Interfacing Graphene-Based Materials With Neural Cells

The scientific community has witnessed an exponential increase in the applications of graphene and graphene-based materials in a wide range of fields, from engineering to electronics to biotechnologies and biomedical applications. For what concerns neuroscience, the interest raised by these materials is two-fold. On one side, nanosheets made of graphene or graphene derivatives (graphene oxide, or its reduced form) can be used as carriers for drug delivery. Here, an important aspect is to evaluate their toxicity, which strongly depends on flake composition, chemical functionalization and dimensions. On the other side, graphene can be exploited as a substrate for tissue engineering. In this case, conductivity is probably the most relevant amongst the various properties of the different graphene materials, as it may allow to instruct and interrogate neural networks, as well as to drive neural growth and differentiation, which holds a great potential in regenerative medicine. In this review, we try to give a comprehensive view of the accomplishments and new challenges of the field, as well as which in our view are the most exciting directions to take in the immediate future. These include the need to engineer multifunctional nanoparticles (NPs) able to cross the blood-brain-barrier to reach neural cells, and to achieve on-demand delivery of specific drugs. We describe the state-of-the-art in the use of graphene materials to engineer three-dimensional scaffolds to drive neuronal growth and regeneration in vivo, and the possibility of using graphene as a component of hybrid composites/multi-layer organic electronics devices. Last but not least, we address the need of an accurate theoretical modeling of the interface between graphene and biological material, by modeling the interaction of graphene with proteins and cell membranes at the nanoscale, and describing the physical mechanism(s) of charge transfer by which the various graphene materials can influence the excitability and physiology of neural cells

Colored-noise thermostats \`a la carte

Recently, we have shown how a colored-noise Langevin equation can be used in the context of molecular dynamics as a tool to obtain dynamical trajectories whose properties are tailored to display desired sampling features. In the present paper, after having reviewed some analytical results for the stochastic differential equations forming the basis of our approach, we describe in detail the implementation of the generalized Langevin equation thermostat and the fitting procedure used to obtain optimal parameters. We discuss in detail the simulation of nuclear quantum effects, and demonstrate that, by carefully choosing parameters, one can successfully model strongly anharmonic solids such as neon. For the reader's convenience, a library of thermostat parameters and some demonstrative code can be downloaded from an on-line repository

Into the Wild of long non-coding RNAs in Gastrointestinal Stromal Tumors (GISTs) to explore new prognostic/predictive biomarkers

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Neuroendocrine neoplasms of the breast: diagnostic agreement and impact on outcome

The classification of breast neuroendocrine neoplasms (Br-NENs) was modified many times over the years and is still a matter of discussion. In the present study, we aimed to evaluate the diagnostic reproducibility and impact on patient outcomes of the most recent WHO 2019 edition of breast tumor classification, namely, for neuroendocrine tumors (NETs) and neuroendocrine carcinomas (NECs). This multicentric observational study included 287 breast neoplasms with NE differentiation. The cases were blindly classified by three independent groups of dedicated breast and/or endocrine pathologists following the 2019 guidelines. Diagnostic concordance and clinical impact were assessed. We observed only a moderate overall diagnostic agreement across the three centers (Cohen’s kappa 0.4532) in distinguishing NET from solid papillary carcinomas (SPCs) and no special type carcinomas (NST) with NE differentiation. Br-NENs were diagnosed in 122/287 (42.5%) cases, subclassified as 11 NET G1 (3.8%), 84 NET G2 (29.3%), and 27 NEC (9.4%), the latter group consisting of 26 large-cell and 1 small-cell NECs. The remaining 165/287 (57.5%) cases were labeled as non-NEN, including SPC, mucinous, NST, and mixed NE carcinomas. While NET and non-NEN cases had a comparable outcome, the diagnosis of NECs showed negative impact on disease-free interval compared to NETs and non-NENs (p = 0.0109). In conclusion, the current diagnostic classification of Br-NENs needs further adjustments regarding morphological and immunohistochemical criteria to increase the diagnostic reproducibility among pathologists. Our data suggest that, apart from high-grade small- and large-cell NECs, Br-NENs behave like non-NEN breast carcinomas and should be managed similarly

A weak characterization of slow variables in stochastic dynamical systems

We present a novel characterization of slow variables for continuous Markov processes that provably preserve the slow timescales. These slow variables are known as reaction coordinates in molecular dynamical applications, where they play a key role in system analysis and coarse graining. The defining characteristics of these slow variables is that they parametrize a so-called transition manifold, a low-dimensional manifold in a certain density function space that emerges with progressive equilibration of the system's fast variables. The existence of said manifold was previously predicted for certain classes of metastable and slow-fast systems. However, in the original work, the existence of the manifold hinges on the pointwise convergence of the system's transition density functions towards it. We show in this work that a convergence in average with respect to the system's stationary measure is sufficient to yield reaction coordinates with the same key qualities. This allows one to accurately predict the timescale preservation in systems where the old theory is not applicable or would give overly pessimistic results. Moreover, the new characterization is still constructive, in that it allows for the algorithmic identification of a good slow variable. The improved characterization, the error prediction and the variable construction are demonstrated by a small metastable system

Stabilizing versus destabilizing the microtubules: A double-edge sword for an effective cancer treatment option?

Microtubules are dynamic and structural cellular components involved in several cell functions, including cell shape, motility, and intracellular trafficking. In proliferating cells, they are essential components in the division process through the formation of the mitotic spindle. As a result of these functions, tubulin and microtubules are targets for anticancer agents. Microtubule-targeting agents can be divided into two groups: microtubule-stabilizing, and microtubule-destabilizing agents. The former bind to the tubulin polymer and stabilize microtubules, while the latter bind to the tubulin dimers and destabilize microtubules. Alteration of tubulin-microtubule equilibrium determines the disruption of the mitotic spindle, halting the cell cycle at the metaphase-anaphase transition and, eventually, resulting in cell death. Clinical application of earlier microtubule inhibitors, however, unfortunately showed several limits, such as neurological and bone marrow toxicity and the emergence of drug-resistant tumor cells. Here we review several natural and synthetic microtubule-targeting agents, which showed antitumor activity and increased efficacy in comparison to traditional drugs in various preclinical and clinical studies. Cryptophycins, combretastatins, ombrabulin, soblidotin, D-24851, epothilones and discodermolide were used in clinical trials. Some of them showed antiangiogenic and antivascular activity and others showed the ability to overcome multidrug resistance, supporting their possible use in chemotherapy

ACTH-producing tumorlets and carcinoids of the lung: clinico-pathologic study of 63 cases and review of the literature.

Adrenocorticotropic hormone (ACTH)-secreting lung carcinoids represent the principal cause of ectopic Cushing syndrome, but the prevalence of ACTH expression and the association between ACTH production and Cushing syndrome in lung carcinoids have scarcely been investigated. In addition, available information on the prognostic meaning of ACTH production is controversial. The aims of this multicentric retrospective study, also including a review of the literature, were to describe the clinico-pathologic features of ACTH-producing lung carcinoids, to assess recurrence and specific survival rates, and to evaluate potential prognostic factors. To identify ACTH production in 254 unselected and radically resected lung carcinoids, we used a double approach including RT-PCR (mRNA encoding for pro-opiomelanocortin) and immunohistochemistry (antibodies against ACTH and β-endorphin). Sixty-three (24.8%) tumors produced ACTH and 11 of them (17.4%), representing 4.3% of the whole series, were associated with Cushing syndrome. The median follow-up time was 71 months. The 10-year overall and specific survival rates were 88.5% and 98.2%, respectively, with difference neither between functioning and nonfunctioning tumors nor between ACTH-positive and ACTH-negative carcinoids. At univariate analysis, histological type (typical or atypical) and Ki67 index significantly correlated with tumor recurrence. The literature review identified 172 previously reported patients with functioning ACTH-secreting lung carcinoids, and the meta-analysis of survival showed that 92% of them were alive after a mean follow-up time of 50 months. Our results demonstrate that ACTH-producing lung carcinoids are not rare, are not always associated with Cushing syndrome, and do not represent an aggressive variant of lung carcinoid

Maximum Flux Transition Paths of Conformational Change

Given two metastable states A and B of a biomolecular system, the problem is to calculate the likely paths of the transition from A to B. Such a calculation is more informative and more manageable if done for a reduced set of collective variables chosen so that paths cluster in collective variable space. The computational task becomes that of computing the "center" of such a cluster. A good way to define the center employs the concept of a committor, whose value at a point in collective variable space is the probability that a trajectory at that point will reach B before A. The committor "foliates" the transition region into a set of isocommittors. The maximum flux transition path is defined as a path that crosses each isocommittor at a point which (locally) has the highest crossing rate of distinct reactive trajectories. (This path is different from that of the MaxFlux method of Huo and Straub.) It is argued that such a path is nearer to an ideal path than others that have been proposed with the possible exception of the finite-temperature string method path. To make the calculation tractable, three approximations are introduced, yielding a path that is the solution of a nonsingular two-point boundary-value problem. For such a problem, one can construct a simple and robust algorithm. One such algorithm and its performance is discussed.Comment: 7 figure