Reversible Formation of g‐C3N4 3D Hydrogels through Ionic Liquid Activation: Gelation Behavior and Room‐Temperature Gas‐Sensing Properties

Many unique properties arise when the 3D stacking of layered materials is disrupted, originating nanostructures. Stabilization, and further reorganization of these individual layers into complex 3D structures, can be essential to allow these properties to persist in macroscopic systems. It is demonstrated that a simple hydrothermal process, assisted by ionic liquids (ILs), can convert bulk g‐C₃N₄ into a stable hydrogel. The gelation occurs through delamination of the layered structure, driven by particular interactions between the IL and the carbon nitride sheets, forming an amphiphilic foam‐like network. This study employs spectroscopic and computational tools to unravel the gelation mechanism, and provides a rational approach toward the stabilization of 2D materials in hydrogels. The solution‐processable hydrogels can also be used as building blocks of complex devices. Chemiresistive gas sensors employing g‐C₃N₄ 3D hydrogels exhibit superior response at room temperature, enabling effective gas sensing under low power conditions.     

Functionalized Hydrogels for Cartilage Repair: The Value of Secretome-Instructive Signaling

Cartilage repair has been a challenge in the medical field for many years. Although treatments that alleviate pain and injury are available, none can effectively regenerate the cartilage. Currently, regenerative medicine and tissue engineering are among the developed strategies to treat cartilage injury. The use of stem cells, associated or not with scaffolds, has shown potential in cartilage regeneration. However, it is currently known that the effect of stem cells occurs mainly through the secretion of paracrine factors that act on local cells. In this review, we will address the use of the secretome—a set of bioactive factors (soluble factors and extracellular vesicles) secreted by the cells—of mesenchymal stem cells as a treatment for cartilage regeneration. We will also discuss methodologies for priming the secretome to enhance the chondroregenerative potential. In addition, considering the difficulty of delivering therapies to the injured cartilage site, we will address works that use hydrogels functionalized with growth factors and secretome components. We aim to show that secretome-functionalized hydrogels can be an exciting approach to cell-free cartilage repair therapy.     

Epigenetic and Genetic Factors Related to Curve Progression in Adolescent Idiopathic Scoliosis: A Systematic Scoping Review of the Current Literature

Adolescent idiopathic scoliosis (AIS) is a progressive deformity of the spine. Scoliotic curves progress until skeletal maturity leading, in rare cases, to a severe deformity. While the Cobb angle is a straightforward tool in initial curve magnitude measurement, assessing the risk of curve progression at the time of diagnosis may be more challenging. Epigenetic and genetic markers are potential prognostic tools to predict curve progression. The aim of this study is to review the available literature regarding the epigenetic and genetic factors associated with the risk of AIS curve progression. This review was carried out in accordance with Preferential Reporting Items for Systematic Reviews and Meta-analyses (PRISMA) guidelines. The search was carried out in January 2022. Only peer-reviewed articles were considered for inclusion. Forty studies were included; fifteen genes were reported as having SNPs with significant association with progressive AIS, but none showed sufficient power to sustain clinical applications. In contrast, nine studies reporting epigenetic modifications showed promising results in terms of reliable markers. Prognostic testing for AIS has the potential to significantly modify disease management. Most recent evidence suggests epigenetics as a more promising field for the identification of factors associated with AIS progression, offering a rationale for further investigation in this field.     

NSCLC as the Paradigm of Precision Medicine at Its Finest: The Rise of New Druggable Molecular Targets for Advanced Disease

Standard treatment for advanced non-small cell lung cancer (NSCLC) historically consisted of systemic cytotoxic chemotherapy until the early 2000s, when precision medicine led to a revolutionary change in the therapeutic scenario. The identification of oncogenic driver mutations in EGFR, ALK and ROS1 rearrangements identified a subset of patients who largely benefit from targeted agents. However, since the proportion of patients with druggable alterations represents a minority, the discovery of new potential driver mutations is still an urgent clinical need. We provide a comprehensive review of the emerging molecular targets in NSCLC and their applications in the advanced setting.     

Molecular Effects of Chronic Exposure to Palmitate in Intestinal Organoids: A New Model to Study Obesity and Diabetes

Intestinal cell dysfunctions involved in obesity and associated diabetes could be correlated with impaired intestinal cell development. To date, the molecular mechanisms underlying these dysfunctions have been poorly investigated because of the lack of a good model for studying obesity. The main aim of this study was to investigate the effects of lipotoxicity on intestinal cell differentiation in small intestinal organoid platforms, which are used to analyze the regulation of cell differentiation. Mouse intestinal organoids were grown in the presence/absence of high palmitate concentrations (0.5 mM) for 48 h to simulate lipotoxicity. Palmitate treatment altered the expression of markers involved in the differentiation of enterocytes and goblet cells in the early (Hes1) and late (Muc2) phases of their development, respectively, and it modified enterocytes and goblet cell numbers. Furthermore, the expression of enteroendocrine cell progenitors (Ngn3) and I cells (CCK) markers was also impaired, as well as CCK-positive cell numbers and CCK secretion. Our data indicate, for the first time, that lipotoxicity simultaneously influences the differentiation of specific intestinal cell types in the gut: enterocytes, goblet cells and CCK cells. Through this study, we identified novel targets associated with molecular mechanisms affected by lipotoxicity that could be important for obesity and diabetes therapy.     

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.     

Probabilistic topic models for sequence data

Probabilistic topic models are widely used in different contexts to uncover the hidden structure in large text corpora. One of the main (and perhaps strong) assumption of these models is that generative process follows a bag-of-words assumption, i.e. each token is independent from the previous one. We extend the popular Latent Dirichlet Allocation model by exploiting three different conditional Markovian assumptions: (i) the token generation depends on the current topic and on the previous token; (ii) the topic associated with each observation depends on topic associated with the previous one; (iii) the token generation depends on the current and previous topic. For each of these modeling assumptions we present a Gibbs Sampling procedure for parameter estimation. Experimental evaluation over real-word data shows the performance advantages, in terms of recall and precision, of the sequence-modeling approaches.     

Mimetic Discretizations of Elliptic Control Problems

We investigate the performance of the Mimetic Finite Difference (MFD) method for the approximation of a constraint optimal control problem governed by an elliptic operator. Low-order and high-order mimetic discretizations are considered and a priori error estimates are derived, in a suitable discrete norm, for both the control and the state variables. A wide class of numerical experiments performed on a set of examples selected from the literature assesses the robustness of the MFD method and confirms the convergence analysis.     

Energy-aware lazy scheduling algorithm for energy-harvesting sensor nodes

The main problem in dealing with energy-harvesting (EH) sensor nodes is represented by the scarcity and non-stationarity of powering, due to the nature of the renewable energy sources. In this work, the authors address the problem of task scheduling in processors located in sensor nodes powered by EH sources. Some interesting solutions have appeared in the literature in the recent past, as the lazy scheduling algorithm (LSA), which represents a performing mix of scheduling effectiveness and ease of implementation. With the aim of achieving a more efficient and conservative management of energy resources, a new improved LSA solution is here proposed. Indeed, the automatic ability of foreseeing at run-time the task energy starving (i.e. the impossibility of finalizing a task due to the lack of power) is integrated within the original LSA approach. Moreover, some modifications have been applied in order to reduce the LSA computational complexity and thus maximizing the amount of energy available for task execution. The resulting technique, namely energy-aware LSA, has then been tested in comparison with the original one, and a relevant performance improvement has been registered both in terms of number of executable tasks and in terms of required computational burden.