In vivo and in vitro effects of genistein as obesogenic/anti-obesogenic compound in the lipid metabolism of rainbow trout (Oncorhynchus mykiss)

Nowadays, soy is one of the most used ingredients in the formulation of fish feed, due to the ample market supply, lower market price, high protein concentration and favorable amino acid composition. Nevertheless, soybean meal products are rich and primary diet source of phytoestrogens, as genistein, which may have a potential negative impact on growth, hormonal regulation and lipid metabolism in fish. The principal aim of this study was to better understand in vivo and in vitro genistein’s effects on lipid metabolism of rainbow trout. In adipose tissue it was showed an unclear role of genistein on lipid metabolism in rainbow trout, and in liver an anti-obesogenic effect, with an up-regulation of autophagy-related genes LC3b (in adipose tissue) and ATG4b (in liver and adipose tissue), a down-regulation of apoptosis-related genes CASP3 (in adipose tissue) and CASP8 (in liver). An increase of VTG mRNA levels in liver was also observed. Genistein partially exerted these effects via estrogen- receptor dependent mechanism. In white muscle, genistein seemed to promote lipid turnover, up-regulating lipogenic (FAS and LXR) and lipolytic (HSL, PPARα and PPARβ) genes. It seemed that genistein could exert its lipolytic role via autophagic way (up-regulation of ATG4b and ATG12l), not through an apoptotic pathway (down-regulation of CASP3). The effects of genistein on lipid-metabolism and apoptosis-related genes in trout muscle were not dose-dependent, only on autophagy-related genes ATG4B and ATG12l. Moreover, a partial estrogenic activity of this phytoestrogen was also seen. Through in vitro analysis (MTT and ORO assay), instead, it was observed an anti-obesogenic effect of genistein on rainbow trout adipocytes, and this effect was not mediated by ERs. Both in vivo and in vitro, genistein exerted its effects in a dose-dependent manner.

In vivo and in vitro effects of antioxidant compounds and PPAR γ modulators on rainbow trout oxidative stress and lipid metabolism.

The aquafeed use of raw plant materials, as protein and lipid sources, has been considered and approved as a sustainable alternative to fish products (fish meal and oils) because the current trend to use high-lipid diets has been shown to induce undesirable increase in fat depots or further physiological alterations, such as induction of oxidative stress. In the aquaculture perspective, the addition of natural substances with antioxidant properties is an emerging strategy for protecting biological systems and foodstuffs from oxidative damage. Among natural substances, hydroxytyrosol (HT) and caffeic acid (CA) have attracted considerable attention as food antioxidant additives and modulators of physiological and molecular pathways involved in energy metabolism and adiposity. The aim of this study was to evaluate the effects of CA and HT on lipid metabolism and oxidative stress of rainbow trout (Oncorhynchus mykiss). In vitro results showed the potential anti-obesogenic effects of the compounds CA and HT on the adipose tissue of the rainbow trout. To support these data, in vitro assays performed (MTT, ORO, immunofluorescence) resulted in accordance among them; only results from proliferating cell nuclear antigen (PCNA) assay were not significant. In vivo results showed a possible anti-obesogenic effect of CA in liver and HT in adipose tissue. Regarding oxidative stress, we could hypothesize a possible anti-oxidant role of CA in liver.

Machine Learning methods for hepatocellular malignancies segmentation and MVI prediction

The aim of this thesis project is to automatically localize HCC tumors in the human liver and subsequently predict if the tumor will undergo microvascular infiltration (MVI), the initial stage of metastasis development. The input data for the work have been partially supplied by Sant'Orsola Hospital and partially downloaded from online medical databases. Two Unet models have been implemented for the automatic segmentation of the livers and the HCC malignancies within it. The segmentation models have been evaluated with the Intersection-over-Union and the Dice Coefficient metrics. The outcomes obtained for the liver automatic segmentation are quite good (IOU = 0.82; DC = 0.35); the outcomes obtained for the tumor automatic segmentation (IOU = 0.35; DC = 0.46) are, instead, affected by some limitations: it can be state that the algorithm is almost always able to detect the location of the tumor, but it tends to underestimate its dimensions. The purpose is to achieve the CT images of the HCC tumors, necessary for features extraction. The 14 Haralick features calculated from the 3D-GLCM, the 120 Radiomic features and the patients' clinical information are collected to build a dataset of 153 features. Now, the goal is to build a model able to discriminate, based on the features given, the tumors that will undergo MVI and those that will not. This task can be seen as a classification problem: each tumor needs to be classified either as “MVI positive” or “MVI negative”. Techniques for features selection are implemented to identify the most descriptive features for the problem at hand and then, a set of classification models are trained and compared. Among all, the models with the best performances (around 80-84% ± 8-15%) result to be the XGBoost Classifier, the SDG Classifier and the Logist Regression models (without penalization and with Lasso, Ridge or Elastic Net penalization).