Conjugation of Toll Like Receptor 7 agonist through conjugation to immunogenic proteins. Synthesis, characterization, formulation and pre-clinical evaluation

The next generation of vaccine adjuvant are represented by a wide ranging set of molecules called Toll like agonists (TLR’s). Although many of these molecules are complex structures extracted from microorganisms, small molecule TLR agonists have also been identified. However, delivery systems have not been optimized to allow their effective delivery in conjunction with antigens. Here we describe a novel approach in which a small molecule TLR agonist has been conjugated directly to antigens to ensure effective co delivery. We describe the conjugation of a relevant protein, a recombinant protective antigen from S.pneumoniae (RrgB), which is linked to a TLR7 agonist. Following thorough characterization to ensure there was no aggregation, the conjugate was evaluated in a murine infection model. Results showed that the conjugate extended animals’ survival after lethal challenge with S.pneumoniae. Comparable results were obtained with a 10 fold lower dose than that of the native unconjugated antigen. Notably, the animals immunized with the same dose of unconjugated TLR7 agonist and antigen showed no adjuvant effect. The increased immunogenicity was likely a consequence of the co-localization of TLR7 agonist and antigen by chemical binding and is was more effective than simple co-administration. Likely, this approach can be adopted to reduce the dose of antigen required to induce protective immunity, and potentially increase the safety of a broad variety of vaccine candidates

Multilevel Domain Decomposition Algorithms for Monolithic Fluid-Structure Interaction Problems with Application to Haemodynamics

Finite element techniques for solving the problem of fluid-structure interaction of an elastic solid material in a laminar incompressible viscous flow are described. The mathematical problem consists of the Navier-Stokes equations in the Arbitrary Lagrangian-Eulerian formulation coupled with a non-linear structure model, considering the problem as one continuum. The coupling between the structure and the fluid is enforced inside a monolithic framework which computes simultaneously for the fluid and the structure unknowns within a unique solver. We used the well-known Crouzeix-Raviart finite element pair for discretization in space and the method of lines for discretization in time. A stability result using the Backward-Euler time-stepping scheme for both fluid and solid part and the finite element method for the space discretization has been proved. The resulting linear system has been solved by multilevel domain decomposition techniques. Our strategy is to solve several local subproblems over subdomain patches using the Schur-complement or GMRES smoother within a multigrid iterative solver. For validation and evaluation of the accuracy of the proposed methodology, we present corresponding results for a set of two FSI benchmark configurations which describe the self-induced elastic deformation of a beam attached to a cylinder in a laminar channel flow, allowing stationary as well as periodically oscillating deformations, and for a benchmark proposed by COMSOL multiphysics where a narrow vertical structure attached to the bottom wall of a channel bends under the force due to both viscous drag and pressure. Then, as an example of fluid-structure interaction in biomedical problems, we considered the academic numerical test which consists in simulating the pressure wave propagation through a straight compliant vessel. All the tests show the applicability and the numerical efficiency of our approach to both two-dimensional and three-dimensional problems.

Silk Fibroin: a biopolymer platform for innovative pharmaceutical formulation and biomedical devices.

The protein silk fibroin (SF) from the silkworm Bombyx mori is a FDA-approved biomaterial used over centuries as sutures wire. Importantly, several evidences highlighted the potential of silk biomaterials obtained by using so-called regenerated silk fibroin (RSF) in biomedicine, tissue engineering and drug delivery. Indeed, by a water-based protocol, it is possible to obtain protein water-solution, by extraction and purification of fibroin from silk fibres. Notably, RSF can be processed in a variety of biomaterials forms used in biomedical and technological fields, displaying remarkable properties such as biocompatibility, controllable biodegradability, optical transparency, mechanical robustness. Moreover, RSF biomaterials can be doped and/or chemical functionalized with drugs, optically active molecules, growth factors and/or chemicals In this view, activities of my PhD research program were focused to standardize the process of extraction and purification of protein to get the best physical and chemical characteristics. The analysis of the chemo-physical properties of the fibroin involved both the RSF water-solution and the protein processed in film. Chemo-physical properties have been studied through: vibrational (FT-IR and Raman-FT) and optical (absorption and emission UV-VIS) spectroscopy, nuclear magnetic resonance (1H and 13C NMR), thermal analysis and thermo-gravimetric scan (DSC and TGA). In the last year of my PhD, activities were focused to study and define innovative methods of functionalization of the silk fibroin solution and films. Indeed, research program was the application of different methods of manufacturing approaches of the films of fibroin without the use of harsh treatments and organic solvents. New approaches to doping and chemical functionalization of the silk fibroin were studied. Two different methods have been identified: 1) biodoping that consists in the doping of fibroin with optically active molecules through the addition of fluorescent molecules in the standard diet used for the breeding of silkworms; 2) chemical functionalization via silylation.

Valorization of by-products from olive oil industry and their utilization for innovative food formulation

The purpose of this thesis work was the valorization of the main by-products obtained from olive oil production chain (wastewater and pomace) and their utilization in innovative food formulation. In the first part of the thesis, an olive mill wastewater extract rich in phenols were used in the formulation of 3 innovative meat products: beef hamburgers, cooked ham and würstels. These studies confirms that olive mill wastewaters extract rich in phenols could be an alternative for the reduction/total replacement of additives (i.e., nitrites) in ground and cooked meat preparations, which would promote the formulation of healthier clean label products and improve the sustainability of the olive oil industry with a circular economy approach, by further valorizing this olive by-product. In the second part of the thesis, the lipid composition and oxidative stability of a spreadable product obtained from a fermented and biologically de-bittered olive pomace, was assessed during a shelf-life study. This study confirmed that olive pomace represents an excellent ingredient for the formulation of functional foods In the third and last part of the thesis, carried out at the Universidad de Navarra (Pamplona, Spain), during a period abroad (3 months), three extracts obtained from purification of olive mill wastewaters, were subjected to in-vitro digestion and characterized. From the analysis of the three phenolic extracts, it emerged that the most promising extract to be used in the food field is the spry-dried one. Thanks to its formulation containing maltodextrins it manages to maintain its antioxidant capacity even after being underwent to in-vitro digestion. This thesis work is a part of the PRIN 2015 project (PROT: 20152LFKAT) "Olive phenols as multifunctional bioactives for healthier food: evaluation of simplified formulation to obtain safe meat products and new foods with higher functionality", coordinated by University of Perugia.

Data driven regularization models of non-linear ill-posed inverse problems in imaging

Imaging technologies are widely used in application fields such as natural sciences, engineering, medicine, and life sciences. A broad class of imaging problems reduces to solve ill-posed inverse problems (IPs). Traditional strategies to solve these ill-posed IPs rely on variational regularization methods, which are based on minimization of suitable energies, and make use of knowledge about the image formation model (forward operator) and prior knowledge on the solution, but lack in incorporating knowledge directly from data. On the other hand, the more recent learned approaches can easily learn the intricate statistics of images depending on a large set of data, but do not have a systematic method for incorporating prior knowledge about the image formation model. The main purpose of this thesis is to discuss data-driven image reconstruction methods which combine the benefits of these two different reconstruction strategies for the solution of highly nonlinear ill-posed inverse problems. Mathematical formulation and numerical approaches for image IPs, including linear as well as strongly nonlinear problems are described. More specifically we address the Electrical impedance Tomography (EIT) reconstruction problem by unrolling the regularized Gauss-Newton method and integrating the regularization learned by a data-adaptive neural network. Furthermore we investigate the solution of non-linear ill-posed IPs introducing a deep-PnP framework that integrates the graph convolutional denoiser into the proximal Gauss-Newton method with a practical application to the EIT, a recently introduced promising imaging technique. Efficient algorithms are then applied to the solution of the limited electrods problem in EIT, combining compressive sensing techniques and deep learning strategies. Finally, a transformer-based neural network architecture is adapted to restore the noisy solution of the Computed Tomography problem recovered using the filtered back-projection method.