Interspecies study of the enteric nervous system and related pathologies

The enteric nervous system (ENS) modulates a number of digestive functions including well known ones, i.e. motility, secretion, absorption and blood flow, along with other critically relevant processes, i.e. immune responses of the gastrointestinal (GI) tract, gut microbiota and epithelial barrier . The characterization of the anatomical aspects of the ENS in large mammals and the identification of differences and similarities existing between species may represent a fundamental basis to decipher several digestive GI diseases in humans and animals. In this perspective, the aim of the present thesis is to highlight the ENS anatomical basis and pathological aspects in different mammalian species, such as horses, dogs and humans. Firstly, I designed two anatomical studies in horses:  “Excitatory and inhibitory enteric innervation of horse lower esophageal sphincter”.  “Localization of 5-hydroxytryptamine 4 receptor (5-HT4R) in the equine enteric nervous system”. Then I focused on the enteric dysfunctions, including:  A primary enteric aganglionosis in horses: “Extrinsic innervation of the ileum and pelvic flexure of foals with ileocolonic aganglionosis”.  A diabetic enteric neuropathy in dogs: “Quantification of nitrergic neurons in the myenteric plexus of gastric antrum and ileum of healthy and diabetic dogs”.  An enteric neuropathy in human neurological patients: “Functional and neurochemical abnormalities in patients with Parkinson's disease and chronic constipation”. The physiology of the GI tract is characterized by a high complexity and it is mainly dependent on the control of the intrinsic nervous system. ENS is critical to preserve body homeostasis as reflect by its derangement occurring in pathological conditions that can be lethal or seriously disabling to humans and animals. The knowledge of the anatomy and the pathology of the ENS represents a new important and fascinating topic, which deserves more attention in the veterinary medicine field.

Assessment of gait spatio-temporal parameters in neurological disorders using wearable inertial sensors

Movement analysis carried out in laboratory settings is a powerful, but costly solution since it requires dedicated instrumentation, space and personnel. Recently, new technologies such as the magnetic and inertial measurement units (MIMU) are becoming widely accepted as tools for the assessment of human motion in clinical and research settings. They are relatively easy-to-use and potentially suitable for estimating gait kinematic features, including spatio-temporal parameters. The objective of this thesis regards the development and testing in clinical contexts of robust MIMUs based methods for assessing gait spatio-temporal parameters applicable across a number of different pathological gait patterns. First, considering the need of a solution the least obtrusive as possible, the validity of the single unit based approach was explored. A comparative evaluation of the performance of various methods reported in the literature for estimating gait temporal parameters using a single unit attached to the trunk first in normal gait and then in different pathological gait conditions was performed. Then, the second part of the research headed towards the development of new methods for estimating gait spatio-temporal parameters using shank worn MIMUs on different pathological subjects groups. In addition to the conventional gait parameters, new methods for estimating the changes of the direction of progression were explored. Finally, a new hardware solution and relevant methodology for estimating inter-feet distance during walking was proposed. Results of the technical validation of the proposed methods at different walking speeds and along different paths against a gold standard were reported and showed that the use of two MIMUs attached to the lower limbs associated with a robust method guarantee a much higher accuracy in determining gait spatio-temporal parameters. In conclusion, the proposed methods could be reliably applied to various abnormal gaits obtaining in some cases a comparable level of accuracy with respect to normal gait.

The frozen elephant trunk technique for the treatment of complex aortic arch and descending thoracic aorta pathologies: long term outcomes and distal reintervention

Background: The frozen elephant trunk(FET) technique is one of the last evolution in the treatment of complex pathologies of the aortic arch and the descending thoracic aorta.Materials and methods: Between January 2007 and March 2021, a total of 396 patients underwent total aortic arch replacements with the FET technique in our centre.The main indications were thoracic aortic aneurysm(n=104,28.2%), chronic aortic dissection(n=224,53.4%) and acute aortic dissection(n=68, 18.4%). We divided the population in two groups according the position of the distal anastomosis (zone 2 vs zone 3) and the length of the stent graft (< 150 mm vs > 150 mm): conservative group (Zone 2 anastomosis + stent length < 150mm, n. 140 pts) and aggressive group (zone 3 anastomosis + stent length > 150mm, n. 141). Results: The overall 30-day mortality rate was 13%(48/369); the risk factor analysis showed that an aggressive approach was neither a risk factor for major complication (permanent dialysis, tracheostomy, bowel malperfusion and permanent paraplegia) neither for 30-day mortality. The survival rate at 1, 5,10 and 15 years was 87.7%,75%,61.3% and 58.4% respectively. During the follow up, an aortic reintervention was performed in 122 patients (38%), 5 patients received a non-aortic cardiac surgery. Freedom from aortic reintervention at 1-,5- and 10-year was 77%,54% and 44% respectively. The freedom from aortic reintervention was higher in the ‘aggressive’ group (62.5%vs40.0% at 5 years, log-rank=0.056). An aggressive approach was not protective for aortic reintervention at follow up and for death at follow up. Conclusions: The FET technique represents a feasible and efficient option in the treatment of complex thoracic aortic pathologies. An aortic reintervention after FET is very common and the decision-making approach should consider and balance the higher risk of an aggressive approach in terms of post-operative complication versus the higher risk of a second aortic reintervention at follow-up.

Skin as a promising biomatrix for the early detection of misfolded proteins by RT-QuIC in neurodegenerative diseases

Real-Time Quaking-Induced Conversion (RT-QuIC) is an ultrasensitive assay capable of detecting pathological aggregates of misfolded proteins in biospecimens. In recent years, efforts have been made to find a more feasible and convenient biomatrix as an alternative to CSF, and skin biopsy may be a suitable candidate. This project aimed to evaluate the diagnostic performance of skin RT-QuIC in 3 different cohorts of patients: 1. Creutzfeldt-Jakob disease (CJD), 2. Lewy body disease (LBD), and 3. Isolated REM sleep behavior disorder (iRBD). We studied 71 punch skin samples of 35 patients with CJD, including five assessed in vitam, using 2 two different substrates: Bank vole 23-230 (Bv23-230) and Syrian hamster 23-231 (Ha23-231) recombinant prion protein. Skin prion RT-QuIC showed a 100% specificity with both substrates and a higher sensitivity with the Bv23-230 than Ha23-231 (87.5% vs. 65.6%, respectively). Forty-one patients underwent both lumbar puncture (LB) and skin biopsy; CSF and skin RT-QuIC showed a high level of concordance (38/41, 92.7%). Then, we analyzed samples taken in vitam (n=69) or postmortem (n=49) from patients with Parkinson’s disease (PD), dementia with Lewy bodies (DLB), incidental Lewy body pathology, and neurological controls. Skin α-syn RT-QuIC distinguished LBD patients with an overall accuracy of 94.1% in the two cohorts (sensitivity, 89.2%; specificity, 96.3%). Seventy-nine patients underwent both CSF and skin α-syn RT-QuIC, and the two assays yielded similar diagnostic accuracy (skin, 97.5%; CSF, 98.7%). Finally, we studied 91 iRBD patients and 41 control. In the skin, RT-QuIC showed a sensitivity of 76.9%, specificity of 97.6%, and 82.0% accuracy. 128 participants (88 patients plus 40 controls) underwent both CSF and skin RT-QuIC. The two protocols showed 99.2% of concordance. These works confirmed that skin punch biopsies might represent a valid and convenient alternative to CSF analysis for an early diagnosis of prion diseases and LB-related pathologies.

Network analysis and machine learning assist drug repurposing and safety assessment in neurological diseases

In recent decades, two prominent trends have influenced the data modeling field, namely network analysis and machine learning. This thesis explores the practical applications of these techniques within the domain of drug research, unveiling their multifaceted potential for advancing our comprehension of complex biological systems. The research undertaken during this PhD program is situated at the intersection of network theory, computational methods, and drug research. Across six projects presented herein, there is a gradual increase in model complexity. These projects traverse a diverse range of topics, with a specific emphasis on drug repurposing and safety in the context of neurological diseases. The aim of these projects is to leverage existing biomedical knowledge to develop innovative approaches that bolster drug research. The investigations have produced practical solutions, not only providing insights into the intricacies of biological systems, but also allowing the creation of valuable tools for their analysis. In short, the achievements are: • A novel computational algorithm to identify adverse events specific to fixed-dose drug combinations. • A web application that tracks the clinical drug research response to SARS-CoV-2. • A Python package for differential gene expression analysis and the identification of key regulatory "switch genes". • The identification of pivotal events causing drug-induced impulse control disorders linked to specific medications. • An automated pipeline for discovering potential drug repurposing opportunities. • The creation of a comprehensive knowledge graph and development of a graph machine learning model for predictions. Collectively, these projects illustrate diverse applications of data science and network-based methodologies, highlighting the profound impact they can have in supporting drug research activities.