Design methods and geometric modelling of scaffolds for bone tissue engineering

Every year, thousand of surgical treatments are performed in order to fix up or completely substitute, where possible, organs or tissues affected by degenerative diseases. Patients with these kind of illnesses stay long times waiting for a donor that could replace, in a short time, the damaged organ or the tissue. The lack of biological alternates, related to conventional surgical treatments as autografts, allografts, e xenografts, led the researchers belonging to different areas to collaborate to find out innovative solutions. This research brought to a new discipline able to merge molecular biology, biomaterial, engineering, biomechanics and, recently, design and architecture knowledges. This discipline is named Tissue Engineering (TE) and it represents a step forward towards the substitutive or regenerative medicine. One of the major challenge of the TE is to design and develop, using a biomimetic approach, an artificial 3D anatomy scaffold, suitable for cells adhesion that are able to proliferate and differentiate themselves as consequence of the biological and biophysical stimulus offered by the specific tissue to be replaced. Nowadays, powerful instruments allow to perform analysis day by day more accurateand defined on patients that need more precise diagnosis and treatments.Starting from patient specific information provided by TC (Computed Tomography) microCT and MRI(Magnetic Resonance Imaging), an image-based approach can be performed in order to reconstruct the site to be replaced. With the aid of the recent Additive Manufacturing techniques that allow to print tridimensional objects with sub millimetric precision, it is now possible to practice an almost complete control of the parametrical characteristics of the scaffold: this is the way to achieve a correct cellular regeneration. In this work, we focalize the attention on a branch of TE known as Bone TE, whose the bone is main subject. Bone TE combines osteoconductive and morphological aspects of the scaffold, whose main properties are pore diameter, structure porosity and interconnectivity. The realization of the ideal values of these parameters represents the main goal of this work: here we'll a create simple and interactive biomimetic design process based on 3D CAD modeling and generative algorithmsthat provide a way to control the main properties and to create a structure morphologically similar to the cancellous bone. Two different typologies of scaffold will be compared: the first is based on Triply Periodic MinimalSurface (T.P.M.S.) whose basic crystalline geometries are nowadays used for Bone TE scaffolding; the second is based on using Voronoi's diagrams and they are more often used in the design of decorations and jewellery for their capacity to decompose and tasselate a volumetric space using an heterogeneous spatial distribution (often frequent in nature). In this work, we will show how to manipulate the main properties (pore diameter, structure porosity and interconnectivity) of the design TE oriented scaffolding using the implementation of generative algorithms: "bringing back the nature to the nature".

Progettazione di una protesi personalizzata del muscolo temporale volta a ridurre il rischio di complicazioni post-operatorie

Il progetto di questa tesi si propone di realizzare una protesi sostitutiva del muscolo temporale la cui funzione principale è colmare il vuoto lasciato dal muscolo dopo la sua rimozione. A seguito di un’analisi sulla forma, sul materiale e le funzionalità della protesi attualmente utilizzata, vengono evidenziati i benefici che devono essere inclusi nel progetto e le debolezze a cui porre maggior attenzione e valutare possibili soluzioni. La protesi deve presentare una superficie esterna priva di discontinuità che potrebbero essere percepite al tatto in post operazione e una dimensione conforme al muscolo rimosso. Il progetto si propone di fissare alcuni punti chiave a cui dare risposta, una su tutte la tipologia di materiale utilizzato per garantire una buona integrazione dell’impianto con l’organismo umano. Il materiale utilizzato deve essere innanzitutto biocompatibile e viene valutato per la sua integrazione con l’organismo, la capacità di proteggere la parete laterale del cranio e per la sua consistenza il più possibile paragonabile al muscolo rimosso. È prioritario in questa tipologia di protesi evitare il ristagno di sangue tra l’intercapedine della protesi e la parete laterale del cranio, a tal fine è bene analizzare lo spessore del muscolo segmentato per delineare la soluzione migliore che possa rispondere a tale necessità. È importante verificare il bordo della protesi, è necessario che sia ben raccordato con la superficie esterna del cranio così da evitare uno scalino a seguito dell’operazione e ottenere un alto grado di soddisfazione del paziente. In questo contesto di protesi il grado di soddisfazione del paziente è importante. Il lato su cui viene impiantata la protesi deve risultare simmetrico al lato opposto. La protesi progettata è di tipo paziente-specifico, l’obbiettivo principale è riempire la cavità temporale ed evitare possibili complicazioni nel tempo in quanto la durata della protesi è correlata alla durata di vita del paziente.

Bio-QA-GNN: Reasoning with Language Models and Knowledge Graphs for Interpretable Biomedical Question Answering

Nowadays the idea of injecting world or domain-specific structured knowledge into pre-trained language models (PLMs) is becoming an increasingly popular approach for solving problems such as biases, hallucinations, huge architectural sizes, and explainability lack—critical for real-world natural language processing applications in sensitive fields like bioinformatics. One recent work that has garnered much attention in Neuro-symbolic AI is QA-GNN, an end-to-end model for multiple-choice open-domain question answering (MCOQA) tasks via interpretable text-graph reasoning. Unlike previous publications, QA-GNN mutually informs PLMs and graph neural networks (GNNs) on top of relevant facts retrieved from knowledge graphs (KGs). However, taking a more holistic view, existing PLM+KG contributions mainly consider commonsense benchmarks and ignore or shallowly analyze performances on biomedical datasets. This thesis start from a propose of a deep investigation of QA-GNN for biomedicine, comparing existing or brand-new PLMs, KGs, edge-aware GNNs, preprocessing techniques, and initialization strategies. By combining the insights emerged in DISI's research, we introduce Bio-QA-GNN that include a KG. Working with this part has led to an improvement in state-of-the-art of MCOQA model on biomedical/clinical text, largely outperforming the original one (+3.63\% accuracy on MedQA). Our findings also contribute to a better understanding of the explanation degree allowed by joint text-graph reasoning architectures and their effectiveness on different medical subjects and reasoning types. Codes, models, datasets, and demos to reproduce the results are freely available at: \url{https://github.com/disi-unibo-nlp/bio-qagnn}.