Validation of a new method for human nerve decellularization: toward a new tool in peripheral nerve reconstructive surgery.

Defects of the peripheral nervous system are extremely frequent in trauma and surgeries and have high socioeconomic costs. In case of peripheral nerve injury, the first approach is primary neurorrhaphy, which is direct nerve repair with epineural microsutures of the two stumps. However, this is not feasible in case of stump retraction or in case of tissue loss (gap > 2 cm), where the main surgical options are autologous grafts, allogenic grafts, or nerve conduits. While the gold standard is the autograft, it has disadvantages related to its harvesting, with an inevitable donor site morbidity and functional deficit. Fresh nerve allografts have therefore become a viable alternative option, but they require immunosuppression, which is often contraindicated. Acellular Nerve Allografts (ANA) represent a valid alternative, they do not need immunosuppression and appear to be safe and effective based on recent studies. The purpose of this study is to propose and develop an innovative method of nerve decellularization (Rizzoli method), conforming to cleanroom requirements in order to perform the direct tissue manipulation step and the nerve decellularization process within five hours, so as to accelerate the detachment of myelin and cellular debris, without detrimental effects on nerve architecture. In this study, the safety and the efficacy of the new method are evaluated in vitro and in vivo by histological, immunohistochemical, and histomorphometric studies in rabbits and humans. The new method is rapid, safe, and cheaper if compared with available commercial ANAs. The present study shows that the method, previously optimized in vitro and in vivo on animal model presented by our group, can be applied on human nerve samples. This work represents the first step in providing a novel, safe, and inexpensive tool for use by European tissue banks to democratize the use of nerve tissue transplantation for nerve injury reconstruction.

Silence after bombs. Practice, role and training of interpreters in conflict zones. A case study of the wars in Bosnia and Herzegovina and Croatia.

This dissertation investigates the role, training and practice of the interpreters that worked during the wars in Bosnia and Herzegovina and Croatia in the 1990s, both at a high political level and on the ground for peackeeping troops. Adopting a historical method that uses interviews, newspaper articles, videos, archival documents and pictures the author tries to retrace how those interpreters were hired, employed and what challenges they faced in their daily work. The aim is to give voice to a category that has long been forgotten, to investigate how mediated interaction is shaped by violent conflict and to offer hindsight to improve the recruitment and management of local interpreters by armed forces.

Numerical modelling of rock masses interacting with dams: the case of Ridracoli

A three-dimensional Direct Finite Element procedure is here presented which takes into account most of the factors affecting the interaction problem of the dam-water-foundation system, whilst keeping the computational cost at a reasonable level by introducing some simplified hypotheses. A truncated domain is defined, and the dynamic behaviour of the system is treated as a wave-scattering problem where the presence of the dam perturbs an original free-field system. The rock foundation truncated boundaries are enclosed by a set of free-field one-dimensional and two-dimensional systems which transmit the effective forces to the main model and apply adsorbing viscous boundaries to ensure radiation damping. The water domain is treated as an added mass moving with the dam. A strategy is proposed to keep the viscous dampers at the boundaries unloaded during the initial phases of analysis, when the static loads are initialised, and thus avoid spurious displacements. A focus is given to the nonlinear behaviour of the rock foundation, with concentrated plasticity along the natural discontinuities of the rock mass, immersed in an otherwise linear elastic medium with Rayleigh damping. The entire procedure is implemented in the commercial software Abaqus®, whose base code is enriched with specific user subroutines when needed. All the extra coding is attached to the Thesis and tested against analytical results and simple examples. Possible rock wedge instabilities induced by intense ground motion, which are not easily investigated within a comprehensive model of the dam-water-foundation system, are treated separately with a simplified decoupled dynamic approach derived from the classical Newmark method, integrated with FE calculation of dam thrust on the wedges during the earthquake. Both the described approaches are applied to the case study of the Ridracoli arch-gravity dam (Italy) in order to investigate its seismic response to the Maximum Credible Earthquake (MCE) in a full reservoir condition.

Supervised and weakly supervised counting-by-segmentation: the fluorescent microscopy use case

This thesis focuses on automating the time-consuming task of manually counting activated neurons in fluorescent microscopy images, which is used to study the mechanisms underlying torpor. The traditional method of manual annotation can introduce bias and delay the outcome of experiments, so the author investigates a deep-learning-based procedure to automatize this task. The author explores two of the main convolutional-neural-network (CNNs) state-of-the-art architectures: UNet and ResUnet family model, and uses a counting-by-segmentation strategy to provide a justification of the objects considered during the counting process. The author also explores a weakly-supervised learning strategy that exploits only dot annotations. The author quantifies the advantages in terms of data reduction and counting performance boost obtainable with a transfer-learning approach and, specifically, a fine-tuning procedure. The author released the dataset used for the supervised use case and all the pre-training models, and designed a web application to share both the counting process pipeline developed in this work and the models pre-trained on the dataset analyzed in this work.