst was able to reach 70% of CO2 conversion and 30% methane conversion, with an H2 yield of 15% and 30% yield of CO at the end of the 30 h test. Moreover, it was also the catalyst with the highest metal dispersion, the strongest interaction with the support, and the lowest coke deposition

Microstructural MRI Correlates of Cognitive Impairment in Multiple Sclerosis: The Role of Deep Gray Matter

Although cognitive impairment (CI) is frequently observed in people with multiple sclerosis (pwMS), its pathogenesis is still controversial. Conflicting results emerged concerning the role of microstructural gray matter (GM) damage especially when involving the deep GM structures. In this study, we aimed at evaluating whether differences in cortical and deep GM structures between apparently cognitively normal (ACN) and CI pwMS (36 subjects in total) are present, using an extensive set of diffusion MRI (dMRI) indices and conventional morphometry measures. The results revealed increased anisotropy and restriction over several deep GM structures in CI compared with ACN pwMS, while no changes in volume were present in the same areas. Conversely, reduced anisotropy/restriction values were detected in cortical regions, mostly the pericalcarine cortex and precuneus, combined with reduced thickness of the superior frontal gyrus and insula. Most of the dMRI metrics but none of the morphometric indices correlated with the Symbol Digit Modality Test. These results suggest that deep GM microstructural damage can be a strong anatomical substrate of CI in pwMS and might allow identifying pwMS at higher risk of developing CI

Melatonin finely tunes proliferation and senescence in hematopoietic stem cells

Human hematopoietic stem/progenitor cells (HSPCs) are pluripotent cells that gradually lose their self-renewal and regenerative potential, to give rise to mature cells of the hematopoietic system by differentiation. HSPC infusion is used to restore hematopoietic function in patients with a variety of onco-hematologic and immune-mediated disorders. The functionality of these cells is therefore of great importance to ensure the homeostasis of the hematopoietic system. Melatonin plays an important role as immunomodulatory and oncostatic hormone. In the present manuscript, we aimed at evaluating the activity of melatonin in modulating HSPC senescence, in the attempt to improve their hemopoietic regenerative potential. We exposed HSPCs to melatonin, in different conditions, and then analyzed the expression of genes regulating cell cycle and cell senescence. Moreover, we assessed cell senescence by β-galactosidase and telomerase activity. Our results showed the ability of melatonin to counteract HSPC senescence, thus paving the way for enhanced efficiency in their clinical application

Titanium Dioxide-Based Nanocomposites for Enhanced Gas-Phase Photodehydrogenation

Light-driven processes can be regarded as a promising technology for chemical production within the bio-refinery concept, due to the very mild operative conditions and high selectivity of some reactions. In this work, we report copper oxide (CuO)-titanium dioxide (TiO2) nanocomposites to be efficient and selective photocatalysts for ethanol photodehydrogenation under gas phase conditions, affording 12-fold activity improvement compared to bare TiO2. In particular, the insertion method of the CuO co-catalyst in different TiO2 materials and its effects on the photocatalytic activity were studied. The most active CuO co-catalyst was observed to be highly dispersed on titania surface, and highly reducible. Moreover, such high dispersion was observed to passivate some surface sites where ethanol is strongly adsorbed, thus improving the activity. This kind of material can be obtained by the proper selection of loading technique for both co-catalysts, allowing a higher coverage of photocatalyst surface (complex-precipitation in the present work), and the choice of titania material itself. Loading copper on a high surface area titania was observed to afford a limited ethanol conversion, due to its intrinsically higher reactivity affording to a strong interaction with the co-catalyst

Intrinsic excitability in layer IV-VI anterior insula to basolateral amygdala projection neurons correlates with the confidence of taste valence encoding.

Avoiding potentially harmful, and consuming safe food is crucial for the survival of living organisms. However, the perceived valence of sensory information can change following conflicting experiences. Pleasurability and aversiveness are two crucial parameters defining the perceived valence of a taste and can be impacted by novelty. Importantly, the ability of a given taste to serve as the conditioned stimulus (CS) in conditioned taste aversion (CTA), is dependent on its valence. Activity in anterior insula (aIC) layer IV-VI pyramidal neurons projecting to the basolateral amygdala (BLA) is correlated with, and necessary for CTA learning and retrieval, as well as the expression of neophobia towards novel tastants, but not learning taste familiarity. Yet, the cellular mechanisms underlying the updating of taste valence representation in this specific pathway are poorly understood. Here, using retrograde viral tracing and whole -cell patch-clamp electrophysiology in trained mice, we demonstrate that the intrinsic properties of deep-lying layer IV-VI, but not superficial layer I-III aIC-BLA neurons, are differentially modulated by both novelty and valence, reflecting the subjective predictability of taste valence arising from prior experience. These correlative changes in the profile of intrinsic properties of LIV-VI aIC-BLA neurons were detectable following both simple taste experiences, as well as following memory retrieval, extinction learning and reinstatement.Significance statementLearning to form aversive or safe taste memories is dependent on genetic predisposition as well as previous experiences. In mice, anterior insula neurons projecting to the basolateral amygdala (aIC-BLA) are indispensable for learning and retrieving learned taste aversion. Kolatt Chandran et al. demonstrate that the intrinsic properties of aIC-BLA neurons, represent the certainty of taste valence prediction, but not percept. Predictive valence-specific changes are reflected through excitability, being low when taste outcome is highly predictive (i.e., following aversive taste memory retrieval or unreinforced familiarization), and high when taste valence is uncertain (i.e., following novelty or aversive taste memory extinction). In addition, the results propose a neuronal mechanism underlying the long delay between taste and visceral discomfort in conditioned taste aversion