Medial Artery Calcification (MAC) e Small Artery Disease (SAD) nei pazienti con ischemia critica degli arti inferiori: definizione dei fattori predisponenti, svilup-po di uno score prognostico, definizione istopatologica e valutazione dei risultati dell'arterializzazione delle vene dell'arto inferiore come tecnica di rivascolarizzazione

Questo studio si concentra sull'ischemia critica cronica dell'arto inferiore (CLTI), una patologia globale con gravi complicanze e impatto sociale elevato. Recentemente, la "Medial Artery Calcification" (MAC) è emersa come fattore prognostico significativo nei pazienti con CLTI e malattia grave dei vasi del piede, ma le informazioni sono principalmente retrospettive. Questa tesi esplora la relazione tra MAC e CLTI in tre sezioni. Nella sezione clinica, 248 pazienti sono stati divisi in gruppi MAC per valutare l'impatto prospettico sulla guarigione e sul salvataggio dell'arto. Nella sezione isto-patologica, campioni arteriosi di 26 pazienti sottoposti ad amputazione maggiore sono stati analizzati per comprendere la relazione tra MAC, aterosclerosi e occlusione vascolare. Nella sezione di arterializzazione, 16 pazienti sottoposti all'arterializzazione delle vene del piede (AVP) sono stati esaminati per valutare i risultati clinici prospettici. I risultati della sezione clinica indicano che la presenza di MAC severa è associata a risultati clinici peggiori nei pazienti affetti da CLTI. L'analisi isto-patologica mostra una prevalenza elevata di MAC rispetto all'aterosclerosi, con una associazione importante tra MAC e iperplasia intimale. L'AVP presenta risultati promettenti nei pazienti affetti da CLTI. In conclusione, la MAC influisce sui risultati clinici della CLTI, e l'AVP potrebbe essere una strategia efficace di trattamento.

Fractal descriptors for quantifying brain complexity in MRI

Magnetic Resonance Imaging (MRI) is the in vivo technique most commonly employed to characterize changes in brain structures. The conventional MRI-derived morphological indices are able to capture only partial aspects of brain structural complexity. Fractal geometry and its most popular index, the fractal dimension (FD), can characterize self-similar structures including grey matter (GM) and white matter (WM). Previous literature shows the need for a definition of the so-called fractal scaling window, within which each structure manifests self-similarity. This justifies the existence of fractal properties and confirms Mandelbrot’s assertion that "fractals are not a panacea; they are not everywhere". In this work, we propose a new approach to automatically determine the fractal scaling window, computing two new fractal descriptors, i.e., the minimal and maximal fractal scales (mfs and Mfs). Our method was implemented in a software package, validated on phantoms and applied on large datasets of structural MR images. We demonstrated that the FD is a useful marker of morphological complexity changes that occurred during brain development and aging and, using ultra-high magnetic field (7T) examinations, we showed that the cerebral GM has fractal properties also below the spatial scale of 1 mm. We applied our methodology in two neurological diseases. We observed the reduction of the brain structural complexity in SCA2 patients and, using a machine learning approach, proved that the cerebral WM FD is a consistent feature in predicting cognitive decline in patients with small vessel disease and mild cognitive impairment. Finally, we showed that the FD of the WM skeletons derived from diffusion MRI provides complementary information to those obtained from the FD of the WM general structure in T1-weighted images. In conclusion, the fractal descriptors of structural brain complexity are candidate biomarkers to detect subtle morphological changes during development, aging and in neurological diseases.

Development and characterization of deep learning techniques for neuroimaging data

Deep learning methods are extremely promising machine learning tools to analyze neuroimaging data. However, their potential use in clinical settings is limited because of the existing challenges of applying these methods to neuroimaging data. In this study, first a data leakage type caused by slice-level data split that is introduced during training and validation of a 2D CNN is surveyed and a quantitative assessment of the model’s performance overestimation is presented. Second, an interpretable, leakage-fee deep learning software written in a python language with a wide range of options has been developed to conduct both classification and regression analysis. The software was applied to the study of mild cognitive impairment (MCI) in patients with small vessel disease (SVD) using multi-parametric MRI data where the cognitive performance of 58 patients measured by five neuropsychological tests is predicted using a multi-input CNN model taking brain image and demographic data. Each of the cognitive test scores was predicted using different MRI-derived features. As MCI due to SVD has been hypothesized to be the effect of white matter damage, DTI-derived features MD and FA produced the best prediction outcome of the TMT-A score which is consistent with the existing literature. In a second study, an interpretable deep learning system aimed at 1) classifying Alzheimer disease and healthy subjects 2) examining the neural correlates of the disease that causes a cognitive decline in AD patients using CNN visualization tools and 3) highlighting the potential of interpretability techniques to capture a biased deep learning model is developed. Structural magnetic resonance imaging (MRI) data of 200 subjects was used by the proposed CNN model which was trained using a transfer learning-based approach producing a balanced accuracy of 71.6%. Brain regions in the frontal and parietal lobe showing the cerebral cortex atrophy were highlighted by the visualization tools.

The search for Multiple Myeloma Stem cells: molecular characterization and self-renewal mechanisms involved in the disease persistence

The existence of Multiple Myeloma Stem cells (MMSCs)is supposed to be one of the major causes of MM drug-resistance. However, very little is known about the molecular characteristics of MMSCs, even if some studies suggested that these cells resembles the memory B cells. In order to molecularly characterize MMSCs, we isolated the 138+138- population. For each cell fraction we performed a VDJ rearrangement analysis. The complete set of aberrations were performed by SNP Array 6.0 and HG-U133 Plus 2.0 microarray analyses (Affymetrix). The VDJ rearrangement analyses confirmed the clonal relationship between the 138+ clone and the immature clone. Both BM and PBL 138+ clones showed exactly the same genomic macroalterations. In the BM and PBL 138-19+27+ cell fractions several micro-alterations (range: 1-350 Kb) unique of the memory B cells clone were highlighted. Any micro-alterations detected were located out of any genomic variants region and are presumably associated to the MM pathogenesis, as confirmed by the presence of KRAS, WWOX and XIAP genes among the amplified regions. To get insight into the biology of the clonotypic B cell population, we compared the gene expression profile of 8 MM B cells samples 5 donor B cells vs, thus showing a differential expression of 11480 probes (p-value: <0,05). Among the self-renewal mechanisms, we observed the down-regulation of Hedgehog pathway and the iperactivation of Notch and Wnt signaling. Moreover, these immature cells showed a particular phenotype correlated to resistance to proteasome inhibitors (IRE1α-XBP1: -18.0; -19.96. P<0,05). Data suggested that the MM 138+ clone might resume the end of the complex process of myelomagenesis, whereas the memory B cells have some intriguing micro-alterations and a specific transcriptional program, supporting the idea that these post germinal center cells might be involved in the transforming event that originate and sustain the neoplastic clone.

Multiple testing in spatial epidemiology: a Bayesian approach

In this work we aim to propose a new approach for preliminary epidemiological studies on Standardized Mortality Ratios (SMR) collected in many spatial regions. A preliminary study on SMRs aims to formulate hypotheses to be investigated via individual epidemiological studies that avoid bias carried on by aggregated analyses. Starting from collecting disease counts and calculating expected disease counts by means of reference population disease rates, in each area an SMR is derived as the MLE under the Poisson assumption on each observation. Such estimators have high standard errors in small areas, i.e. where the expected count is low either because of the low population underlying the area or the rarity of the disease under study. Disease mapping models and other techniques for screening disease rates among the map aiming to detect anomalies and possible high-risk areas have been proposed in literature according to the classic and the Bayesian paradigm. Our proposal is approaching this issue by a decision-oriented method, which focus on multiple testing control, without however leaving the preliminary study perspective that an analysis on SMR indicators is asked to. We implement the control of the FDR, a quantity largely used to address multiple comparisons problems in the eld of microarray data analysis but which is not usually employed in disease mapping. Controlling the FDR means providing an estimate of the FDR for a set of rejected null hypotheses. The small areas issue arises diculties in applying traditional methods for FDR estimation, that are usually based only on the p-values knowledge (Benjamini and Hochberg, 1995; Storey, 2003). Tests evaluated by a traditional p-value provide weak power in small areas, where the expected number of disease cases is small. Moreover tests cannot be assumed as independent when spatial correlation between SMRs is expected, neither they are identical distributed when population underlying the map is heterogeneous. The Bayesian paradigm oers a way to overcome the inappropriateness of p-values based methods. Another peculiarity of the present work is to propose a hierarchical full Bayesian model for FDR estimation in testing many null hypothesis of absence of risk.We will use concepts of Bayesian models for disease mapping, referring in particular to the Besag York and Mollié model (1991) often used in practice for its exible prior assumption on the risks distribution across regions. The borrowing of strength between prior and likelihood typical of a hierarchical Bayesian model takes the advantage of evaluating a singular test (i.e. a test in a singular area) by means of all observations in the map under study, rather than just by means of the singular observation. This allows to improve the power test in small areas and addressing more appropriately the spatial correlation issue that suggests that relative risks are closer in spatially contiguous regions. The proposed model aims to estimate the FDR by means of the MCMC estimated posterior probabilities b i's of the null hypothesis (absence of risk) for each area. An estimate of the expected FDR conditional on data (\FDR) can be calculated in any set of b i's relative to areas declared at high-risk (where thenull hypothesis is rejected) by averaging the b i's themselves. The\FDR can be used to provide an easy decision rule for selecting high-risk areas, i.e. selecting as many as possible areas such that the\FDR is non-lower than a prexed value; we call them\FDR based decision (or selection) rules. The sensitivity and specicity of such rule depend on the accuracy of the FDR estimate, the over-estimation of FDR causing a loss of power and the under-estimation of FDR producing a loss of specicity. Moreover, our model has the interesting feature of still being able to provide an estimate of relative risk values as in the Besag York and Mollié model (1991). A simulation study to evaluate the model performance in FDR estimation accuracy, sensitivity and specificity of the decision rule, and goodness of estimation of relative risks, was set up. We chose a real map from which we generated several spatial scenarios whose counts of disease vary according to the spatial correlation degree, the size areas, the number of areas where the null hypothesis is true and the risk level in the latter areas. In summarizing simulation results we will always consider the FDR estimation in sets constituted by all b i's selected lower than a threshold t. We will show graphs of the\FDR and the true FDR (known by simulation) plotted against a threshold t to assess the FDR estimation. Varying the threshold we can learn which FDR values can be accurately estimated by the practitioner willing to apply the model (by the closeness between\FDR and true FDR). By plotting the calculated sensitivity and specicity (both known by simulation) vs the\FDR we can check the sensitivity and specicity of the corresponding\FDR based decision rules. For investigating the over-smoothing level of relative risk estimates we will compare box-plots of such estimates in high-risk areas (known by simulation), obtained by both our model and the classic Besag York Mollié model. All the summary tools are worked out for all simulated scenarios (in total 54 scenarios). Results show that FDR is well estimated (in the worst case we get an overestimation, hence a conservative FDR control) in small areas, low risk levels and spatially correlated risks scenarios, that are our primary aims. In such scenarios we have good estimates of the FDR for all values less or equal than 0.10. The sensitivity of\FDR based decision rules is generally low but specicity is high. In such scenario the use of\FDR = 0:05 or\FDR = 0:10 based selection rule can be suggested. In cases where the number of true alternative hypotheses (number of true high-risk areas) is small, also FDR = 0:15 values are well estimated, and \FDR = 0:15 based decision rules gains power maintaining an high specicity. On the other hand, in non-small areas and non-small risk level scenarios the FDR is under-estimated unless for very small values of it (much lower than 0.05); this resulting in a loss of specicity of a\FDR = 0:05 based decision rule. In such scenario\FDR = 0:05 or, even worse,\FDR = 0:1 based decision rules cannot be suggested because the true FDR is actually much higher. As regards the relative risk estimation, our model achieves almost the same results of the classic Besag York Molliè model. For this reason, our model is interesting for its ability to perform both the estimation of relative risk values and the FDR control, except for non-small areas and large risk level scenarios. A case of study is nally presented to show how the method can be used in epidemiology.

New Synthetic Polyamines as Multi-Target-Directed Ligands for the Treatment of Alzheimer's Disease and Cancer: Design, Synthesis and Biological Evaluation

Alzheimer's disease (AD) and cancer represent two of the main causes of death worldwide. They are complex multifactorial diseases and several biochemical targets have been recognized to play a fundamental role in their development. Basing on their complex nature, a promising therapeutical approach could be represented by the so-called "Multi-Target-Directed Ligand" approach. This new strategy is based on the assumption that a single molecule could hit several targets responsible for the onset and/or progression of the pathology. In particular in AD, most currently prescribed drugs aim to increase the level of acetylcholine in the brain by inhibiting the enzyme acetylcholinesterase (AChE). However, clinical experience shows that AChE inhibition is a palliative treatment, and the simple modulation of a single target does not address AD aetiology. Research into newer and more potent anti-AD agents is thus focused on compounds whose properties go beyond AChE inhibition (such as inhibition of the enzyme β-secretase and inhibition of the aggregation of beta-amyloid). Therefore, the MTDL strategy seems a more appropriate approach for addressing the complexity of AD and may provide new drugs for tackling its multifactorial nature. In this thesis, it is described the design of new MTDLs able to tackle the multifactorial nature of AD. Such new MTDLs designed are less flexible analogues of Caproctamine, one of the first MTDL owing biological properties useful for the AD treatment. These new compounds are able to inhibit the enzymes AChE, beta-secretase and to inhibit both AChE-induced and self-induced beta-amyloid aggregation. In particular, the most potent compound of the series is able to inhibit AChE in subnanomolar range, to inhibit β-secretase in micromolar concentration and to inhibit both AChE-induced and self-induced beta-amyloid aggregation in micromolar concentration. Cancer, as AD, is a very complex pathology and many different therapeutical approaches are currently use for the treatment of such pathology. However, due to its multifactorial nature the MTDL approach could be, in principle, apply also to this pathology. Aim of this thesis has been the development of new molecules owing different structural motifs able to simultaneously interact with some of the multitude of targets responsible for the pathology. The designed compounds displayed cytotoxic activity in different cancer cell lines. In particular, the most potent compounds of the series have been further evaluated and they were able to bind DNA resulting 100-fold more potent than the reference compound Mitonafide. Furthermore, these compounds were able to trigger apoptosis through caspases activation and to inhibit PIN1 (preliminary result). This last protein is a very promising target because it is overexpressed in many human cancers, it functions as critical catalyst for multiple oncogenic pathways and in several cancer cell lines depletion of PIN1 determines arrest of mitosis followed by apoptosis induction. In conclusion, this study may represent a promising starting pint for the development of new MTDLs hopefully useful for cancer and AD treatment.

Neuroprotective activity of guanosine in an in vitro model of Alzheimer's disease

The β-Amyloid (βA) peptide is the major component of senile plaques that are one of the hallmarks of Alzheimer’s Disease (AD). It is well recognized that Aβ exists in multiple assembly states, such as soluble oligomers or insoluble fibrils, which affect neuronal viability and may contribute to disease progression. In particular, common βA-neurotoxic mechanisms are Ca2+ dyshomeostasis, reactive oxygen species (ROS) formation, altered signaling, mitochondrial dysfunction and neuronal death such as necrosis and apoptosis. Recent study shows that the ubiquitin-proteasome pathway play a crucial role in the degradation of short-lived and regulatory proteins that are important in a variety of basic and pathological cellular processes including apoptosis. Guanosine (Guo) is a purine nucleoside present extracellularly in brain that shows a spectrum of biological activities, both under physiological and pathological conditions. Recently it has become recognized that both neurons and glia also release guanine-based purines. However, the role of Guo in AD is still not well established. In this study, we investigated the machanism basis of neuroprotective effects of GUO against Aβ peptide-induced toxicity in neuronal (SH-SY5Y), in terms of mitochondrial dysfunction and translocation of phosphatidylserine (PS), a marker of apoptosis, using MTT and Annexin-V assay, respectively. In particular, treatment of SH-SY5Y cells with GUO (12,5-75 μM) in presence of monomeric βA25-35 (neurotoxic core of Aβ), oligomeric and fibrillar βA1-42 peptides showed a strong dose-dependent inhibitory effects on βA-induced toxic events. The maximum inhibition of mitochondrial function loss and PS translocation was observed with 75 μM of Guo. Subsequently, to investigate whether neuroprotection of Guo can be ascribed to its ability to modulate proteasome activity levels, we used lactacystin, a specific inhibitor of proteasome. We found that the antiapoptotic effects of Guo were completely abolished by lactacystin. To rule out the possibility that this effects resulted from an increase in proteasome activity by Guo, the chymotrypsin-like activity was assessed employing the fluorogenic substrate Z-LLL-AMC. The treatment of SH-SY5Y with Guo (75 μM for 0-6 h) induced a strong increase, in a time-dependent manner, of proteasome activity. In parallel, no increase of ubiquitinated protein levels was observed at similar experimental conditions adopted. We then evaluated an involvement of anti and pro-apoptotic proteins such as Bcl-2, Bad and Bax by western blot analysis. Interestingly, Bax levels decreased after 2 h treatment of SH-SY5Y with Guo. Taken together, these results demonstrate that Guo neuroprotective effects against βA-induced apoptosis are mediated, at least partly, via proteasome activation. In particular, these findings suggest a novel neuroprotective pathway mediated by Guo, which involves a rapid degradation of pro-apoptotic proteins by the proteasome. In conclusion, the present data, raise the possibility that Guo could be used as an agent for the treatment of AD.

Multi-target-directed ligands: application to the Alzheimer's disease

The MTDL (multi-target-directed ligand) design strategy is used to develop single chemical entities that are able to simultaneously modulate multiple targets. The development of such compounds might disclose new avenues for the treatment of a variety of pathologies (e.g. cancer, AIDS, neurodegenerative diseases), for which an effective cure is urgently needed. This strategy has been successfully applied to Alzheimer’s disease (AD) due to its multifactorial nature, involving cholinergic dysfunction, amyloid aggregation, and oxidative stress. Despite many biological entities have been recognized as possible AD-relevant, only four achetylcholinesterase inhibitors (AChEIs) and one NMDA receptor antagonist are used in therapy. Unfortunately, such compounds are not disease-modifying agents behaving only as cognition enhancers. Therefore, MTDL strategy is emerging as a powerful drug design paradigm: pharmacophores of different drugs are combined in the same structure to afford hybrid molecules. In principle, each pharmacophore of these new drugs should retain the ability to interact with its specific site(s) on the target and, consequently, to produce specific pharmacological responses that, taken together, should slow or block the neurodegenerative process. To this end, the design and synthesis of several examples of MTDLs for combating neurodegenerative diseases have been published. This seems to be the more appropriate approach for addressing the complexity of AD and may provide new drugs for tackling the multifactorial nature of AD, and hopefully stopping its progression. According to this emerging strategy, in this work thesis different classes of new molecular structures, based on the MTDL approach, have been developed. Moreover, curcumin and its constrained analogs have currently received remarkable interest as they have a unique conjugated structure which shows a pleiotropic profile that we considered a suitable framework in developing MTDLs. In fact, beside the well-known direct antioxidant activity, curcumin displays a wide range of biological properties including anti-inflammatory and anti-amyloidogenic activities and an indirect antioxidant action through activation of the cytoprotective enzyme heme oxygenase (HO-1). Thus, since many lines of evidence suggest that oxidative stess and mitochondria impairment have a cental role in age-related neurodegenerative diseases such as AD, we designed mitochondria-targeted antioxidants by connecting curcumin analogs to different polyamine chains that, with the aid of electrostatic force, might drive the selected antioxidant moiety into mitochondria.

Tumori primitivi multipli del polmone. Profilo clinico e biologico in pazienti affetti da neoplasie polmonari multiple. Analisi di elementi clinici e marcatori biologici come possibili fattori di differenziazione dei tumori polmonari multipli

Background: MPLC represents a diagnostic challenge. Topic of the discussion is how to distinguish these patients as a metastatic or a multifocal disease. While in case of the different histology there are less doubt on the opposite in case of same histology is mandatory to investigate on other clinical features to rule out this question. Matherials and Methods: A retrospective review identified all patients treated surgically for a presumed diagnosis of SPLC. Pre-operative staging was obtained with Total CT scan and fluoro-deoxy positron emission tomography and mediastinoscopy. Patients with nodes interest or extra-thoracic location were excluded from this study. Epidermal growth factor receptor (EGFR) expression with complete immunohistochemical analisis was evaluated. Survival was estimated using Kaplan-Meyer method, and clinical features were estimated using a long-rank test or Cox proportional hazards model for categorical and continuous variable, respectively. Results: According to American College Chest Physician, 18 patients underwent to surgical resection for a diagnosis of MPLC. Of these, 8 patients had 3 or more nodules while 10 patients had less than 3 nodules. Pathologic examination demonstrated that 13/18(70%) of patients with multiple histological types was Adenocarcinoma, 2/18(10%) Squamous carcinoma, 2/18(10%) large cell carcinoma and 1/18(5%) Adenosquamosu carcinoma. Expression of EGFR has been evaluated in all nodules: in 7 patients of 18 (38%) the percentage of expression of each nodule resulted different. Conclusions: MPLC represent a multifocal disease where interactions of clinical informations with biological studies reinforce the diagnosis. EGFR could contribute to differentiate the nodules. However, further researches are necessary to validate this hypothesis.

The impact of polymorphisms in P-gp, DNA repair and folic acid metabolism genes in newly diagnosed multiple myeloma patients treated with thalidomide plus dexamethasone, with or without bortezomib

The principle aim of this study was to investigate biological predictors of response and resistance to multiple myeloma treatment. Two hypothesis had been proposed as responsible of responsiveness: SNPs in DNA repair and Folate pathway, and P-gp dependent efflux. As a first objective, panel of SNPs in DNA repair and Folate pathway genes, were analyzed. It was a retrospective study in a group of 454, previously untreated, MM patients enrolled in a randomized phase III open-label study. Results show that some SNPs in Folate pathway are correlated with response to MM treatment. MTR genotype was associated with favorable response in the overall population of MM patients. However, this relation, disappear after adjustment for treatment response. When poor responder includes very good partial response, partial response and stable/progressive disease MTFHR rs1801131 genotype was associated with poor response to therapy. This relation - unlike in MTR – was still significant after adjustment for treatment response. Identification of this genetic variant in MM patients could be used as an independent prognostic factor for therapeutic outcome in the clinical practice. In the second objective, basic disposition characteristics of bortezomib was investigated. We demonstrated that bortezomib is a P-gp substrate in a bi-directional transport study. We obtain apparent permeability rate values that together with solubility values can have a crucial implication in better understanding of bortezomib pharmacokinetics with respect to the importance of membrane transporters. Subsequently, in view of the importance of P-gp for bortezomib responsiveness a panel of SNPs in ABCB1 gene - coding for P-gp - were analyzed. In particular we analyzed five SNPs, none of them however correlated with treatment responsiveness. However, we found a significant association between ABCB1 variants and cytogenetic abnormalities. In particular, deletion of chromosome 17 and t(4;14) translocation were present in patients harboring rs60023214 and rs2038502 variants respectively.

Longitudinal evolution of cognitive functions in patients with multiple system atrophy: a prospective study

Introduction and Background: Multiple system atrophy (MSA) is a sporadic, adult-onset, progressive neurodegenerative disease characterized clinically by parkinsonism, cerebellar ataxia, and autonomic failure. We investigated cognitive functions longitudinally in a group of probable MSA patients, matching data with sleep parameters. Patients and Methods: 10 patients (7m/3f) underwent a detailed interview, a general and neurological examination, laboratory exams, MRI scans, a cardiovascular reflexes study, a battery of neuropsychological tests, and video-polysomnographic recording (VPSG). Patients were revaluated (T1) a mean of 16±5 (range: 12-28) months after the initial evaluation (T0). At T1, the neuropsychological assessment and VPSG were repeated. Results: The mean patient age was 57.8±6.4 years (range: 47-64) with a mean age at disease onset of 53.2±7.1 years (range: 43-61) and symptoms duration at T0 of 60±48 months (range: 12-144). At T0, 7 patients showed no cognitive deficits while 3 patients showed isolated cognitive deficits. At T1, 1 patient worsened developing multiple cognitive deficits from a normal condition. At T0 and T1, sleep efficiency was reduced, REM latency increased, NREM sleep stages 1-2 slightly increased. Comparisons between T1 and T0 showed a significant worsening in two tests of attention and no significant differences of VPSG parameters. No correlation was found between neuropsychological results and VPSG findings or RBD duration. Discussion and Conclusions: The majority of our patients do not show any cognitive deficits at T0 and T1, while isolated cognitive deficits are present in the remaining patients. Attention is the cognitive function which significantly worsened. Our data confirm the previous findings concerning the prevalence, type and the evolution of cognitive deficits in MSA. Regarding the developing of a condition of dementia, our data did not show a clear-cut diagnosis of dementia. We confirm a mild alteration of sleep structure. RBD duration does not correlate with neuropsychological findings.

The impact of intraclonal heterogeneity on the outcomes of Multiple Myeloma patients treated with new drugs

Understanding the biology of Multiple Myeloma (MM) is of primary importance in the struggle to achieve a cure for this yet incurable neoplasm. A better knowledge of the mechanism underlying the development of MM can guide us in the development of new treatment strategies. Studies both on solid and haematological tumours have shown that cancer comprises a collection of related but subtly different clones, a feature that has been termed “intra-clonal heterogeneity”. This intra-clonal heterogeneity is likely, from a “Darwinian” natural selection perspective, to be the essential substrate for cancer evolution, disease progression and relapse. In this context the critical mechanism for tumour progression is competition between individual clones (and cancer stem cells) for the same microenvironmental “niche”, combined with the process of adaptation and natural selection. The Darwinian behavioural characteristics of cancer stem cells are applicable to MM. The knowledge that intra-clonal heterogeneity is an important feature of tumours’ biology has changed our way to addressing cancer, now considered as a composite mixture of clones and not as a linear evolving disease. In this variable therapeutic landscape it is important for clinicians and researchers to consider the impact that evolutionary biology and intra-clonal heterogeneity have on the treatment of myeloma and the emergence of treatment resistance. It is clear that if we want to effectively cure myeloma it is of primarily importance to understand disease biology and evolution. Only by doing so will we be able to effectively use all of the new tools we have at our disposal to cure myeloma and to use treatment in the most effective way possible. The aim of the present research project was to investigate at different levels the presence of intra-clonal heterogeneity in MM patients, and to evaluate the impact of treatment on clonal evolution and on patients’ outcomes.

Sleep disorders in Multiple System Atrophy: a longitudinal study

Introduction: A higher frequency of sleep and breathing disorders in Multiple System Atrophy (MSA) populations is documented in literature. The analysis of disease progression and prognosis in patients with sleep and breathing disorders could shed light on specific neuropathology and pathophysiology of MSA. Objective: To characterize sleep disorders and their longitudinal modifications during disease course in MSA patients, and to determine their prognostic value. Methods: This is a retrospective and prospective cohort study including 182 MSA patients (58.8% males). Type of onset was defined by the first reported motor or autonomic symptom/sign related to MSA. The occurrence of symptoms/signs and milestones of disease progression and their latency were collected. REM sleep behaviour disorder (RBD) and stridor were video-polysomnography (VPSG)-confirmed. VPSG recordings were analysed in a standardized fashion during the disease course. Survival data were based on time to death from the first symptom of disease. Results: Isolated RBD represented the first MSA symptom in 30% of patients, preceding disease onset according to international criteria with a median of 3(1–5) years. Patients developing early stridor or presenting with RBD at disease onset showed a more rapid and severe disease progression. These features had independent negative prognostic value for survival. Sleep architecture was characterized by peculiar features which could represent negative markers in MSA prognosis. Patients with stridor treated with tracheostomy showed a reduced risk of death. Conclusions: This is one of the first studies focusing on longitudinal progression of sleep in MSA. Sleep disorders are key features of disease, playing a role in presentation, prognosis and progression. In our MSA cohort, RBD represented the most frequent mode of disease presentation. Moreover, some specific clinical and instrumental sleep features could represent a hallmark of MSA and could be involved in prognosis and, in particular, in sudden death and death during sleep.

The human gut microbiome in disease: Role in chemotherapy treatments and relationship with clinical outcomes

The gut microbiome (GM) is a plastic entity, capable of adapting in response to intrinsic and extrinsic factors. However, several circumstances can disrupt this homeostatic balance, forcing the GM to shift from a health-associated mutualistic configuration to a disease-associated profile. Nowadays, a new frontier of microbiome research is understanding the GM role in chemo-immunotherapies and clinical outcomes. Here, the role of the genotoxin‐producing pathogen Salmonella in colorectal carcinogenesis was characterized by in-vitro models. A synergistic effect of Salmonella and the CRC-associated mutation (APC gene) promoted a tumorigenic microenvironment by increasing cellular genomic instability. Subsequently, the GM involvement in anti-cancer therapies was investigated via next-generation sequencing in different patient cohorts. The GM trajectory during treatments was characterized for women with epithelial ovarian cancer and pediatric patients undergoing hematopoietic stem cell transplantation (HSCT). The results highlighted the loss of GM homeostasis, with diversity reduction, decrease in health-associated microorganisms and pathobiont bloom. Interestingly, a distinctive GM profile was identified in ovarian cancer patients with a poor response to chemotherapy compared to patients in remission. Moreover, maintenance of GM homeostasis through enteral feeding in pediatric HSCT patients highlighted a better prognosis, with reduced risk of clinical complications. In this context, the gut resistome – the pattern of GM antibiotic-resistance genes (ARGs) – was evaluated longitudinally in HSCT patients. The results showed new acquisitions and consolidation of ARGs already present in patients developing clinical complications. Antibiotic exposure was also evaluated in infants under low-dose antibiotic prophylaxis for vesico-ureteral reflux showing an impairment of the GM configuration with possible long-term health implications. Dramatic GM dysbiosis was finally observed in critically ill patients with COVID-19 (undergoing multiple drug therapies) and correlated with increased risk of bloodstream infection. All these findings pointed out the importance of maintaining GM homeostasis during chemotherapy treatments for improving patients’ clinical outcomes.

Evaluation of alpha-synuclein RT-QuIC for an early and differential diagnosis of Lewy body disease

Synucleinopathies are a group of neurodegenerative diseases characterized by tissue deposition of insoluble aggregates of the protein α-synuclein. Currently, the clinical diagnosis of these diseases, including Parkinson’s disease (PD), dementia with Lewy bodies (DLB), and multiple system atrophy (MSA), is very challenging, especially at an early disease stage, due to the heterogeneous and often non-specific clinical manifestations. Therefore, identifying specific biomarkers to aid the diagnosis and improve the clinical management of patients with these disorders represents a primary goal in the field. Pursuing this aim, we applied the α-Syn Real-Time Quaking-Induced Conversion (RT-QuIC), an ultrasensitive technique able to detect minute amounts of amyloidogenic proteins, to a large cohort of 953 CSF samples from clinically well-characterized (“clinical” group), or neuropathologically verified (“NP” group) patients with parkinsonism or dementia. Of significance, we also studied patients with prodromal synucleinopathies (“prodromal” group), such as pure autonomic failure (PAF) (n = 28), isolated REM sleep behavior disorder (iRBD) (n = 18), and mild cognitive impairment due to probable Lewy body (LB) disease (MCI-LB) (n = 81). Our findings show that α-syn RT-QuIC can accurately detect α-Syn seeding activity across the whole spectrum of LB-related disorders (LBD), exhibiting a mean sensitivity of 95.2% in the “clinical” and “NP” group, while ranging between 89.3% (PAF) and 100% (RBD) in the “prodromal group”. Moreover, we observed 95.1% sensitivity and 96.6% specificity in the distinction between MCI-LB patients and cognitively unimpaired controls, demonstrating the solid diagnostic potential of α-Syn RT-QuIC in the early phase of the disease. Finally, 13.3% of MCI-AD patients also had a positive test, suggesting an underlying LB co-pathology. This work demonstrated that α-Syn RT-QuIC is an efficient assay for accurate and early diagnosis of LBD, which should be implemented for clinical management and recruitment for clinical trials in memory clinics.