Toxicological effects related to a novel heated tobacco product

The tobacco epidemic is a public health burden. Nicotine-Delivery-Systems(NDS) are devices designed to help people replace conventional cigarette(CC) and among these devices we find electronic cigarettes(e-cig), which are classified as Electronic-NDS(ENDS). E-cigs use different technologies to vaporize a liquid or to heat the tobacco avoiding the combustion phenomenon(IQOS). The US Food and Drug Administration(FDA) has labelled IQOS as modified risk tobacco products(MRTPs), indirectly encouraging the perception of safety in the consumers, but IQOS smoke, although to a lesser extent than conventional, still presents a great deal of harmful or potentially harmful compounds. My PhD thesis aims to study the toxic effects related to IQOS exposure. I sought to answer the question of whether the toxic compounds released by IQOS, albeit in reduced concentrations, could lead to genotoxicity and damage to the airways and liver in vivo. At the University of Nottingham, I have investigated in vitro the effects generated by the IQOS, e-cigs and CC exposure on PBMCs and human lung epithelial cell line. Finally, at University of Milano–Bicocca, I have developed a in vivo Positron Emission computed Tomography(PET) imaging procedure meant to be applied to the monitoring of ENDS toxicity, particularly in the brain. These results indicate that IQOS is not a low-risk product in vivo, for primary target organs but also for secondary organs, although we have observed a small impact in vitro. Labelling as MRTP may mislead consumers who interpret “a lower level of toxic compounds” as an indication of “harmlessness” when there is a health risk for users. In the last part, I set up a methodology for studying temporal fluctuations of regional brain metabolism and connectivity derived from mice of different ages allowing researchers to obtain normative values in investigations of the efficacy or toxicity of substances at the functional level of the CNS.

Fluorogenic hyaluronan-based probe: characterization and use in advanced microscopy

Among all, the application of nanomaterials in biomedical research and most recently in the environmental one has opened the fields of nanomedicine and nanoremediation. Sensing methods based on fluorescence optical probe are generally requested for their selectivity, sensitivity. However, most imaging methods in literature rely on a fluorescent covalent labelling of the system. Therefore, the main aim of this project was to synthetise a biocompatible fluorogenic hyaluronan probe (HA) polymer functionalised with a rhomadine B (RB) moieties and study its behaviour as an optical probe with different materials with microscopy techniques. A derivatization of HA with RB (HA-RB) was successfully obtained providing a photophysical characterization showing a particular fluorescence mechanism of the probe. Firstly, we tested the interaction with different lab-grade micro and nanoplastics in water. Thanks to the peculiar photophysical behaviour of the probe nanoplastics can be detected with confocal microscopy and more interestingly their nature can be discriminated based on the fluorescence lifetime decay with FLIM microscopy. After, the interaction of a model plant derived metabolic enzyme GAPC1 undergoing oxidative-triggered aggregation was explored with the HA-RB. We highlighted the probe interaction with the protein even at early stage of the kinetic. Moreover, nanoparticle tracking analysis (NTA) experiment demonstrates that the probe is in fact able to interact with the small pre-aggregates in the early stage of the aggregation kinetic. Ultimately, we focused on the possibility to apply the probe in a super resolution microscopy technique, PALM, exploiting its aspecific interaction to characterize the surface topography of PTFE polydisperse microplastics. Optimal conditions were reached at high concentration of the probe (70 nM) where 0.5-5 nM is always advisable for this technique. Thanks to the polymeric nature and fluorescence mechanism of the probe, this technique was able to reveal features of PTFE surface under the diffraction limit (< 250 nm).

Application of active and transfer learning to wound image segmentation

Wound management is a fundamental task in standard clinical practice. Automated solutions already exist for humans, but there is a lack of applications on wound management for pets. The importance of a precise and efficient wound assessment is helpful to improve diagnosis and to increase the effectiveness of treatment plans for the chronic wounds. The goal of the research was to propose an automated pipeline capable of segmenting natural light-reflected wound images of animals. Two datasets composed by light-reflected images were used in this work: Deepskin dataset, 1564 human wound images obtained during routine dermatological exams, with 145 manual annotated images; Petwound dataset, a set of 290 wound photos of dogs and cats with 0 annotated images. Two implementations of U-Net Convolutioal Neural Network model were proposed for the automated segmentation. Active Semi-Supervised Learning techniques were applied for human-wound images to perform segmentation from 10% of annotated images. Then the same models were trained, via Transfer Learning, adopting an Active Semi- upervised Learning to unlabelled animal-wound images. The combination of the two training strategies proved their effectiveness in generating large amounts of annotated samples (94% of Deepskin, 80% of PetWound) with the minimal human intervention. The correctness of automated segmentation were evaluated by clinical experts at each round of training thus we can assert that the results obtained in this thesis stands as a reliable solution to perform a correct wound image segmentation. The use of Transfer Learning and Active Semi-Supervied Learning allows to minimize labelling effort from clinicians, even requiring no starting manual annotation at all. Moreover the performances of the model with limited number of parameters suggest the implementation of smartphone-based application to this topic, helping the future standardization of light-reflected images as acknowledge medical images.