3D Printing and bioprinting of osteomimetic materials for 3D in vitro models and tissue repair applications

Bone disorders have severe impact on body functions and quality life, and no satisfying therapies exist yet. The current models for bone disease study are scarcely predictive and the options existing for therapy fail for complex systems. To mimic and/or restore bone, 3D printing/bioprinting allows the creation of 3D structures with different materials compositions, properties, and designs. In this study, 3D printing/bioprinting has been explored for (i) 3D in vitro tumor models and (ii) regenerative medicine. Tumor models have been developed by investigating different bioinks (i.e., alginate, modified gelatin) enriched by hydroxyapatite nanoparticles to increase printing fidelity and increase biomimicry level, thus mimicking the organic and inorganic phase of bone. High Saos-2 cell viability was obtained, and the promotion of spheroids clusters as occurring in vivo was observed. To develop new syntethic bone grafts, two approaches have been explored. In the first, novel magnesium-phosphate scaffolds have been investigated by extrusion-based 3D printing for spinal fusion. 3D printing process and parameters have been optimized to obtain custom-shaped structures, with competent mechanical properties. The 3D printed structures have been combined to alginate porous structures created by a novel ice-templating technique, to be loaded by antibiotic drug to address infection prevention. Promising results in terms of planktonic growth inhibition was obtained. In the second strategy, marine waste precursors have been considered for the conversion in biogenic HA by using a mild-wet conversion method with different parameters. The HA/carbonate ratio conversion efficacy was analysed for each precursor (by FTIR and SEM), and the best conditions were combined to alginate to develop a composite structure. The composite paste was successfully employed in custom-modified 3D printer for the obtainment of 3D printed stable scaffolds. In conclusion, the osteomimetic materials developed in this study for bone models and synthetic grafts are promising in bone field.

A robotic platform for precision agriculture and applications

Agricultural techniques have been improved over the centuries to match with the growing demand of an increase in global population. Farming applications are facing new challenges to satisfy global needs and the recent technology advancements in terms of robotic platforms can be exploited. As the orchard management is one of the most challenging applications because of its tree structure and the required interaction with the environment, it was targeted also by the University of Bologna research group to provide a customized solution addressing new concept for agricultural vehicles. The result of this research has blossomed into a new lightweight tracked vehicle capable of performing autonomous navigation both in the open-filed scenario and while travelling inside orchards for what has been called in-row navigation. The mechanical design concept, together with customized software implementation has been detailed to highlight the strengths of the platform and some further improvements envisioned to improve the overall performances. Static stability testing has proved that the vehicle can withstand steep slopes scenarios. Some improvements have also been investigated to refine the estimation of the slippage that occurs during turning maneuvers and that is typical of skid-steering tracked vehicles. The software architecture has been implemented using the Robot Operating System (ROS) framework, so to exploit community available packages related to common and basic functions, such as sensor interfaces, while allowing dedicated custom implementation of the navigation algorithm developed. Real-world testing inside the university’s experimental orchards have proven the robustness and stability of the solution with more than 800 hours of fieldwork. The vehicle has also enabled a wide range of autonomous tasks such as spraying, mowing, and on-the-field data collection capabilities. The latter can be exploited to automatically estimate relevant orchard properties such as fruit counting and sizing, canopy properties estimation, and autonomous fruit harvesting with post-harvesting estimations.

Essays in political and gender economics

In the first chapter, “Political power and the influence of minorities: theory and evidence from Italy”, I analyze the relationship between minority and majority in politics, and how it can influence policy outcomes. I first present a theoretical model describing the possible consequences of an increase in a minority’s political power and show how it can increase difficulties in reaching a compromise on policy outcomes between parties. Furthermore, I empirically test these implications by exploiting the introduction in 2012 of a gender quota in Italian local elections: the increase in female politicians had heterogeneous effects on the level of funding for daycare, based on its differential effects on the share of women councillors. The second chapter, “Marriage patterns and the gender gap in labor force participation: evidence from Italy”, presents evidence highlighting a new possible determinant of the large gender gap in the Italian labor force: endogamy intensity. I argue that endogamy helps preserve social norms stigmatizing working women and reduces the probability of divorce, which disincentivizes women’s participation in the labor force. Endogamy is proxied by the degree of concentration of its surnames’ distribution, and I provide evidence that a more intense custom of endogamy contributed to enlarging gender participation gaps across Italian municipalities in 2001. The third chapter, “Information and quality of politicians: is transparency helping voters?”, studies how voting choices are affected by giving voters more personal information on candidates. I exploit the introduction of the “Spazzacorrotti” law in Italy in 2019, which imposed candidates at local elections to publish their CVs and criminal records before elections. I find no effects on elected candidates’ age, gender, educational level, or ideology. Moreover, I present anecdotal evidence that candidates with a criminal record received fewer votes on average, but only in the case of local media exposing it.

Identification of molecular biomarkers in esophageal adenocarcinoma

Esophageal adenocarcinoma (EAC) is a severe cancer that has been on the rise in Western nations over the past few decades. It has a high mortality rate and the 5-year survival rate is only 35%–45%. EAC has been included in a group of tumors with one of the highest rates of copy number alterations (CNAs), somatic structural rearrangements, high mutation frequency, with different mutational signatures, and with epigenetic mechanisms. The vast heterogeneity of EAC mutations makes it challenging to comprehend the biology that underlies tumor onset and development, identify prognostic biomarkers, and define a molecular classification to stratify patients. The only way to resolve the current disagreements is through an exhaustive molecular analysis of EAC. We examined the genetic profile of 164 patients' esophageal adenocarcinoma samples (without chemo-radiotherapy). The included patients did not receive neoadjuvant therapies, which can change the genetic and molecular composition of the tumor. Using next-generation sequencing technologies (NGS) at high coverage, we examined a custom panel of 26 cancer-related genes. Over the entire cohort, 337 variants were found, with the TP53 gene showing the most frequent alteration (67.27%). Poorer cancer-specific survival was associated with missense mutations in the TP53 gene (Log Rank P=0.0197). We discovered HNF1alpha gene disruptive mutations in 7 cases that were also affected by other gene changes. We started to investigate its role in EAC cell lines by silencing HNF1alpha to mimic our EAC cohort and we use Seahorse technique to analyze its role in the metabolism in esophageal cell. No significant changes were found in transfected cell lines. We conclude by finding that a particular class of TP53 mutations (missense changes) adversely impacted cancer-specific survival in EAC. HNF1alpha, a new EAC-mutated gene, was found, but more research is required to fully understand its function as a tumor suppressor gene.

Data-driven and data-oriented methods for materials science and technologies

The discovery of new materials and their functions has always been a fundamental component of technological progress. Nowadays, the quest for new materials is stronger than ever: sustainability, medicine, robotics and electronics are all key assets which depend on the ability to create specifically tailored materials. However, designing materials with desired properties is a difficult task, and the complexity of the discipline makes it difficult to identify general criteria. While scientists developed a set of best practices (often based on experience and expertise), this is still a trial-and-error process. This becomes even more complex when dealing with advanced functional materials. Their properties depend on structural and morphological features, which in turn depend on fabrication procedures and environment, and subtle alterations leads to dramatically different results. Because of this, materials modeling and design is one of the most prolific research fields. Many techniques and instruments are continuously developed to enable new possibilities, both in the experimental and computational realms. Scientists strive to enforce cutting-edge technologies in order to make progress. However, the field is strongly affected by unorganized file management, proliferation of custom data formats and storage procedures, both in experimental and computational research. Results are difficult to find, interpret and re-use, and a huge amount of time is spent interpreting and re-organizing data. This also strongly limit the application of data-driven and machine learning techniques. This work introduces possible solutions to the problems described above. Specifically, it talks about developing features for specific classes of advanced materials and use them to train machine learning models and accelerate computational predictions for molecular compounds; developing method for organizing non homogeneous materials data; automate the process of using devices simulations to train machine learning models; dealing with scattered experimental data and use them to discover new patterns.

Exploiting molecular simulations as an atomic-level microscope to elucidate clinically relevant cation chloride cotransporters

The cation chloride cotransporters (CCCs) represent a vital family of ion transporters, with several members implicated in significant neurological disorders. Specifically, conditions such as cerebrospinal fluid accumulation, epilepsy, Down’s syndrome, Asperger’s syndrome, and certain cancers have been attributed to various CCCs. This thesis delves into these pharmacological targets using advanced computational methodologies. I primarily employed GPU-accelerated all-atom molecular dynamics simulations, deep learning-based collective variables, enhanced sampling methods, and custom Python scripts for comprehensive simulation analyses. Our research predominantly centered on KCC1 and NKCC1 transporters. For KCC1, I examined its equilibrium dynamics in the presence/absence of an inhibitor and assessed the functional implications of different ion loading states. In contrast, our work on NKCC1 revealed its unique alternating access mechanism, termed the rocking-bundle mechanism. I identified a previously unobserved occluded state and demonstrated the transporter's potential for water permeability under specific conditions. Furthermore, I confirmed the actual water flow through its permeable states. In essence, this thesis leverages cutting-edge computational techniques to deepen our understanding of the CCCs, a family of ion transporters with profound clinical significance.