Emilio Marengo

Pancreatic ductal adenocarcinoma (PDAC) accounts for 90% of all pancreatic malignancies. Despite the remarkable improvement concerning treatment, late detection and resistance to clinically used chemotherapeutic agents remain major challenges. Trichostatin A (TSA), a histone deacetylase inhibitor, has been recognized as an effective therapeutic agent against PDAC by inhibiting proliferation, inducing apoptosis, and sensitizing PDAC cells to chemotherapeutic agents such as gemcitabine. Microgravity has become a useful tool in cancer research due to its effects on various cellular processes. This paper presents a deep molecular and proteomic analysis investigating cell growth, the modulation of cytokeratins, and proteins related to apoptosis, cellular metabolism, and protein synthesis after TSA treatment in simulated microgravity (SMG)-exposed PaCa44 3D cells. Our analysis concerns the effects of TSA treatment on cell proliferation: the impairment of the cell cycle with the downregulation of proteins involved in Cdc42 signaling and G1/G2- and G2/M-phase transitions. Thus, we observed modification of survival pathways and proteins related to autophagy and apoptosis. We also observed changes in proteins involved in the regulation of transcription and the repair of damaged DNA. TSA treatment promotes the downregulation of some markers involved in the maintenance of the potency of stem cells, while it upregulates proteins involved in the induction and modulation of the differentiation process. Our data suggest that TSA treatment restores the cell phenotype prior to simulated microgravity exposure, and exerts an intriguing activity on PDAC cells by reducing proliferation and inducing cell death via multiple pathways.

This study is focused on the implementation of statistical learning methods for the prediction of the mechanical properties of steel products from the chemical profile of the raw material and the process parameters. The integration of this model into the production process allows a large-scale steel industry to predict steel properties with heightened accuracy, optimizing the manufacturing process for minimal waste and improved consistency. A workflow for process data analysis has been developed, based on the use of machine learning algorithms to build an interface for data treatment to be directly used online. The proposed approach has a comprehensive connotation, starting from data pre-treatment and cleaning, to model building and prediction. Different machine learning algorithms are compared (Polynomial Regression, LASSO, Random Forests and Gradient Boosting, ANN, SVM, and k-NN), to provide the best predictive ability, also exploiting human reinforcement. The results proved to be very promising for all the types of steel investigated, with very good RMSE and R² values both in fitting and in prediction. The application here presented is being integrated into Total Quality Tutor (TQT) software, developed in-house in C# language, for predicting the mechanical properties of steel.

Global fish and seafood consumption is increasing annually, frequently leading to the emergence of food fraud, mainly related to mislabeling and adulteration like, for example, the use of illicit/unauthorized food additives to mask or delay fish spoilage. Among the available diagnostic tools for control purposes, spectroscopic techniques have often been proposed to identify these kinds of illicit practices in fish and seafood products. The presented study aims to test two cheap and portable near infrared (NIR) spectrometers, a handheld MicroNIR and a pocket-sized SCiO, to uncover use of the illicit food additive Cafodos, a mixture of sodium citrate and hydrogen peroxide used to preserve some fish characteristics (like smell, color, na dtexture). The NIR spectroscopy in combination with chemometric approaches, allowed the successfully classification of (81–100%) samples of sea bass (Dicentrarchus labrax) treated with Cafodos. The study highlights the potential of this technique that, by not requiring pre-treatment of samples with further reagents, is cheaper and safer for the environment. In conclusion, the study confirmed the potential of portable devices for rapid NIR spectroscopy analysis to identify food fraud and ensure consumer safety.

The present work shows that the exposure of mesothelial cells to simulated microgravity changes their cytoskeleton and adhesion proteins, leading to a cell switch from normal towards tumoral cells. Immunohistochemical and molecular data were obtained from both MeT-5A exposed to simulated microgravity and BR95 mesothelioma cell lines. Simulated microgravity was found to affect the expression of actin, vinculin, and connexin-43, altering their quantitative and spatial distribution pattern inside the cell. The analysis of the tumoral markers p27, CD44, Fibulin-3, and NANOG and the expression of genes related to cancer transformation such as NANOG, CDH-1, and Zeb-1 showed that the simulated microgravity environment led to expression patterns in MeT-5A cells similar to those observed in BR95 cells. The alteration in both quantitative expression and structural organization of the cytoskeleton and adhesion/communication proteins can thus be considered a pivotal mechanism involved in the cellular shift towards tumoral progression.

Fish freshness consists of complex endogenous and exogenous processes; therefore, the use of a few parameters to unravel illicit practices could be insufficient. Moreover, the development of strategies for the identification of such practices based on additives known to prevent and/or delay fish spoilage is still limited. The paper deals with the identification of the effect played by a Cafodos solution on the conservation state of sea bass at both short-term (3 h) and long-term (24 h). Controls and treated samples were characterized by a multi-omic approach involving proteomics, lipidomics, metabolomics, and metagenomics. Different parts of the fish samples were studied (muscle, skin, eye, and gills) and sampled through a non-invasive procedure based on EVA strips functionalized by ionic exchange resins. Data fusion methods were then applied to build models able to discriminate between controls and treated samples and identify the possible markers of the applied treatment. The approach was effective in the identification of the effect played by Cafodos that proved to be different in the short- and long-term and complex, involving proteins, lipids, and small molecules to a different extent.

In the field of food control for fresh products, the identification of foods subjected to illicit conservation treatments to extend their shelf life is fundamental. Fresh fish products are particularly subjected to this type of fraud due to their high commercial value and the fact that they often have to be transported over a long distance, keeping their organoleptic characteristics unaltered. Treatments of this type involve, e.g., the bleaching of the meat and/or the momentary abatement of the microbial load, while the degradation process continues. It is therefore important to find rapid methods that allow the identification of illicit treatments. The study presented here was performed on 24 sea bass samples divided into four groups: 12 controls (stored on ice in the fridge for 3 or 24 h), and 12 treated with a Cafodos-like solution for 3 or 24 h. Muscle and skin samples were then characterized using micro-Raman spectroscopy. The data were pre-processed by smoothing and taking the first derivative and then PLS-DA models were built to identify short- and long- term effects on the fish’s muscle and skin. All the models provided the perfect classification of the samples both in fitting and cross-validation and an analysis of the bands responsible for the effects was also reported. To the best of the authors’ knowledge, this is the first time Raman spectroscopy has been applied for the identification of a Cafodos-like illicit treatment, focusing on both fish muscle and skin evaluation. The procedure could pave the way for a future application directly on the market through the use of a portable device.

Biochemical investigations were carried out on the embalmed head of Nebiri (Museo Egizio, Turin; S-5109)—an 18th Dynasty Ancient Egyptian dignitary—and on the canopic jar containing his lungs (Museo Egizio, Turin; S. 5111/02) with the aim of characterizing the organ’s (lung) specific paleo-proteins and of identifying the compounds used in his embalming “recipe”. The application of a functionalized film method allowed us to perform a non-invasive sampling. Paleo-proteomics confirmed the presence of lung tissue-specific proteins (organ specific) as well as the presence of proteins linked to severe inflammation. Paleoproteomics and paleometabolomics further allowed the identification of the main components of Nebiri’s embalming recipe: animal fats and glue, balms, essential oils, aromatic plants, heated Pistacia, and coniferous resins. Both the use of Pistacia and coniferous resins in an early 18th Dynasty individual confirm Nebiri’s high social status. The technique applied offers a targeted approach to the chemical characterization of human tissues, embalming compounds, and organic materials layering in pottery. The ability of the functionalized film method to harvest all types of compounds, from macromolecules (i.e., proteins) to small molecules (i.e., organic acids) opens a new path in the study of ancient material culture; furthermore, it allows to perform untargeted analysis, which is necessary when no a priori information is available.

Corticosteroids such as Dexamethasone (DEX) are commonly licensed for therapy in meat animals due to their known pharmacological properties. However, their misuse aimed to achieve anabolic effects is often found by National Residues Control Plans. The setup of a complementary “biomarker based” methods to unveil such illicit practices is encouraged by current European legislation. In this study, the combined use of molecular and histological quantitative techniques was applied on formalin fixed paraffin embedded (FFPE) muscle samples to assess the effects of illicit DEX treatment on veal calves. A PCR array, including 28 transcriptional biomarkers related to DEX exposure, was combined with a histochemical analysis of muscle fiber. An analysis based on unsupervised (PCA) and supervised (PLS-DA and Kohonen’s SOM) methods, was applied in order to define multivariate models able to classify animals suspected of illicit treatment by DEX. According to the conventional univariate approach, a not-significant reduction in type I fibres was recorded in the DEX-treated group, and only 12 out of 28 targeted genes maintained their expected differential expression, confirming the technical limitations of a quantitative analysis on FFPE samples. However, the multivariate models developed highlighted the possibility to establish complementary screening strategies, particularly when based on transcriptional biomarkers characterised by low expression profiles.