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Development of personalized medicine in osteosarcoma by exploiting novel, representative, genetically characterized experimental models
No full textOsteosarcoma (OS) is the most common primary malignant tumor affecting bone. Its frequency is low (3.4 cases per year per million people), but because it primarily affects children and adolescents, its social impact is significant. The survival rate is around 70% but decreases dramatically to 20-30% for patients who are metastatic at diagnosis or do not respond to treatment. Managing OS remains challenging, necessitating a multifaceted approach. Current treatment protocols involve aggressive multidrug therapy and surgical resection, yet survival rates have remained unaltered for the past four decades. Molecularly, OS exhibits elevated levels of chromosomal structural variations, including whole genome duplication, chromotripsis, and kataegis. Somatic copy number alterations and structural variants, with few recurrent point mutations in protein-coding genes (except for TP53 and RB1), contribute to its complex yet low mutational burden. Intra- and inter-tumor heterogeneity in genomic alterations underscore the necessity for personalized, genome-driven therapeutic strategies. Recent research efforts have focused on identifying targeted genetic alterations for new therapeutic interventions. In this study, a sixty-seven-cancer gene panel was utilized through High-Throughput Sequencing to analyze twenty-one osteosarcoma samples, 21 Patient Derived Xenografts (PDXs), and 6 PDX-derived cell lines. Genomic correlations between OS samples, OS-PDX, and OS-PDX-derived cell lines were established and validated at the protein level. A highly aggressive OS cell line was employed to assess the efficacy of 2880 FDA-approved drugs via High Throughput Screening (HTS). Analysis revealed a low mutational burden with notable copy number level structural variations, identifying C-MYC, DDR2, CCNE1, and CDK4 as the most amplified genes. PDXs and PDX-derived cell lines mirrored the original tumors' genomic characteristics. Several compounds exhibited significant cell growth inhibition, prompting further investigation. Seventeen promising compounds are scheduled for D/R curve testing, indicating the potential of PDX models and associated cell lines as reliable preclinical platforms for personalized OS therapy
Engineering compartmentalized systems at the nanoscale
No full textThis work is focused on investigating compartmentalization effects on the operation of molecules and molecular machines in artificial systems. The thesis is divided into three experimental chapters encompassing different approaches to environmental compartmentalization and their subsequent influence provided to the system components, ranging on different complexity scales from phase-phase separation to molecular confinement. In the first project, it is described how a compartmentalized environment can affect the operation of molecular switches. The second project focused on approaching the opportunity of liposome membrane functionalization. In the third project, the effects of molecular crowding on the internal rotational processes in enamines were studied
Liquid biopsy for prostate cancer molecular characterization: homologous recombination system and genomic profile
No full textPathogenic aberrations in homologous recombination DNA repair (HRR) genes occur in approximately 1 to 4 men with advanced prostate cancer (PCa). Treatment with PARP inhibitors (PARPi) has recently been introduced for metastatic castration-resistant PCa patients, increasing clinicians' interest in the molecular characterization of all PCa patients. The limitations of using old, low-quality tumor tissue for genetic analysis, which is very common for PCa, can be overcome by using liquid biopsy as an alternative biomarker source. In this study, we aimed to evaluate the detection of molecular alterations in HRR genes on liquid biopsy compared with tumor tissue from PCa patients. Secondarily, we explored the genomic instability score (GIS), and a broader range of gene alterations for in-depth characterization of the PCa cohort. Plasma samples were collected from 63 patients with PCa. Sophia Homologous Recombination Solution (targeting 16 HRR genes) and shallow whole genome sequencing (sWGS) were used for genomic analysis of tissue DNA and circulating tumor DNA (ct). A total of 33 alterations (mainly on TP53, ATM, CHEK2, CDK12, and BRCA1/2) were identified in 28,5% of PCa plasma patients. By integrating the mutational and sWGS data, the HRR status of PCa patients was determined and a concordance agreement of 85,7% was identified with tumor tissue. A median GIS of 15 was obtained, reaching a score of 63 in 2 samples with double alterations, BRCA1 and TP53. We explored the PCa mutation landscape, and the most significant enriched pathways identified were the sphingosine 1-phosphate (S1P) receptor signaling and the PI3K-AKT-mTOR pathway. HRR analysis on FFPE and liquid biopsy samples show high concordance, demonstrating that the noninvasive ctDNA-enriched plasma can be an optimal alternative source for molecular SNV and CNV analysis. In addition, the evaluation of GIS and pathway interaction should be considered for more comprehensive molecular characterization in PCa patients
Analysis and prediction of fretting fatigue life.
No full textFretting fatigue is a fatigue damage process that occurs when two surfaces in contact with each other are subjected to relative micro-slip, causing a reduced fatigue life with respect to the plain fatigue case. Fretting has been now studied deeply for over 50 years, but still no univocal design approach has been universally accepted. This thesis presents a method for predicting the fretting fatigue life of materials based on the material specific fatigue parameters. To validate the method, a set of fretting fatigue experimental tests have been run, using a newly designed specimen. FE analyses of the tests were also run and the SWT parameter was retrieved and it was found
to be useful to successfully identify which samples failed. Finally, S-N curves were retrieved by using two different fatigue life predicting methods (CoffinManson and Jahed-Varvani). The two different methods were compared with
the experimental results and it was found that the Jahed-Varvani method gave accurate results in terms of fretting fatigue life
Numerical simulation of the flow and performance evaluation of an aerospike engine
No full textThe main focus of this work is to define a numerical methodology to simulate an aerospike engine and then to analyse the performance of DemoP1, which is a small aerospike demonstrator built by Pangea Aerospace.
The aerospike is a promising solution to build more efficient engine than the actual one. Its main advantage is the expansion adaptation that allows to reach the optimal expansion in a wide range of ambient pressures delivering more thrust than an equivalent bell-shaped nozzle. The main drawbacks are the cooling system design and the spike manufacturing but nowadays, these issues seem to be overcome with the use of the additive manufacturing method.
The simulations are performed with dbnsTurbFoam which is a solver of OpenFOAM. It has been designed to simulate a supersonic compressible turbulent flow.
This work is divided in four chapters. The first one is a short introduction. The second one shows a brief summary of the theoretical performance of the aerospike. The third one introduces the numerical methodology to simulate a compressible supersonic flow. In the fourth chapter, the solver has been verified with an experiment found in literature. And in the fifth chapter, the simulations on DemoP1 engine are illustrated
Service design innovation for supporting the well-being of chinese transnational left-behind children in Italy
No full textThis study delves into the experiences of Chinese transnational left-behind children in Italy and proposes service innovations to support them. Transnational left-behind children, a global phenomenon, stem from economic disparities and labor mobility. After over 3 years of research in Italian Chinatowns and Chinese immigrant communities, the researcher analyzed social support networks using service design tools and Grounded Theory for qualitative analysis. Collaborating with relevant individuals, the researcher co-created issue cards and empathy maps, forming a comprehensive understanding of the children's situation. The study identifies two main limitations in the current Italian support system: it primarily aids self-motivated individuals, overlooking those lacking motivation or facing setbacks. Additionally, the Chinese immigrant community, influenced by a meritocratic culture, lacks widespread awareness of charitable efforts for disadvantaged groups. Drawing from field research, the study redefines the challenges faced by transnational Chinese left-behind children, emphasizing issues like lack of agency and learned helplessness. Redesigned research questions aim to enhance their self-agency through innovative service design, fostering a positive outlook on life and active participation.
To address these issues, the study proposes the conceptual service of a social support platform named StoriaCità. It also introduces the concept of ‘Enabling Design’ to promote subjective agency and ego development, urging design researchers to focus on fostering users’ autonomy and potential, especially in cultures where purposelessness and disconnection prevail. Summarizing the research process as an Inclusive Service Design method tailored for minority groups, it integrates social sciences and design studies. By combining research findings from social sciences disciplines with the problem-solving capabilities of design, this approach aims to advance social equity and inclusivity in design interventions
Development and application of AI-based algorithms for engine emissions virtual sensing and vehicle NVH fault detection and diagnosis
No full textThis PhD thesis, developed in collaboration with the Ferrari Powertrain Development and Testing department, aims to develop algorithms and models to manage and analyse the available data, coming from experimental testing carried out in test cells, roller benches and on-board with real vehicle prototypes. To this end, the possibilities of the AI-based algorithms are explored in two different applications: (i) pollutant emissions virtual sensing and (ii) NVH fault detection and diagnosis.
The data from experimental tests on the Ferrari Purosangue are used to train and develop virtual sensors based on an enhanced version of the Light Gradient Boosting Regressor. The developed virtual sensor was used to predict both engine-out and tailpipe emissions, showing excellent results. Once the system is validated, it is deployed as a software to predict emissions during driving tests without physical sensors and in virtual environment to identify potential emissions-critical manoeuvres.
The second part of NVH fault detection and diagnosis is carried out by also involving the Test Division of Siemens Digital Industries. In this activity, systems based on autoencoders and a 2D-CNN classifier are developed to automatically detect and diagnose the faults emerged during the SF90 Stradale end-of-line testing, using the in-cabin audio recordings. To address the lack of anomalous experimental recordings, an NVH Simulator developed by Siemens is employed to synthesize a large dataset of faulty acoustic signals to train the AI models. Finally, these models are tested on synthesized sounds, showing excellent fault detection and diagnosis accuracy. Fine-tuning the model based on few-shot learning allows to improve the system’s accuracy even for applications on experimental data, making it implementable on future end-of-line testing. In conclusion, two innovative AI-based tools for emissions virtual sensing and NVH fault detection and diagnosis have been successfully developed and deployed to fulfil current needs of automotive manufacturers
Innovative and sustainable solutions for quality control of virgin olive oils and valorization of olive pomace
No full textThis thesis reports the main results of the research activities carried out during the presented Ph.D. project. In this work, innovative solutions for quality control of virgin olive oils and technological valorization of olive pomace were investigated in the food sciences and technology context. Concerning the quality control of virgin olive oils, the conducted studies were aimed to the development and application of innovative, rapid, and sustainable analytical instrumental methods to support the sensory analysis (Panel test). In particular, a focus was carried out on the study of the volatile fraction by gas-chromatographic analyses, such as HS-GC-IMS and Flash-GC, and an additional one based on spectroscopic techniques, i.e. FT-IR, NIR, and FT-Raman, as relevant potential tools in the determination of the commercial category of virgin olive oils. The final aim is to propose the investigated techniques as screening methods to pre-classify the samples in combination with the Panel test to speed up and simplify the quality control procedures carried out by laboratories and companies of the olive oil sector. Moreover, the activity regarding the technological valorization of olive pomace was focused on the set-up of sustainable methods for the extraction of phenolic compounds (i.e. by a mechanical approach and using less toxic solvents than those usually adopted) by obtaining extracts rich in phenolic compounds, potentially usable in different industrial sectors, such as pharmaceutical, food and cosmetic, as well as the characterization and stability evaluation of the so obtained phenolic extracts. This activity was aimed to the recovery of valuable phenolic compounds from this by-product in a circular economy perspective; since, even if olive mill pomace represents an important environmental problem, it still contains molecules, i.e. phenolic compounds, widely recognised for their healthy properties (e.g. antioxidant activity)
Integration of flexible multibody system dynamics and virtual commissioning of mechatronic systems. A case study: advanced modelling and simulations of a transfer machine tool.
No full textIn the industrial manufacturing sector, the Industry 4.0 paradigms have fostered the proliferation of advanced modelling tools capable of simulating virtual replica of production systems and processes, offering manufacturers advantages to keep global market competitivity. Simultaneously, with the growing complexity of modern production systems, software developers are facing increasingly burdensome challenges.
Currently, to the best author’s knowledge, there is no commercial software that reliably enables comprehensive simulations of complex mechatronic systems, which require the integration of advanced analyses in the different domains of Mechanics and Automation. A methodology to address this gap is proposed and herein illustrated. Flexible Multibody models’ simulations, for elastodynamic analyses of the mechanical systems, are synergically combined with the Virtual Commissioning of the machines’ automations, to account for control issues. The methodology is based on a recursive implementation of simulations performed in two different software frameworks (for Mechanics and Automation domains, respectively) to study the overall response of mechatronic systems.
The procedure is implemented referring to a practical application: a transfer machine tool is targeted as case-study. Each of its working station hosts modular operating units, 3-axis milling machines. Elastodynamic models are developed according to different modelling approaches, simulated and finally validated based on experimental data retrieved from ad-hoc tests. The alternative modelling strategies are discussed. As for the Automation domain, a model for the Virtual Commissioning of the overall machine tool is developed and simulated to virtually test and debug real Part Programs, enabling working cycle optimization prior to its physical commissioning. To facilitate integration among the software simulation frameworks, the flexible multibody model of the operating unit is combined with its control system model, enabling Co-Simulations.
The synergetic use of the modelling-and-simulation frameworks is illustrated along with the impact of the resulting outcomes over the design process of automatic machinery in real industrial scenarios
Beyond normality: small area estimation based on generalized additive models for location, scale, and shape.
No full textThis research presents three papers that explore new methodologies at the intersection of Small Area Estimation (SAE) and Generalized Additive Models for Location, Scale, and Shape (GAMLSS), contributing novel insights to socioeconomic statistics. The first proposal introduces a SAE model based on GAMLSS, SAE-GAMLSS, for estimating household indicators. This model challenges traditional assumptions of normality by allowing more than 100 different distributions (Rigby et al., 2019). In GAMLSS each parameter can depend on covariates. Simulation results demonstrate that SAE-GAMLSS outperforms the empirical best linear unbiased predictor in various scenarios. In the application, per-capita consumption at the regional level is estimated, distinguishing between urban and rural areas, and reveals nuanced findings regarding the North-South economic divide for foreigners in Italy. The second study focuses on economic inequality among foreigners in Italy at the regional level, distinguishing between urban, peri-urban, and rural areas. A Simplified SAE-GAMLSS is proposed to overcome two common challenges in unit-level SAE: difficulties in finding covariates and high computational times. The Simplified SAE-GAMLSS, even without covariates, effectively reduces the variability of the direct estimator, minimizing computational burden. Simulation results support these findings. In the application, the Atkinson index is estimated. Findings indicate significant disparities in inequality between foreigners and natives, providing valuable insights for formulating targeted policies promoting equity and immigrant integration. Lastly, the third investigation addresses the critical issue of measuring the Carbon Footprint (CFP) at the household level. The study proposes the use of a conversion factor matrix to bridge macroeconomic data with consumption data and suggests employing the Generalized Beta Distribution of the Second kind (GB2) to describe households’ CFP. To obtain reliable per-capita CFP estimates at the provincial level, a SAE-GAMLSS based on a GB2 distribution is introduced