NGF Modulates Cholesterol Metabolism and Stimulates ApoE Secretion in Glial Cells Conferring Neuroprotection against Oxidative Stress

Cholesterol plays a crucial role in the brain, where its metabolism is particularly regulated by astrocytic activity. Indeed, adult neurons suppress their own cholesterol biosynthesis and import this sterol through ApoE-rich particles secreted from astrocytes. Recent evidence suggests that nerve growth factor (NGF) may exert neurotrophic activity by influencing cell metabolism. Nevertheless, the effect of NGF on glial cholesterol homeostasis has still not been elucidated. Thus, the aim of this project is to assess whether NGF could influence cholesterol metabolism in glial cells. To reach this objective, the U373 astrocyte-derived cell line was used as an experimental model. Immunoblot and ELISA analysis showed that proteins and enzymes belonging to the cholesterol metabolism network were increased upon NGF treatment in glial cells. Furthermore, NGF significantly increased ApoE secretion and the amount of extracellular cholesterol in the culture medium. Co-culture and U373-conditioned medium experiments demonstrated that NGF treatment efficiently counteracted rotenone-mediated cytotoxicity in N1E-115 neuronal cells. Conversely, neuroprotection mediated by NGF treatment was suppressed when N1E-115 were co-cultured with ApoE-silenced U373 cells. Taken together, these data suggest that NGF controls cholesterol homeostasis in glial cells. More importantly, NGF exerts neuroprotection against oxidative stress, which is likely associated with the induction of glial ApoE secretion.     

Challenges in Metabolomics-Based Tests,Biomarkers Revealed by Metabolomic Analysis,and the Promise of the Application of Metabolomics in Precision Medicine

Metabolomics helps identify metabolites to characterize/refine perturbations of biological pathways in living organisms. Pre-analytical, analytical, and post-analytical limitations that have hampered a wide implementation of metabolomics have been addressed. Several potential biomarkers originating from current targeted metabolomics-based approaches have been discovered. Precision medicine argues for algorithms to classify individuals based on susceptibility to disease, and/or by response to specific treatments. It also argues for a prevention-based health system. Because of its ability to explore gene–environment interactions, metabolomics is expected to be critical to personalize diagnosis and treatment. Stringent guidelines have been applied from the very beginning to design studies to acquire the information currently employed in precision medicine and precision prevention approaches. Large, prospective, expensive and time-consuming studies are now mandatory to validate old, and discover new, metabolomics-based biomarkers with high chances of translation into precision medicine. Metabolites from studies on saliva, sweat, breath, semen, feces, amniotic, cerebrospinal, and broncho-alveolar fluid are predicted to be needed to refine information from plasma and serum metabolome. In addition, a multi-omics data analysis system is predicted to be needed for omics-based precision medicine approaches. Omics-based approaches for the progress of precision medicine and prevention are expected to raise ethical issues.     

Focused Design of Novel Cyclic Peptides Endowed with GABARAP-Inhibiting Activity

(1) Background: Disfunctions in autophagy machinery have been identified in various conditions, including neurodegenerative diseases, cancer, and inflammation. Among mammalian autophagy proteins, the Atg8 family member GABARAP has been shown to be greatly involved in the autophagy process of prostate cancer cells, supporting the idea that GABARAP inhibitors could be valuable tools to fight the progression of tumors. (2) Methods: In this paper, starting from the X-ray crystal structure of GABARAP in a complex with an AnkirinB-LIR domain, we identify two new peptides by applying in silico drug design techniques. The two ligands are synthesized, biophysically assayed, and biologically evaluated to ascertain their potential anticancer profile. (3) Results: Two cyclic peptides (WC8 and WC10) displayed promising biological activity, high conformational stability (due to the presence of disulfide bridges), and K_d values in the low micromolar range. The anticancer assays, performed on PC-3 cells, proved that both peptides exhibit antiproliferative effects comparable to those of peptide K1, a known GABARAP inhibitor. (4) Conclusions: WC8 and WC10 can be considered new GABARAP inhibitors to be employed as pharmacological tools or even templates for the rational design of new small molecules.     

SmartK: Smart cards in operating systems at kernel level

A smart card is a tamper-resistant miniature computer that performs some basic computations on input a secret information. So far, smart cards have been widely used for securing many digital transactions (e.g., pay television, ATM machines).We focus on the implementation of operating system security services leveraging on smart cards. This very challenging feature allows one to personalize some functionalities of the operating system by simply changing a smart card. Current solutions for integrating smart card features in operating system services require at least a partial execution of some of the operating system functionalities at “user level”. Unfortunately, system functionalities built on top of components lying at both kernel and user levels may negatively affect the overall system security, due to the introduction of multiple points of failure.In this work, we present the design and implementation of SmartK: a framework that integrates features of smart cards uniquely in the Linux kernel. In order to validate our approach, we propose a host of enhancements to the Linux operating system built on top of SmartK: 1) in-kernel clients' authentication with Kerberos; 2) execution of trusted code; 3) key management in secure network filesystems.In particular, we present an experimental Linux OS distribution (SalSA), which addresses the security issues related to downloading packages and to updating an operating system through the Internet.     

A 2D Full-Band Monte Carlo Study of HgCdTe-Based Avalanche Photodiodes

This work presents a numerical simulation study of HgCdTe-based avalanche photodetectors (APDs). The two-dimensional model used is based on a full-band Monte Carlo approach in which the electronic structure is computed using a nonlocal empirical pseudopotential model with spin–orbit corrections. The carrier–phonon scattering rates have been computed from first principles using a rigid pseudo-ion model. The most attractive feature of these devices is the potential for single-carrier ionization when electrons are used as the primary injection carrier. For this reason, this work focuses on two front-illuminated (electron-injection) device structures: a planar diffused PIN structure and a planar diffused PN photodiode with guard rings. To predict the performance of these APDs, the electron multiplication gain has been studied as a function of the position where photogenerated carriers are injected and as a function of the curvature of the p-type diffusion region. We find that, in the diffused PIN structure, the limited lateral spatial extent of the high-electric-field region leads to a reduction of the multiplication gain from the center of the device to the periphery. Furthermore, the higher the curvature, the more abruptly the gain decreases. For the simple PN structure, we find that the presence of the guard rings removes the high electric field from the surface and induces a more gradual roll-off of the gain from the center of the device to the periphery.     

A Brief Survey of our Contribution to Stable Carbene Chemistry

This personal account summarizes our work, beginning with the discovery of the first stable carbene in 1988 up until the recent isolation of mesoionic carbenes. It explains why we have moved our focus from acyclic to cyclic carbenes, and shows that these stable species are not limited to the role of ligand for transition metals, but that they are also powerful agents for the activation of small molecules, and for the stabilization of highly reactive diamagnetic and paramagnetic species.     

The Effect of Surface Stresses on the Critical Debonding Stress Around Nanoparticles

With the aid of an energy analysis and the surface elasticity theory, this work provides a closed form solution for the critical debonding stress of a rigid nanoparticle embedded in an elastic matrix subjected to a remote hydrostatic stress. It is proved that the debonding stress depends on the particle radius, the matrix elastic properties and the fracture energy per unit surface. The solution allows quantifying the effects of surface elastic constants, also showing that the smaller the particle size the more significant those effects are.     

A Meshless Cohesive Segments Method for Crack Initiation and Propagation in Composites

A modeling method aimed at eliminating the need of explicit crack representation in bi-dimensional structures is presented for the simulation of the initiation and subsequent propagation within composite materials. This is achieved by combining a meshless method with a physical stress–displacement based criterion known as Cohesive Model. This model consents to apply a penalty-based approach to delamination modeling where a variable penalty factor along the crack segment allows to loosen or tight the two parts according to their relative displacements. Results are showed for classical single mode loading benchmark cases and compared to experimental results taken from the literature.     

A Method for Improving Multivariate Measurement Systems Assessment

Multivariate measurement systems analysis (MSA) is usually performed by designing suitable gauge repeatability and reproducibility (R&R) experiments, ignoring available data generated by the measurement system while used for inspection or process control. This article proposes an approach that, by using the data that are routinely available from the regular operation of the instrument, allows the measurement instrument's current precision to be compared against a benchmark. The proposed method may be appropriately used in an integrated and coordinated manner with the usual multivariate gauge study in the sense that it can be used to assess the stability or a possible deterioration in the precision of the measurement instrument while operational. Therefore, the complementary use of the proposed approach and the traditional multivariate gauge R&R studies can be a useful strategy for improving the overall quality of multivariate measurement systems. Furthermore, because it can be implemented at almost no additional cost, it may be effective in reducing the costs of a multivariate MSA performed with a certain frequency. Copyright © 2014 John Wiley & Sons, Ltd.     

A global approach to the design of discrete-time cellular neural networks for associative memories

In this paper a global design method for associative memories using discrete-time cellular neural networks (DTCNNs) is presented. The proposed synthesis technique enables to realize associative memories with several advantageous features. First of all, grey-level as well as bipolar images can be stored. Moreover, the proposed approach generates networks with learning and forgetting capabilities. Finally, it is possible to design networks with any kind of predetermined interconnection structure. In particular, neighbourhoods without line crossings can be chosen, greatly simplifying the VLSI implementation of the designed DTCNNs. In the first part of this work a model of a multilevel threshold network is presented and a stability analysis is carried out using basic notions deriving from non-linear dynamical system theory. The synthesis procedure is then developed by means of a pseudoinversion technique, assuring learning and forgetting capabilities of the designed DTCNN. The use of a neighbourhood without line crossings is also discussed. Simulation results are reported to show the capability of the proposed approach.