Cachexia: Pathophysiology and Ghrelin Liposomes for Nose-to-Brain Delivery.

Cachexia, a severe multifactorial condition that is underestimated and unrecognized in patients, is characterized by continuous muscle mass loss that leads to progressive functional impairment, while nutritional support cannot completely reverse this clinical condition. There is a strong need for more effective and targeted therapies for cachexia patients. There is a need for drugs that act on cachexia as a distinct and treatable condition to prevent or reverse excess catabolism and inflammation. Due to ghrelin properties, it has been studied in the cachexia and other treatments in a growing number of works. However, in the body, exogenous ghrelin is subject to very rapid degradation. In this context, the intranasal release of ghrelin-loaded liposomes to cross the blood-brain barrier and the release of the drug into the central nervous system may be a promising alternative to improve its bioavailability. The administration of nose-to-brain liposomes for the management of cachexia was addressed only in a limited number of published works. This review focuses on the discussion of the pathophysiology of cachexia, synthesis and physiological effects of ghrelin and the potential treatment of the diseased using ghrelin-loaded liposomes through the nose-to-brain route.

Analysis of the adaptive multilevel splitting method on the isomerization of alanine dipeptide.

We apply the adaptive multilevel splitting (AMS) method to the C eq → C ax transition of alanine dipeptide in vacuum. Some properties of the algorithm are numerically illustrated, such as the unbiasedness of the probability estimator and the robustness of the method with respect to the reaction coordinate. We also calculate the transition time obtained via the probability estimator, using an appropriate ensemble of initial conditions. Finally, we show how the AMS method can be used to compute an approximation of the committor function. © 2019 Wiley Periodicals, Inc.

Vibrational and electronic spectroscopy of neutral antimony coordination compounds of the 1,3-dithiole-2-thione-4,5-dithiolate (dmit).

The S 1s X-ray absorption near edge structure (XANES) and X-ray photoelectron spectra (XPS) of the neutral complexes [SbL(dmit)] (L = Br or I; dmit =1,3-dithiole-2-thione-4,5-dithiolate) have been measured using tunable synchrotron radiation. The valence shell electronic excitation by ultraviolet-visible (UV-vis) spectroscopy and the infrared vibrational spectra are presented and analyzed. The UV-vis results lead to an assignment of bands at 400 nm as π(Sm) → π*(C═S), where S(m) is the thiolate sulfur. The corresponding S 1s → π*(C═S) transition was identified at 2468.3 eV. Ab initio calculations, within the improved virtual orbital (IVO) method, carried out with the GSCF3 program, were applied to establish a complete and accurate spectral assignment. It has been the first attempt to apply such methodology for dmit coordination compounds, and very consistent results were obtained.

Human Learning for Molecular Simulations: The Collective Variables Dashboard in VMD.

The Collective Variables Dashboard is a software tool for real-time, seamless exploration of molecular structures and trajectories in a customizable space of collective variables. The Dashboard arises from the integration of the Collective Variables Module (also known as Colvars) with the visualization software VMD, augmented with a fully discoverable graphical interface offering interactive workflows for the design and analysis of collective variables. Typical use cases include a priori design of collective variables for enhanced sampling and free energy simulations as well as analysis of any type of simulation or collection of structures in a collective variable space. A combination of those cases commonly occurs when preliminary simulations, biased or unbiased, reveal that an optimized set of collective variables is necessary to improve sampling in further simulations. Then the Dashboard provides an efficient way to intuitively explore the space of likely collective variables, validate them on existing data, and use the resulting collective variable definitions directly in further biased simulations using the Collective Variables Module. Visualization of biasing energies and forces is proposed to help analyze or plan biased simulations. We illustrate the use of the Dashboard on two applications: discovering coordinates to describe ligand unbinding from a protein binding site and designing volume-based variables to bias the hydration of a transmembrane pore.