Silicon nanocrystals tailored for bioimaging and energy conversion

This thesis focus is the development of hybrid organic-inorganic systems based on Silicon Nanocrystals (SiNCs) with possible applications in the field of bioimaging and solar energy conversion. SiNCs were engineered thanks to the realization of a strong covalent Si-C bond on their surface, which allowed us to disperse them in different solvents with different final purpose. Chapter 1 introduces the basic properties of nanomaterials. Chapter 2 describes all the synthetic procedures to obtain the organic molecules-functionalized SiNCs. Chapter 3 illustrates an organic-inorganic antenna system based on SiNCs conjugated with diphenylanthracene (DPA) photoactive molecules, which was also embedded into Luminescent Solar Concentrators (LSC) made of a polymeric matrix. The optical and photovoltaic performances of this device were compared with the ones of a LSC embedded with a physical mixture made of SiNCs plus DPA at the same concentrations of the two components in the covalent system. Chapter 4 shows many different techniques to functionalize SiNCs with polyethylene glycol (PEG) chains in order to make them dispersible in water, for biomedical imaging applications. Chapter 5 presents the synthesis of dyes and/or SiNCs loaded Polymer Nanoparticles (PNPs) capable of excitation energy transfer (EET) mechanism. Chapter 6 is focused on the realization of photo-switchable systems based on azobenzene derivatives-functionalized SiNCs. These organic-inorganic hybrid materials were studied to possibly obtain a new light-driven response of SiNCs. In the end, chapter 7 reports the activity I followed in America, at The University of Texas at Austin, in the laboratory led by the professor Brian Korgel. Here I studied and compared the properties of high temperature hydrosilylated SiNCs and room temperature, radical promoted, hydrosilylated SiNCs.

Statistical methods for biomedical signal analysis and processing

Statistical modelling and statistical learning theory are two powerful analytical frameworks for analyzing signals and developing efficient processing and classification algorithms. In this thesis, these frameworks are applied for modelling and processing biomedical signals in two different contexts: ultrasound medical imaging systems and primate neural activity analysis and modelling. In the context of ultrasound medical imaging, two main applications are explored: deconvolution of signals measured from a ultrasonic transducer and automatic image segmentation and classification of prostate ultrasound scans. In the former application a stochastic model of the radio frequency signal measured from a ultrasonic transducer is derived. This model is then employed for developing in a statistical framework a regularized deconvolution procedure, for enhancing signal resolution. In the latter application, different statistical models are used to characterize images of prostate tissues, extracting different features. These features are then uses to segment the images in region of interests by means of an automatic procedure based on a statistical model of the extracted features. Finally, machine learning techniques are used for automatic classification of the different region of interests. In the context of neural activity signals, an example of bio-inspired dynamical network was developed to help in studies of motor-related processes in the brain of primate monkeys. The presented model aims to mimic the abstract functionality of a cell population in 7a parietal region of primate monkeys, during the execution of learned behavioural tasks.

Content-aware approach for improving biomedical image analysis: an interdisciplinary study series

Biomedicine is a highly interdisciplinary research area at the interface of sciences, anatomy, physiology, and medicine. In the last decade, biomedical studies have been greatly enhanced by the introduction of new technologies and techniques for automated quantitative imaging, thus considerably advancing the possibility to investigate biological phenomena through image analysis. However, the effectiveness of this interdisciplinary approach is bounded by the limited knowledge that a biologist and a computer scientist, by professional training, have of each other’s fields. The possible solution to make up for both these lacks lies in training biologists to make them interdisciplinary researchers able to develop dedicated image processing and analysis tools by exploiting a content-aware approach. The aim of this Thesis is to show the effectiveness of a content-aware approach to automated quantitative imaging, by its application to different biomedical studies, with the secondary desirable purpose of motivating researchers to invest in interdisciplinarity. Such content-aware approach has been applied firstly to the phenomization of tumour cell response to stress by confocal fluorescent imaging, and secondly, to the texture analysis of trabecular bone microarchitecture in micro-CT scans. Third, this approach served the characterization of new 3-D multicellular spheroids of human stem cells, and the investigation of the role of the Nogo-A protein in tooth innervation. Finally, the content-aware approach also prompted to the development of two novel methods for local image analysis and colocalization quantification. In conclusion, the content-aware approach has proved its benefit through building new approaches that have improved the quality of image analysis, strengthening the statistical significance to allow unveiling biological phenomena. Hopefully, this Thesis will contribute to inspire researchers to striving hard for pursuing interdisciplinarity.

Development of effect-based bio-chemiluminescent cell bioassays for nutraceutical and biomedical applications

The final goal of the bioassay developed during the first two years of my Ph.D. was its application for the screening of antioxidant activity of nutraceuticals and for monitoring the intracellular H2O2 production in peripheral blood mononuclear cells (PBMCs) from hypercholesterolemic subjects before and after two months treatment with Evolocumab, a new generation LDL-cholesterol lowering drug. Moreover, a recombinant bioluminescent protein was developed during the last year using the Baculovirus expression system in insect cells. In particular, the protein combines the extracellular domain (ECD) of the Notch high affinity mutated form of one of the selective Notch ligands defined as Jagged 1 (Jag1) with a red emitting firefly luciferase since a pivotal role of “aberrant” Notch signaling activation in colorectal cancer (CRC) was reported. The probe was validated and characterized in terms of analytical performance and through imaging experiments, in order to understand if Jagged1-FLuc binding correlates with a Notch signaling overexpression and activation in CRC progression.