Nanomechanical characterization of soft bioelectronic interfaces via modeling-informed atomic force microscopy

The field of bioelectronics involves the use of electrodes to exchange electrical signals with biological systems for diagnostic and therapeutic purposes in biomedical devices and healthcare applications. However, the mechanical compatibility of implantable devices with the human body has been a challenge, particularly with long-term implantation into target organs. Current rigid bioelectronics can trigger inflammatory responses and cause unstable device functions due to the mechanical mismatch with the surrounding soft tissue. Recent advances in flexible and stretchable electronics have shown promise in making bioelectronic interfaces more biocompatible. To fully achieve this goal, material science and engineering of soft electronic devices must be combined with quantitative characterization and modeling tools to understand the mechanical issues at the interface between electronic technology and biological tissue. Local mechanical characterization is crucial to understand the activation of failure mechanisms and optimizing the devices. Experimental techniques for testing mechanical properties at the nanoscale are emerging, and the Atomic Force Microscope (AFM) is a good candidate for in situ local mechanical characterization of soft bioelectronic interfaces. In this work, in situ experimental techniques with solely AFM supported by interpretive models for the characterization of planar and three-dimensional devices suitable for in vivo and in vitro biomedical experimentations are reported. The combination of the proposed models and experimental techniques provides access to the local mechanical properties of soft bioelectronic interfaces. The study investigates the nanomechanics of hard thin gold films on soft polymeric substrates (Poly(dimethylsiloxane) PDMS) and 3D inkjet-printed micropillars under different deformation states. The proposed characterization methods provide a rapid and precise determination of mechanical properties, thus giving the possibility to parametrize the microfabrication steps and investigate their impact on the final device.

Heterosis in maize (Zea mays, L.): characterization of heterotic quantitative trait loci (QTL) for agronomic traits in near isogenic lines (NILs) and their testcrosses

In a previous study on maize (Zea mays, L.) several quantitative trait loci (QTL) showing high dominance-additive ratio for agronomic traits were identified in a population of recombinant inbred lines derived from B73 × H99. For four of these mapped QTL, namely 3.05, 4.10, 7.03 and 10.03 according to their chromosome and bin position, families of near-isogenic lines (NILs) were developed, i.e., couples of homozygous lines nearly identical except for the QTL region that is homozygote either for the allele provided by B73 or by H99. For two of these QTL (3.05 and 4.10) the NILs families were produced in two different genetic backgrounds. The present research was conducted in order to: (i) characterize these QTL by estimating additive and dominance effects; (ii) investigate if these effects can be affected by genetic background, inbreeding level and environmental growing conditions (low vs. high plant density). The six NILs’ families were tested across three years and in three Experiments at different inbreeding levels as NILs per se and their reciprocal crosses (Experiment 1), NILs crossed to related inbreds B73 and H99 (Experiment 2) and NILs crossed to four unrelated inbreds (Experiment 3). Experiment 2 was conducted at two plant densities (4.5 and 9.0 plants m-2). Results of Experiments 1 and 2 confirmed previous findings as to QTL effects, with dominance-additive ratio superior to 1 for several traits, especially for grain yield per plant and its component traits; as a tendency, dominance effects were more pronounced in Experiment 1. The QTL effects were also confirmed in Experiment 3. The interactions involving QTL effects, families and plant density were generally negligible, suggesting a certain stability of the QTL. Results emphasize the importance of dominance effects for these QTL, suggesting that they might deserve further studies, using NILs’ families and their crosses as base materials.

Semi-synthetic bile acids as novel drug candidate in liver diseases: physico-chemical characterization and HPLC-ES-MS/MS methods for their quali-quantitative analysis in different experimental animal models

The physico-chemical characterization, structure-pharmacokinetic and metabolism studies of new semi synthetic analogues of natural bile acids (BAs) drug candidates have been performed. Recent studies discovered a role of BAs as agonists of FXR and TGR5 receptor, thus opening new therapeutic target for the treatment of liver diseases or metabolic disorders. Up to twenty new semisynthetic analogues have been synthesized and studied in order to find promising novel drugs candidates. In order to define the BAs structure-activity relationship, their main physico-chemical properties (solubility, detergency, lipophilicity and affinity with serum albumin) have been measured with validated analytical methodologies. Their metabolism and biodistribution has been studied in “bile fistula rat”, model where each BA is acutely administered through duodenal and femoral infusion and bile collected at different time interval allowing to define the relationship between structure and intestinal absorption and hepatic uptake ,metabolism and systemic spill-over. One of the studied analogues, 6α-ethyl-3α7α-dihydroxy-5β-cholanic acid, analogue of CDCA (INT 747, Obeticholic Acid (OCA)), recently under approval for the treatment of cholestatic liver diseases, requires additional studies to ensure its safety and lack of toxicity when administered to patients with a strong liver impairment. For this purpose, CCl4 inhalation to rat causing hepatic decompensation (cirrhosis) animal model has been developed and used to define the difference of OCA biodistribution in respect to control animals trying to define whether peripheral tissues might be also exposed as a result of toxic plasma levels of OCA, evaluating also the endogenous BAs biodistribution. An accurate and sensitive HPLC-ES-MS/MS method is developed to identify and quantify all BAs in biological matrices (bile, plasma, urine, liver, kidney, intestinal content and tissue) for which a sample pretreatment have been optimized.

Comprehensive characterization of snowfall in terms of microphysical features, quantitative precipitation estimation and scavenging properties by in situ and remote sensing observations at an Antarctic coastal site

Snow plays a crucial role in the Earth's hydrological cycle and energy budget, making its monitoring necessary. In this context, ground-based radars and in situ instruments are essential thanks to their spatial coverage, resolution, and temporal sampling. Deep understanding and reliable measurements of snow properties are crucial over Antarctica to assess potential future changes of the surface mass balance (SMB) and define the contribution of the Antarctic ice sheet on sea-level rise. However, despite its key role, Antarctic precipitation is poorly investigated due to the continent's inaccessibility and extreme environment. In this framework, this Thesis aims to contribute to filling this gap by in-depth characterization of Antarctic precipitation at the Mario Zucchelli station from different points of view: microphysical features, quantitative precipitation estimation (QPE), vertical structure of precipitation, and scavenging properties. For this purpose, a K-band vertically pointing radar collocated with a laser disdrometer and an optical particle counter (OPC) were used. The radar probed the lowest atmospheric layers with high vertical resolution, allowing the first trusted measurement at only 105 m height. Disdrometer and OPC provided information on the particle size distribution and aerosol concentrations. An innovative snow classification methodology was designed by comparing the radar reflectivity (Ze) and disdrometer-derived reflectivity by means of DDA simulations. Results of classification were exploited in QPE through appropriate Ze-snow rate relationships. The accuracy of the resulting QPE was benchmarked against a collocated weighing gauge. Vertical radar profiles were also investigated to highlight hydrometeors' sublimation and growth processes. Finally, OPC and disdrometer data allowed providing the first-ever estimates of scavenging properties of Antarctic snowfall. Results presented in this Thesis give rise to advances in knowledge of the characteristics of snowfall in Antarctica, contributing to a better assessment of the SMB of the Antarctic ice sheet, the major player in the global sea-level rise.

Valorisation of organic waste: new developments from proton nuclear magnetic resonance characterization

The last half-century has seen a continuing population and consumption growth, increasing the competition for land, water and energy. The solution can be found in the new sustainability theories, such as the industrial symbiosis and the zero waste objective. Reducing, reusing and recycling are challenges that the whole world have to consider. This is especially important for organic waste, whose reusing gives interesting results in terms of energy release. Before reusing, organic waste needs a deeper characterization. The non-destructive and non-invasive features of both Nuclear Magnetic Resonance (NMR) relaxometry and imaging (MRI) make them optimal candidates to reach such characterization. In this research, NMR techniques demonstrated to be innovative technologies, but an important work on the hardware and software of the NMR LAGIRN laboratory was initially done, creating new experimental procedures to analyse organic waste samples. The first results came from soil-organic matter interactions. Remediated soils properties were described in function of the organic carbon content, proving the importance of limiting the addition of further organic matter to not inhibit soil processes as nutrients transport. Moreover NMR relaxation times and the signal amplitude of a compost sample, over time, showed that the organic matter degradation of compost is a complex process that involves a number of degradation kinetics, as a function of the mix of waste. Local degradation processes were studied with enhanced quantitative relaxation technique that combines NMR and MRI. The development of this research has finally led to the study of waste before it becomes waste. Since a lot of food is lost when it is still edible, new NMR experiments studied the efficiency of conservation and valorisation processes: apple dehydration, meat preservation and bio-oils production. All these results proved the readiness of NMR for quality controls on a huge kind of organic residues and waste.

Fusarium head blight: characterization of secondary Fusarium species, evaluation of their pathogenetic role and ability to produce mycotoxins

Fusarium head blight (FHB) is a worldwide cereal disease caused by a complex of Fusarium species resulting in high yield losses, reduction in quality and mycotoxin contamination of grain. A shift in Fusarium head blight community has been observed worldwide. The present work aimed to analyze the evolution of Italian FHB community focusing the attention on species considered “secondary” in the past years such as members of Fusarium tricinctum species complex (FTSC) and F. proliferatum. The first goal of the study was to analyze the fungal community associated with Italian durum wheat in two different years. F. poae, F. avenaceum and F. proliferatum were the main species detected on Italian durum kernels. A variable mycotoxins contamination was observed in the analyzed samples. Considering, the increased incidence of F. avenaceum and other members of FTSC in Italian FHB, the second aim was to investigate genetic diversity among the FTSC and estimate the mycotoxin risk related to these species. Phylogenetic analyses revealed that F. avenaceum (FTSC 4) was the most common species in Italy, followed by an unnamed Fusarium sp., F. tricinctum and F. acuminatum. In addition to these four phylospecies, five other F. tricinctum clade species were sampled. These included strains of four newly discovered species (Fusarium spp. FTSC 11, 13, 14, 15) and F. iranicum (FTSC 6). Most isolates tested for mycotoxin production on rice cultures were able to produce quantitative levels of enniatins and moniliformin. In addition, a preliminary study was conducted to evaluate the ability of a selected F. proliferatum isolate to produce fumonisins on wheat in open field and under natural climatic conditions. The three analogues (FB1, FB2 and FB3) were quantified by HPLC-FLD analysis on kernels, chaff and rachis. Fumonisins were detected in all the three investigated fractions without significant differences.

Development and characterization of deep learning techniques for neuroimaging data

Deep learning methods are extremely promising machine learning tools to analyze neuroimaging data. However, their potential use in clinical settings is limited because of the existing challenges of applying these methods to neuroimaging data. In this study, first a data leakage type caused by slice-level data split that is introduced during training and validation of a 2D CNN is surveyed and a quantitative assessment of the model’s performance overestimation is presented. Second, an interpretable, leakage-fee deep learning software written in a python language with a wide range of options has been developed to conduct both classification and regression analysis. The software was applied to the study of mild cognitive impairment (MCI) in patients with small vessel disease (SVD) using multi-parametric MRI data where the cognitive performance of 58 patients measured by five neuropsychological tests is predicted using a multi-input CNN model taking brain image and demographic data. Each of the cognitive test scores was predicted using different MRI-derived features. As MCI due to SVD has been hypothesized to be the effect of white matter damage, DTI-derived features MD and FA produced the best prediction outcome of the TMT-A score which is consistent with the existing literature. In a second study, an interpretable deep learning system aimed at 1) classifying Alzheimer disease and healthy subjects 2) examining the neural correlates of the disease that causes a cognitive decline in AD patients using CNN visualization tools and 3) highlighting the potential of interpretability techniques to capture a biased deep learning model is developed. Structural magnetic resonance imaging (MRI) data of 200 subjects was used by the proposed CNN model which was trained using a transfer learning-based approach producing a balanced accuracy of 71.6%. Brain regions in the frontal and parietal lobe showing the cerebral cortex atrophy were highlighted by the visualization tools.