High Glucose Exposure Impairs L-Cell Differentiation in Intestinal Organoids: Molecular Mechanisms and Clinical Implications

Intestinal organoids are used to analyze the differentiation of enteroendocrine cells (EECs) and to manipulate their density for treating type 2 diabetes. EEC differentiation is a continuous process tightly regulated in the gut by a complex regulatory network. However, the effect of chronic hyperglycemia, in the modulation of regulatory networks controlling identity and differentiation of EECs, has not been analyzed. This study aimed to investigate the effect of glucotoxicity on EEC differentiation in small intestinal organoid platforms. Mouse intestinal organoids were cultured in the presence/absence of high glucose concentrations (35 mM) for 48 h to mimic glucotoxicity. Chronic hyperglycemia impaired the expression of markers related to the differentiation of EEC progenitors (Ngn3) and L-cells (NeuroD1), and it also reduced the expression of Gcg and GLP-1 positive cell number. In addition, the expression of intestinal stem cell markers was reduced in organoids exposed to high glucose concentrations. Our data indicate that glucotoxicity impairs L-cell differentiation, which could be associated with decreased intestinal stem cell proliferative capacity. This study provides the identification of new targets involved in new molecular signaling mechanisms impaired by glucotoxicity that could be a useful tool for the treatment of type 2 diabetes.     

Sleep-related car crashes: risk perception and decision-making processes in young drivers

The aim of the present study is to analyse factors affecting worries, coping strategies and decisions of young drivers regarding the risk of sleep-related car crashes. Furthermore, the study also analyses whether framing the same information about sleepiness in two different linguistic forms influences: (1) the evaluation of the level of risk associated to a specific level of drowsiness (Attribute Framing problem); (2) the willingness to enact strategies to "prevent" sleepiness before night-time driving (Goal Framing problem); (3) the choice between two different ways, both of equal expected efficacy, of lowering drowsiness (Risky decision-making Framing problem). Six hundred and ninety-five young drivers [(57.6% females, 42.4% males); mean age 20.85 years (S.D.=1.2)] answered questions on drive risk perception and sleepiness, on nocturnal driving experience and on the strategies to deal with driver sleepiness, responding to one of the two different versions of the framed problems. A sub-sample of 130 participants completed the framed problems in both versions. The results show that experiences of sleep attacks and nocturnal driving frequency in the past 6 months affect both risk perception and the preventive strategies adopted. Furthermore, the manipulation on two out of the three problems (attribute and risky decision-making frames) significantly affected the respondents' evaluation.     

GMTR: two-dimensional geo-fit multitarget retrieval model for michelson interferometer for passive atmospheric sounding/environmental satellite observations

We present a new retrieval model designed to analyze the observations of the Michelson Interferometer for Passive Atmospheric Sounding (MIPAS), which is on board the ENVironmental SATellite (ENVISAT). The new geo-fit multitarget retrieval model (GMTR) implements the geo-fit two-dimensional inversion for the simultaneous retrieval of several targets including a set of atmospheric constituents that are not considered by the ground processor of the MIPAS experiment. We describe the innovative solutions adopted in the inversion algorithm and the main functionalities of the corresponding computer code. The performance of GMTR is compared with that of the MIPAS ground processor in terms of accuracy of the retrieval products. Furthermore, we show the capability of GMTR to resolve the horizontal structures of the atmosphere. The new retrieval model is implemented in an optimized computer code that is distributed by the European Space Agency as "open source" in a package that includes a full set of auxiliary data for the retrieval of 28 atmospheric targets.     

Modeling of the polluting emissions from a cement production plant by partial least-squares, principal component regression, and artificial neural networks

A Portland cement process was taken into consideration and monitored for one month with respect to polluting emissions, fuel and raw material physical-chemical properties, and operative conditions. Soft models, based on linear (partial least-squares, PLS, and principal component regression, PCR) and nonlinear (artificial neural networks, ANNs) approaches, were employed to predict the polluting emissions. The predictive ability of the three regression methods was evaluated by means of the partition of the dataset by Kohonen self-associative maps into both a training and a test set. Then, a "leave-more-out" approach, based on the use of a training set, a test set, and a production set, was adopted. The training set was used to build the models, the test set was used to select the number of latent variables or the neural network training endpoint, and the production set was used to produce genuine predictions. ANNs proved to be much more effective in prediction with respect to PLS and PCR and, at least in the case of SO2 and dust, provided a predictive ability comparable with the experimental estimated uncertainty of the response. This showed that it is possible to satisfactorily predict the two responses. Such a prediction will result in the prevention of environmental and legal problems connected to the polluting emissions.     

Finite-element modeling of bones from CT data: sensitivity to geometry and material uncertainties

The aim of this paper is to analyze how the uncertainties in modelling the geometry and the material properties of a human bone affect the predictions of a finite-element model derived from computed tomography (CT) data. A sensitivity analysis, based on a Monte Carlo method, was performed using three femur models generated from in vivo CT datasets, each subjected to two different loading conditions. The geometry, the density and the mechanical properties of the bone tissue were considered as random input variables. Finite-element results typically used in biomechanics research were considered as statistical output variables, and their sensitivity to the inputs variability assessed. The results showed that it is not possible to define a priori the influence of the errors related to the geometry definition process and to the material assignment process on the finite-element analysis results. The errors in the geometric representation of the bone are always the dominant variables for the stresses, as was expected. However, for all the variables, the results seemed to be dependent on the loading condition and to vary from subject to subject. The most interesting result is, however, that using the proposed method to build a finite-element model of a femur from a CT dataset of the quality typically achievable in the clinical practice, the coefficients of variation of the output variables never exceed the 9%. The presented method is hence robust enough to be used for investigating the mechanical behavior of bones with subject-specific finite-element models derived from CT data taken in vivo.     

Preparation and Characterization of Hyperbranched Polymer/Silica Hybrid Nanocoatings by Dual‐Curing Process

Summary: Organic‐inorganic nanocomposite hybrid coatings were prepared through a dual‐cure process involving cationic photopolymerization of a hyperbranched epoxy functionalized resin and subsequent condensation of an alkoxysilane inorganic precursor. All the formulations investigated gave rise to photocured films characterized by high gel content values. An increase in glass transition temperature and an increase in storage modulus above T_g in the rubbery plateau is observed with increasing TEOS content in the photocurable formulation. The important role of GPTS on reducing the inorganic domain size and avoiding macroscopic phase separation was demonstrated by TEM analyses.

TEM obtained for one of the cured films in the presence of GPTS.     

FEMMINA real-time, radio-frequency echo-signal equipment for testing novel investigation methods

Fast echographic multiparameter multi-image novel apparatus (FEMMINA), is a hardware and software platform dedicated to ultrasonic signal and image processing. FEMMINA is able to operate with sequences of radiofrequency (RF) frames. Its architecture is designed to be modular, expandable, and aimed at implementing different ultrasonic investigation techniques. The first experimental characteristic of this system is in its capability to operate in real time with ultrasonic RF signals, starting from acquisition up to processing, storage, and visualization. The second characteristic is the user-system interactivity that allows one to modify the operation appropriately while observing results. Currently, FEMMINA works in both typical experimental situations to study novel investigation techniques and clinical field to validate the proposed methods in different human districts.     

Power efficiency evaluation in Dickson and voltage doubler charge pump topologies

This paper presents a theoretical and experimental comparison between two charge pump architectures commonly used in CMOS integrated circuits, namely the Dickson scheme and the cascade of voltage doublers. The comparison is carried out considering power efficiency as the main feature of interest. To compare the two topologies, two charge pumps were integrated in 0.18-μm triple-well CMOS technology. The two charge pumps were designed with the same operating clock frequency, the same storage capacitance per stage, and the same number of stages (and, thus, approximately the same area). The theoretical and the experimental comparison showed that the power efficiency of the voltage doubler scheme is higher (by about 13% at ), mainly thanks to the lower parasitic capacitance associated to the boosted nodes.     

Sample treatment considerations in the analysis of organic colorants by surface-enhanced Raman scattering

The introduction of surface-enhanced Raman spectroscopy (SERS) in the field of cultural heritage has significantly improved the analysis of the organic dyes and their complexes that have been used as textile dyes and pigments in paintings and other polychrome works of art since antiquity. Over the last five years, a number of different procedures have been developed by various research groups. In this Article, we evaluate the effect of pretreating samples by exposing them to hydrofluoric acid (HF) vapor prior to SERS analysis, a step designed to hydrolyze the dye-metal complexes and increase analyte adsorption on the nanosized metallic support, thus enhancing the SERS signal. Materials studied include pure colorants, commercial lake pigments, and fibers from dyed textiles, as well as actual aged samples, such as microscopic fragments of lakes on paper and ancient pigments and glazes from several works of art, covering a wide range of time, from the second century B.C. to the early 20th century. In each case, SERS spectra obtained with or without HF hydrolysis were critically evaluated. The pretreatment with HF vapor resulted in faster analysis and increased sensitivity in most cases, with the exception of dyed silk fibers, where silk protein hydrolyzates were found to interfere with SERS analysis. As a final point, a two-step procedure including SERS on untreated and treated samples is proposed as a standard approach: by analyzing a sample first without hydrolysis, and then, following removal of the colloid, upon HF treatment, the best and most reliable results for a great number of dyes and substrates are assured.