https://hdl.handle.net/10955/10 La collezione contiene le tesi di dottorato dell'Università della Calabria dal 2004 (in aggiornamento) 2026-04-09T03:03:22Z https://hdl.handle.net/10955/5630 Diagnostic challenges for genetic approaches in Amyotrophic Lateral Sclerosis Perrone, Benedetta; Catalano, Stefania; Conforti, Francesca Luisa Over the past years, our understanding of the genetic mechanisms involved in complex diseases, such as Amyotrophic Lateral Sclerosis, has increased dramatically. ALS is a fatal and devastating motor neuron disease for which there is no truly effective cure. In 1993, the first gene associated with ALS was identified (1). Since then, our knowledge of the genetic mechanisms of disease has expanded significantly. Diagnostic tools have followed these research insights and Sanger DNA sequencing has been routinely used for many years. The emergence of next-generation DNA sequencing (NGS) approaches in the same decade allowed high throughput approaches to DNA sequencing, enabling the identification of new genes and pathways that highlight the heterogeneity of ALS disease, providing exciting opportunities for the identification of biomarkers useful for patient stratification and helping the development of targeted therapies. Despite our increased understanding of the mechanisms of this disease, the majority of patients remain undiagnosed, and the remaining cases have no successful treatments. The absence of an effective cure can be well explained by the complex and heterogeneous nature of ALS, with patients displaying distinct clinical characteristics and distinct molecular mechanisms. In this context, the molecular profiling of patients into clinically meaningful subgroups can be extremely valuable for the development of new precision diagnostics. In this thesis project, we provide an overview on the genetic investigation of ALS patients using different diagnostic approaches highlighting the importance of each methodology and their integrative use for the study of the disease, with the aim of providing a more comprehensive characterization of patients useful for the development of new-targeted strategies in clinical practice and personalized medicine. Università della Calabria. Dipartimento di Farmacia e Scienze della Salute e della Nutrizione. Dottorato di ricerca in Medicina Traslazionale. Ciclo XXXV 2023-02-28T00:00:00Z https://hdl.handle.net/10955/5629 Breast tumor microenvironment and endocrine resistance: dissecting the molecular link Caruso, Amanda; Catalano, Stefania; Andò, Sebastiano Università della Calabria. Dipartimento di Farmacia e Scienze della Salute e della Nutrizione. Dottorato di ricerca in Medicina Traslazionale. Ciclo XXXV 2023-07-04T00:00:00Z https://hdl.handle.net/10955/5628 Small molecules from cycloaddition reactions: synthesis, theoretical perspectives, and biological evaluation Tallarida, Matteo Antonio; Maiuolo, Loredana; Catalano, Stefania; Breugst, Martin; Rutjes, Floris The research work is related to a Ph.D. course in Translational Medicine of the Department of Pharmacy, Health, and Nutritional Sciences, University of Calabria. The project was carried out at the Department of Chemistry and Chemical Technologies of the same institution under the supervision of Prof. Loredana Maiuolo in the Laboratory of Organic Synthesis and Chemical Preparations (LabOrSy) headed by Prof. Antonio De Nino. The main subject of this research regards the use of cycloaddition reactions for the synthesis of small molecules with potential biological activity in diverse contexts. Alongside the prominent synthetic part, a series of QM computational studies were conducted to clarify some reaction mechanisms. In addition, molecular docking studies were performed to propose potential targets for some of the prepared compounds. The work is subdivided into four main parts. The first chapter is dedicated to the synthesis of 1,5-disubstituted 1,2,3-triazoles, to a series of molecular docking simulations, and to the biological evaluation of two compounds as inhibitors of the permeability transition pore opening event. The second part is about the microwave-assisted synthesis of isoxazolidine bisphosphonates as potential farnesyl pyrophosphate synthase (hFPPS) inhibitors. The third chapter focuses on the use of pyridinium ylides as building blocks for the multicomponent synthesis of indolizines and spirocyclopropyl oxindoles. The reaction mechanism regarding these latter was computationally investigated. The fourth – and last – chapter regards the synthesis and the radical expansion reaction of norbornane derivatives. A computational assessment of the mechanism is reported also in this case. All the computational studies reported in chapters 1, 3, and 4 were conducted in the frame of an abroad research stay spent in the Computational Chemistry Group headed by Dr. Gonzalo Jiménez Osés of the Center for Cooperative Research in Biosciences (CIC bioGUNE). Università della Calabria. Dipartimento di Farmacia e scienze della salute e della nutrizione. Dottorato di ricerca in Medicina Traslazionale. Ciclo XXXV 2023-02-07T00:00:00Z https://hdl.handle.net/10955/5627 Ensemble of deep learning prediction models for data analytics Zicari, Paolo; Fortino, Giancarlo; Folino, Gianluigi The abundance of available unstructured or raw text requires the automatic extraction of information for di↵erent tasks. One of the most relevant, Text Classification, extracts this information by assigning informative labels to raw texts from a pre-defined set. Deep Learning (DL) o↵ers challenging solutions to the automatic text classification problem. Despite the great potentialities of DL-based text classifiers, current solutions are exposed to a number of challenging issues that frequently occur in scenarios where text categorization is used in reallife applications. First of all, a large number of labelled data are usually necessary to train a deep model adequately, while labelling texts is timeconsuming, expensive, and very often requires specific knowledge. Moreover, configuring the structure and hyper-parameters of a Deep Neural Network (DNN) architecture is a difficult task, which entails long and careful design and tuning activities to make the DNN perform well. Typical scenarios are characterized by the fact that classes are often imbalanced. These issues entail a high risk of eventually obtaining a DNN-based classifier that overfits the training data and relies on non-general, biased and unreliable classification patterns. On the other hand, the black-box nature of a DNN model does not allow for easy reasoning on which features of a data instance drove the model to its classification decision. The work in this thesis, starting from the general problem of text classification, focuses on some challenging aspects associated with using an ensemble of deep learning methods to classify raw texts. More in detail, this work focuses on the analysis, exploration, study and test of algorithms and learning models to be employed in the proposal of novel techniques of Ensemble Deep Learning (EDL) aimed at performing classification and explanation tasks and on the research of semi-supervised strategies based on pseudo-labelling for improving classifier prediction performances in case of scarcity of labelled data. To this aim, this thesis proposes a complete framework based on the paradigm of ensembles of deep learning algorithms. The proposed framework is designed to furnish a valid instrument for exploring, validating and testing the proposed novel deep ensemble techniques contextualised in reallife applications, covering the entire classification process, including preprocessing, learning model building, explanation of the results, self-training for scarce labelled data, human-in-the-loop validating and model refining. Even though the methods proposed in this work could be used in any field of interest, the problem of extracting information from the raw text was specialised for two specific application contexts: automatic customer support ticket classification and the problem of fake detection. The first application scenario deals with the necessity of the Customer Care Department of most companies to answer their customer requests applied as tickets through several common channels like email, short message texts, social posts, etc. Ticket classification is necessary for automatic answer generation and routing to the specific human operator. Limiting the spread of misinformation, related to the high growth of social media dissemination and sharing of information, has raised the issue of distinguishing true news from fakes, with the challenging problem of processing long texts like news for fake detection. For this reason, the second scenario deals with the critical problem of discerning fake news from the vast amount of information circulating on the Web. In these research areas, the ensemble paradigm has been adopted only recently; thus, discovering the possible advantages when applying this technique is challenging. Experimental tests conducted on real data collected by two Customer Relationship Management (CRM) systems have proven the framework’s effectiveness in di↵erent ticket categorisation tasks and the practical value of their associated explanations. In addition, experiments conducted on two fake news datasets have proven the e↵ectiveness of the proposed semisupervised self-training ensemble-based strategy for improving performances when a few labelled data are available. Università della Calabria. Dipartimento di Ingegneria Informatica, Modellistica, Elettronica e Sistemistica Dottorato di Ricerca in Information and Communication Technologies CICLO XXXV 2021-06-21T00:00:00Z