Diatomite silica nanoparticles for drug delivery

Diatomite is a natural fossil material of sedimentary origin, constituted by fragments of diatom siliceous skeletons. In this preliminary work, the properties of diatomite nanoparticles as potential system for the delivery of drugs in cancer cells were exploited. A purification procedure, based on thermal treatments in strong acid solutions, was used to remove inorganic and organic impurities from diatomite and to make them a safe material for medical applications. The micrometric diatomite powder was reduced in nanoparticles by mechanical crushing, sonication, and filtering. Morphological analysis performed by dynamic light scattering and transmission electron microscopy reveals a particles size included between 100 and 300 nm. Diatomite nanoparticles were functionalized by 3-aminopropyltriethoxysilane and labeled by tetramethylrhodamine isothiocyanate. Different concentrations of chemically modified nanoparticles were incubated with cancer cells and confocal microscopy was performed. Imaging analysis showed an efficient cellular uptake and homogeneous distribution of nanoparticles in cytoplasm and nucleus, thus suggesting their potentiality as nanocarriers for drug delivery.

PACS

87.85.J81.05.Rm; 61.46. + w     

BMP9-Induced Survival Effect in Liver Tumor Cells Requires p38MAPK Activation

The study of bone morphogenetic proteins (BMPs) role in tumorigenic processes, and specifically in the liver, has gathered importance in the last few years. Previous studies have shown that BMP9 is overexpressed in about 40% of hepatocellular carcinoma (HCC) patients. In vitro data have also shown evidence that BMP9 has a pro-tumorigenic action, not only by inducing epithelial to mesenchymal transition (EMT) and migration, but also by promoting proliferation and survival in liver cancer cells. However, the precise mechanisms driving these effects have not yet been established. In the present work, we deepened our studies into the intracellular mechanisms implicated in the BMP9 proliferative and pro-survival effect on liver tumor cells. In HepG2 cells, BMP9 induces both Smad and non-Smad signaling cascades, specifically PI3K/AKT and p38MAPK. However, only the p38MAPK pathway contributes to the BMP9 growth-promoting effect on these cells. Using genetic and pharmacological approaches, we demonstrate that p38MAPK activation, although dispensable for the BMP9 proliferative activity, is required for the BMP9 protective effect on serum withdrawal-induced apoptosis. These findings contribute to a better understanding of the signaling pathways involved in the BMP9 pro-tumorigenic role in liver tumor cells.     

Geranylgeraniol and Neurological Impairment: Involvement of Apoptosis and Mitochondrial Morphology

Deregulation of the cholesterol pathway is an anomaly observed in human diseases, many of which have in common neurological involvement and unknown pathogenesis. In this study we have used Mevalonate Kinase Deficiency (MKD) as a disease-model in order to investigate the link between the deregulation of the mevalonate pathway and the consequent neurodegeneration. The blocking of the mevalonate pathway in a neuronal cell line (Daoy), using statins or mevalonate, induced an increase in the expression of the inflammasome gene (NLRP3) and programmed cell death related to mitochondrial dysfunction. The morphology of the mitochondria changed, clearly showing the damage induced by oxidative stress and the decreased membrane potential associated with the alterations of the mitochondrial function. The co-administration of geranylgeraniol (GGOH) reduced the inflammatory marker and the damage of the mitochondria, maintaining its shape and components. Our data allow us to speculate about the mechanism by which isoprenoids are able to rescue the inflammatory marker in neuronal cells, independently from the block of the mevalonate pathway, and about the fact that cell death is mitochondria-related.     

Immersive and desktop virtual reality in virtual fashion stores: a comparison between shopping experiences

Virtual Reality - With the high growth and prosperity of e-commerce, the retail industry needs to explore new technologies that improve digital shopping experiences. In the current technological...     

Analysis and verification of SMIL documents

In this paper, we consider the problem of automatic verification of SMIL documents and present a tool which can assist the user in the complex task of authoring a multimedia presentation. The tool is based on a formal semantics defining the temporal aspects of SMIL elements by means of a set of inference rules. The rules, in the spirit of Hoare’s semantics, describe how the execution of a piece of code changes the state of the computation of a player. If any temporal conflict is found, the system returns a message to the user pointing out the element which contains the conflict and its motivation. This helps the user to develop robust and clear code.     

Network regression with predictive clustering trees

Network data describe entities represented by nodes, which may be connected with (related to) each other by edges. Many network datasets are characterized by a form of autocorrelation, where the value of a variable at a given node depends on the values of variables at the nodes it is connected with. This phenomenon is a direct violation of the assumption that data are independently and identically distributed. At the same time, it offers an unique opportunity to improve the performance of predictive models on network data, as inferences about one entity can be used to improve inferences about related entities. Regression inference in network data is a challenging task. While many approaches for network classification exist, there are very few approaches for network regression. In this paper, we propose a data mining algorithm, called NCLUS, that explicitly considers autocorrelation when building regression models from network data. The algorithm is based on the concept of predictive clustering trees (PCTs) that can be used for clustering, prediction and multi-target prediction, including multi-target regression and multi-target classification. We evaluate our approach on several real world problems of network regression, coming from the areas of social and spatial networks. Empirical results show that our algorithm performs better than PCTs learned by completely disregarding network information, as well as PCTs that are tailored for spatial data, but do not take autocorrelation into account, and a variety of other existing approaches.     

Promoting the integration of technology in teaching: An analysis of the factors that increase the intention to use technologies among Italian teachers

Background: The rapid spread of COVID-19 forced many countries to adopt severe containment measures, transferring all didactic activities into virtual environments. However, the integration of technology in teaching may present difficulties, especially in some countries, such as Italy. Objectives: The present study analyzed how the two main factors of the Technology Acceptance Model (TAM), namely perceived usefulness and perceived ease of use, combined with online teaching self-efficacy, were associated with the intention to use technology to teach. We posited a moderated moderation model in which perceived usefulness represented the main predictor, with self-efficacy and perceived ease of use as moderators of intention to use technology to teach. Method: The model was tested through multiple regressions, using the PROCESS macro on SPSS 26 with a sample of 178 upper secondary school teachers in Italy. Results and conclusions: Regressions showed that each variable significantly predicted the intention to use technology. In addition, a moderation effect of self-efficacy on the perceived usefulness of using technology was found for medium and high-level of perceived ease of use of technology. Implications: The present study provides targeted implications for distance education policy and practice to promote its adoption (or the blended modality) in Italian upper schools.     

Mixture of KL subspaces for relevance feedback

Relevance feedback has recently emerged as a solution to the problem of improving the retrieval performance of an image retrieval system based on low-level information such as color, texture and shape features. Most of the relevance feedback approaches limit the utilization of the user’s feedback to a single search session, performing a short-term learning. In this paper we present a novel approach for short and long term learning, based on the definition of an adaptive similarity metric and of a high level representation of the images. For short-term learning, the relevant and non-relevant information given by the user during the feedback process is employed to create a positive and a negative subspace of the feature space. For long-term learning, the feedback history of all the users is exploited to create and update a representation of the images which is adopted for improving retrieval performance and progressively reducing the semantic gap between low-level features and high-level semantic concepts. The experimental results prove that the proposed method outperforms many other state of art methods in the short-term learning, and demonstrate the efficacy of the representation adopted for the long-term learning.