Constant chemical potential-quantum mechanical-molecular dynamics simulations of the graphene-electrolyte double layer.

We present the coupling of two frameworks-the pseudo-open boundary simulation method known as constant potential molecular dynamics simulations (CμMD), combined with quantum mechanics/molecular dynamics (QMMD) calculations-to describe the properties of graphene electrodes in contact with electrolytes. The resulting CμQMMD model was then applied to three ionic solutions (LiCl, NaCl, and KCl in water) at bulk solution concentrations ranging from 0.5 M to 6 M in contact with a charged graphene electrode.

Cleaving Method for Molecular Crystals and Its Application to Calculation of the Surface Free Energy of Crystalline β-d-Mannitol at Room Temperature.

Calculation of the surface free energy (SFE) is an important application of the thermodynamic integration (TI) methodology, which was mainly employed for atomic crystals (such as Lennard-Jones or metals). In this work, we present the calculation of the SFE of a molecular crystal using the cleaving technique which we implemented in the LAMMPS molecular dynamics package. We apply this methodology to a crystal of β-d-mannitol at room temperature and report the results for two types of force fields belonging to the GROMOS family: all atoms and united atoms.

Dynamically Polarizable Force Fields for Surface Simulations via Multi-output Classification Neural Networks.

We present a general procedure to introduce electronic polarization into classical Molecular Dynamics (MD) force fields using a Neural Network (NN) model. We apply this framework to the simulation of a solid-liquid interface where the polarization of the surface is essential to correctly capture the main features of the system. By introducing a multi-input, multi-output NN and treating the surface polarization as a discrete classification problem, we are able to obtain very good accuracy in terms of quality of predictions.

Shuttleworth equation: A molecular simulations perspective.

Even though the study of interfacial phenomena can be traced back to Laplace and was given a solid thermodynamic foundation by Gibbs, it appears that some concepts and relations among them are still causing some confusion and debates in the literature, particularly for interfaces involving solids. In particular, the definitions of the concepts of interfacial tension, free energy, and stress and the relationships between them sometimes lack clarity, and some authors even question their validity. So far, the debates about these relationships, in particular the Shuttleworth equation, have taken place within the framework of classical thermodynamics.

Current treatment of COVID-19 in renal patients: hope or hype?

To date the severe acute respiratory syndrome coronavirus 2 (SARS- CoV-2), known as COVID-19, is for clinicians the most difficult global therapeutic problem. In this landscape, the management of patients with chronic kidney disease, acute kidney injury or patients undergoing immunosuppressant therapies for kidney transplant or glomerular diseases, represent a clinical challenge for nephrologists, especially in patients with severe acute lung involvement. Therefore in this setting, due to the lack of anti-COVID treatment schedules, tailored management is mandatory to reduce the side effects, as consequence of impaired renal function and drugs interactions.

Tumor Homing Reactive Oxygen Species Nanoparticle for Enhanced Cancer Therapy.

Multifunctional nanoparticles that carry chemotherapeutic agents can be innovative anticancer therapeutic options owing to their tumor-targeting ability and high drug-loading capacity. However, the nonspecific release of toxic DNA-intercalating anticancer drugs from the nanoparticles has significant side effects on healthy cells surrounding the tumors. Herein, we report a tumor homing reactive oxygen species nanoparticle (THoR-NP) platform that is highly effective and selective for ablating malignant tumors.

Systematic derivation of hybrid coarse-grained models.

Molecular dynamics represents a key enabling technology for applications ranging from biology to the development of new materials. However, many real-world applications remain inaccessible to fully resolved simulations due to their unsustainable computational costs and must therefore rely on semiempirical coarse-grained models. Significant efforts have been devoted in the last decade towards improving the predictivity of these coarse-grained models and providing a rigorous justification of their use, through a combination of theoretical studies and data-driven approaches.

NIR Biosensing of Neurotransmitters in Stem Cell-Derived Neural Interface Using Advanced Core-Shell Upconversion Nanoparticles.

Nondestructive neurotransmitter detection and real-time monitoring of stem cell differentiation are both of great significance in the field of neurodegenerative disease and regenerative medicine. Although luminescent biosensing nanoprobes have been developed to address this need, they have intrinsic limitations such as autofluorescence, scattering, and phototoxicity. Upconversion nanoparticles (UCNPs) have gained increasing attention for various biomedical applications due to their high photostability, low auto-fluorescent background, and deep tissue penetration; however, UCNPs also suffer from low emission intensities due to undesirable energy migration pathways.

Overcoming Chemoresistance in Cancer via Combined MicroRNA Therapeutics with Anticancer Drugs Using Multifunctional Magnetic Core-Shell Nanoparticles.

In this study, we report the use of a multifunctional magnetic core-shell nanoparticle (MCNP), composed of a highly magnetic zinc-doped iron oxide (ZnFeO) core nanoparticle and a biocompatible mesoporous silica (mSi) shell, for the simultaneous delivery of let-7a microRNA (miRNA) and anticancer drugs (e.g., doxorubicin) to overcome chemoresistance in breast cancer.

The accuracy of ab initio calculations without ab initio calculations for charged systems: Kriging predictions of atomistic properties for ions in aqueous solutions.

Using the machine learning method kriging, we predict the energies of atoms in ion-water clusters, consisting of either Cl or Na surrounded by a number of water molecules (i.e., without NaCl interaction).

Synthesis, characterization and DNA interactions of [Pt(TPymT)Cl], the trinuclear platinum(II) complex of the TPymT ligand.

The triplatinum complex of the 2,4,6-Tris(2-pyrimidyl)-1,3,5-triazine ligand, PtTPymT hereafter, has been prepared and characterized for the first time. NMR studies point out that the three platinum(II) centers possess an identical coordination environment. The interactions of PtTPymT with DNA were explored in comparison to the free ligand.

Geometry Optimization with Machine Trained Topological Atoms.

The geometry optimization of a water molecule with a novel type of energy function called FFLUX is presented, which bypasses the traditional bonded potentials. Instead, topologically-partitioned atomic energies are trained by the machine learning method kriging to predict their IQA atomic energies for a previously unseen molecular geometry. Proof-of-concept that FFLUX's architecture is suitable for geometry optimization is rigorously demonstrated.

Local and global dynamics of multi-resolved polymer chains: Effects of the interactions atoms-beads on the dynamic of the chains.

We present a thorough analysis of the dynamic behaviour of hybrid atomistic/coarse-grained (CG) models of polymer melts. While structural properties are well preserved in a dual-resolved model, we show how the dynamic of the chains can be influenced by the simultaneous presence of atoms and beads. We show that although the polymer chains are long enough to exhibit reptation, the corresponding CG model is unable to capture the expected subdiffusive regimes and seems to still follow the Rouse dynamics.

FEREBUS: Highly parallelized engine for kriging training.

FFLUX is a novel force field based on quantum topological atoms, combining multipolar electrostatics with IQA intraatomic and interatomic energy terms. The program FEREBUS calculates the hyperparameters of models produced by the machine learning method kriging. Calculation of kriging hyperparameters (θ and p) requires the optimization of the concentrated log-likelihood L̂(θ,p).

Kriging atomic properties with a variable number of inputs.

A new force field called FFLUX uses the machine learning technique kriging to capture the link between the properties (energies and multipole moments) of topological atoms (i.e., output) and the coordinates of the surrounding atoms (i.

Incorporation of local structure into kriging models for the prediction of atomistic properties in the water decamer.

Machine learning algorithms have been demonstrated to predict atomistic properties approaching the accuracy of quantum chemical calculations at significantly less computational cost. Difficulties arise, however, when attempting to apply these techniques to large systems, or systems possessing excessive conformational freedom. In this article, the machine learning method kriging is applied to predict both the intra-atomic and interatomic energies, as well as the electrostatic multipole moments, of the atoms of a water molecule at the center of a 10 water molecule (decamer) cluster.

Optimization Algorithms in Optimal Predictions of Atomistic Properties by Kriging.

The machine learning method kriging is an attractive tool to construct next-generation force fields. Kriging can accurately predict atomistic properties, which involves optimization of the so-called concentrated log-likelihood function (i.e.

Stem cell-based gene therapy activated using magnetic hyperthermia to enhance the treatment of cancer.

Stem cell-based gene therapies, wherein stem cells are genetically engineered to express therapeutic molecules, have shown tremendous potential for cancer applications owing to their innate ability to home to tumors. However, traditional stem cell-based gene therapies are hampered by our current inability to control when the therapeutic genes are actually turned on, thereby resulting in detrimental side effects. Here, we report the novel application of magnetic core-shell nanoparticles for the dual purpose of delivering and activating a heat-inducible gene vector that encodes TNF-related apoptosis-inducing ligand (TRAIL) in adipose-derived mesenchymal stem cells (AD-MSCs).

Hybrid upconversion nanomaterials for optogenetic neuronal control.

Nanotechnology-based approaches offer the chemical control required to develop precision tools suitable for applications in neuroscience. We report a novel approach employing hybrid upconversion nanomaterials, combined with the photoresponsive ion channel channelrhodopsin-2 (ChR2), to achieve near-infrared light (NIR)-mediated optogenetic control of neuronal activity. Current optogenetic methodologies rely on using visible light (e.

Induction of stem-cell-derived functional neurons by NanoScript-based gene repression.

Even though gene repression is a powerful approach to exogenously regulate cellular behavior, developing a platform to effectively repress targeted genes, especially for stem-cell applications, remains elusive. Herein, we introduce a nanomaterial-based platform that is capable of mimicking the function of transcription repressor proteins to downregulate gene expression at the transcriptional level for enhancing stem-cell differentiation. We developed the "NanoScript" platform by integrating multiple gene repression molecules with a nanoparticle.

Surface and Structural Investigation of a MnOx Birnessite-Type Water Oxidation Catalyst Formed under Photocatalytic Conditions.

Catalytically active MnOx species have been reported to form in situ from various Mn-complexes during electrocatalytic and solution-based water oxidation when employing cerium(IV) ammonium ammonium nitrate (CAN) oxidant as a sacrificial reagent. The full structural characterization of these oxides may be complicated by the presence of support material and lack of a pure bulk phase. For the first time, we show that highly active MnOx catalysts form without supports in situ under photocatalytic conditions.

Solvent structuring and its effect on the polymer structure and processability: the case of water-acetone poly-ε-caprolactone mixtures.

One of the most common processes to produce polymer nanoparticles is the solvent-displacement method, in which the polymer is dissolved in a "good" solvent and the solution is then mixed with an "anti-solvent". The polymer processability is therefore determined by its structural and transport properties in solutions of the pure solvents and at the intermediate compositions. In this work, we focus on poly-ε-caprolactone (PCL) which is a biocompatible polymer that finds widespread application in the pharmaceutical and biomedical fields, performing full atomistic molecular dynamics simulations of one PCL chain of different molecular weight in a solution of pure acetone (good solvent), of pure water (antisolvent), and their mixtures.

An upconversion nanoparticle with orthogonal emissions using dual NIR excitations for controlled two-way photoswitching.

Developing multicolor upconversion nanoparticles (UCNPs) with the capability of regulating their emission wavelengths in the UV to visible range in response to external stimuli can offer more dynamic platforms for applications in high-resolution bioimaging, multicolor barcoding, and driving multiple important photochemical reactions, such as photoswitching. Here, we have rationally designed single-crystal core-shell-structured UCNPs which are capable of orthogonal UV and visible emissions in response to two distinct NIR excitations at 808 and 980 nm. The orthogonal excitation-emission properties of such UCNPs, as well as their ability to utilize low-power excitation, which attenuates any local heating from the lasers, endows the UCNPs with great potential for applications in materials and biological settings.

Core-shell nanoparticle-based peptide therapeutics and combined hyperthermia for enhanced cancer cell apoptosis.

Mitochondria-targeting peptides have garnered immense interest as potential chemotherapeutics in recent years. However, there is a clear need to develop strategies to overcome the critical limitations of peptides, such as poor solubility and the lack of target specificity, which impede their clinical applications. To this end, we report magnetic core-shell nanoparticle (MCNP)-mediated delivery of a mitochondria-targeting pro-apoptotic amphipathic tail-anchoring peptide (ATAP) to malignant brain and metastatic breast cancer cells.

A multiple time step scheme for multiresolved models of macromolecules.

In hybrid particle models where coarse-grained beads and atoms are used simultaneously, two clearly separate time scales are mixed. If such models are used in molecular dynamics simulations, a multiple time step (MTS) scheme can therefore be used. In this manuscript, we propose a simple MTS algorithm which approximates for a specific number of integration steps the slow coarse-grained bead-bead interactions with a Taylor series approximation while the atom-atom ones are integrated every time step.