Regulating Histone Deacetylases and Carbonic Anhydrases by Metal Complexes: A Potent Strategy for Treating Cancers.

Utilizing metal complexes to inhibit histone deacetylases (HDACs) and carbonic anhydrases (CAs) highlights their therapeutic potential, particularly in anticancer strategies. The metal complexes, with their unique three-dimensional structures, fit adequately into the active sites of the enzymes, not only improving selectivity but also providing facile coordination with amino acid residues to enhance their inhibitory ability. This review emphasizes the role of metal complexes in the selective inhibition of HDACs and CAs along with details of their mechanism of action.

Heterogeneous Structure of Ni-Mo Nanoalloys Decorated on MoO for an Efficient Hydrogen Evolution Reaction Using Hydrogen Spillover.

Herein, a heterogeneous structure of Ni-Mo catalyst comprising NiMo nanoalloys decorated on a MoO matrix via electrodeposition is introduced. This catalyst exhibits remarkable hydrogen evolution reaction (HER) activity across a range of pH conditions. The heterogeneous Ni-Mo catalyst showed low overpotentials only of 24 and 86, 21 and 60, and 37 and 168 mV to produce a current density of 10 and 100 mA cm (η and η) in alkaline, acidic, and neutral media, respectively, which represents one of the most active catalysts for the HER.

Natural Products as Regulators against Matrix Metalloproteinases for the Treatment of Cancer.

Cancers are currently the major cause of mortality in the world. According to previous studies, matrix metalloproteinases (MMPs) have an impact on tumor cell proliferation, which could lead to the onset and progression of cancers. Therefore, regulating the expression and activity of MMPs, especially MMP-2 and MMP-9, could be a promising strategy to reduce the risk of cancers.

Solar-Powered AEM Electrolyzer via PGM-Free (Oxy)hydroxide Anode with Solar to Hydrogen Conversion Efficiency of 12.44.

Water electrolyzers powered by renewable energy are emerging as clean and sustainable technology for producing hydrogen without carbon emissions. Specifically, anion exchange membrane (AEM) electrolyzers utilizing non-platinum group metal (non-PGM) catalysts have garnered attention as a cost-effective method for hydrogen production, especially when integrated with solar cells. Nonetheless, the progress of such integrated systems is hindered by inadequate water electrolysis efficiency, primarily caused by poor oxygen evolution reaction (OER) electrodes.

Chemical Insights into Topical Agents in Intraocular Pressure Management: From Glaucoma Etiopathology to Therapeutic Approaches.

Glaucoma encompasses a group of optic neuropathies characterized by complex and often elusive etiopathology, involvihttng neurodegeneration of the optic nerve in conjunction with abnormal intraocular pressure (IOP). Currently, there is no cure for glaucoma, and treatment strategies primarily aim to halt disease progression by managing IOP. This review delves into the etiopathology, diagnostic methods, and treatment approaches for glaucoma, with a special focus on IOP management.

Vitamin A, D, E, and K as Matrix Metalloproteinase-2/9 Regulators That Affect Expression and Enzymatic Activity.

Fat-soluble vitamins (vitamin A, D, E, and K) assume a pivotal role in maintaining human homeostasis by virtue of their enzymatic functions. The daily inclusion of these vitamins is imperative to the upkeep of various physiological processes including vision, bone health, immunity, and protection against oxidative stress. Current research highlights fat-soluble vitamins as potential therapeutics for human diseases, especially cancer.

Polymer-Based Thermally Stable Chemiresistive Sensor for Real-Time Monitoring of NO Gas Emission.

We present a thermally stable, mechanically compliant, and sensitive polymer-based NO gas sensor design. Interconnected nanoscale morphology driven from spinodal decomposition between conjugated polymers tethered with polar side chains and thermally stable matrix polymers offers judicious design of NO-sensitive and thermally tolerant thin films. The resulting chemiresitive sensors exhibit stable NO sensing even at 170 °C over 6 h.

Machine learning-enabled exploration of the electrochemical stability of real-scale metallic nanoparticles.

Surface Pourbaix diagrams are critical to understanding the stability of nanomaterials in electrochemical environments. Their construction based on density functional theory is, however, prohibitively expensive for real-scale systems, such as several nanometer-size nanoparticles (NPs). Herein, with the aim of accelerating the accurate prediction of adsorption energies, we developed a bond-type embedded crystal graph convolutional neural network (BE-CGCNN) model in which four bonding types were treated differently.

Metal Complexes as Promising Matrix Metalloproteinases Regulators.

Nowadays, cancers and dementia, such as Alzheimer's disease, are the most fatal causes of death. Many studies tried to understand the pathogenesis of those diseases clearly and develop a promising way to treat the diseases. Matrix metalloproteinases (MMPs) have been reported to be involved in the pathology of cancers and AD through tumor cell movement and amyloid degradation.

Gold single-atoms confined at the CeO-TiOinterfaces with enhanced low-temperature activity toward CO oxidation.

We use CeO-TiOhetero-interfaces generated on the surface of CeO-TiOhybrid oxide supporting powders to stabilize Au single-atoms (SAs) with excellent low-temperature activity toward CO oxidation. Based on intriguing density functional theory calculation results on the preferential formation of Au-SAs at the CeO-TiOinterfaces and the high activity of Au-SAs toward the Mars-van Krevelen type CO oxidation, we synthesized a Au/CeO-TiO(ACT) catalyst with 0.05 wt.

Interpretable Deep Learning Model for Analyzing the Relationship between the Electronic Structure and Chemisorption Property.

The use of machine learning (ML) is exploding in materials science as a result of its high predictive performance of material properties. Tremendous trainable parameters are required to build an outperforming predictive model, which makes it impossible to retrace how the model predicts well. However, it is necessary to develop a ML model that can extract human-understandable knowledge while maintaining performance for a universal application to materials science.

Bioactive Secondary Metabolites Isolated from the Antarctic Lichen Himantormia lugubris.

Two new depsidones, himantormiones A and B (1 and 2) were isolated and identified from the Antarctic lichen, Himantormia lugubris (Parmeliaceae), with seven known compounds (3-9). The structures of two new compounds (1 and 2) were determined by means of spectroscopic analyses, including 1D and 2D NMR and HR-MS. The isolated compounds were tested for antimicrobial and cytotoxic activities, where himantormione B (2) exhibited inhibitory effect against Staphylococcus aureus with the IC value of 7.

Modified Dynamic Physical Model of Valence Change Mechanism Memristors.

Valence change-type resistance switching behaviors in oxides can be understood by well-established physical models describing the field-driven oxygen vacancy distribution change. In those models, electroformed residual oxygen vacancy filaments are crucial as they work as an electric field concentrator and limit the oxygen vacancy movement along the vertical direction. Therefore, their movement outward by diffusion is negligible.

Controlled Removal of Surfactants from Double-Walled Carbon Nanotubes for Stronger p-Doping Effect and Its Demonstration in Perovskite Solar Cells.

Double-walled carbon nanotubes (DWNTs) have shown potential as promising alternatives to conventional transparent electrodes owing to their solution processability as well as high conductivity and transparency. However, their DC to optical conductivity ratio is limited by the surrounding surfactants that prevent the p-doping of the DWNTs. To maximize the doping effectiveness, the surfactants are removed from the DWNTs, with negligible damage to the nanotubes, by calcination in an Ar atmosphere.

Monodisperse Carbon Nitride Nanosheets as Multifunctional Additives for Efficient and Durable Perovskite Solar Cells.

Two-dimensional (2D) materials are promising components for defect passivation of metal halide perovskites. Unfortunately, commonly used polydisperse liquid-exfoliated 2D materials generally suffer from heterogeneous structures and properties while incorporated into perovskite films. We introduce monodisperse multifunctional 2D crystalline carbon nitride, poly(triazine imide) (PTI), as an effective defect passivation agent in perovskite films via typical solution processing.

Redox-Active Metal Ions and Amyloid-Degrading Enzymes in Alzheimer's Disease.

Redox-active metal ions, Cu(I/II) and Fe(II/III), are essential biological molecules for the normal functioning of the brain, including oxidative metabolism, synaptic plasticity, myelination, and generation of neurotransmitters. Dyshomeostasis of these redox-active metal ions in the brain could cause Alzheimer's disease (AD). Thus, regulating the levels of Cu(I/II) and Fe(II/III) is necessary for normal brain function.

Impact of sphingosine and acetylsphingosines on the aggregation and toxicity of metal-free and metal-treated amyloid-β.

Pathophysiological shifts in the cerebral levels of sphingolipids in Alzheimer's disease (AD) patients suggest a link between sphingolipid metabolism and the disease pathology. Sphingosine (), a structural backbone of sphingolipids, is an amphiphilic molecule that is able to undergo aggregation into micelles and micellar aggregates. Considering its structural properties and cellular localization, we hypothesized that potentially interacts with amyloid-β (Aβ) and metal ions that are found as pathological components in AD-affected brains, with manifesting its reactivity towards metal-free Aβ and metal-bound Aβ (metal-Aβ).

In Silico High-Throughput Screening of Ag-Based Electrocatalysts for Anion-Exchange Membrane Fuel Cells.

The alkaline environment in anion-exchange membrane fuel cells allows the use of Pt-free electrocatalysts, thus reducing the system cost. We performed a theoretical high-throughput study of various low-cost Ag-based oxygen reduction reaction anode electrocatalysts and assessed their catalytic performance using density functional theory. From the Materials Project database, a total of 106 binary Ag alloys were investigated by estimating their heat of formation, dissolution potential, and overpotential on low-index surfaces.

Multiple reactivities of flavonoids towards pathological elements in Alzheimer's disease: structure-activity relationship.

Amyloid-β (Aβ) accumulation, metal ion dyshomeostasis, oxidative stress, and cholinergic deficit are four major characteristics of Alzheimer's disease (AD). Herein, we report the reactivities of 12 flavonoids against four pathogenic elements of AD: metal-free and metal-bound Aβ, free radicals, and acetylcholinesterase. A series of 12 flavonoids was selected based on the molecular structures that are responsible for multiple reactivities including hydroxyl substitution and transfer of the B ring from C2 to C3.

Accelerated mapping of electronic density of states patterns of metallic nanoparticles via machine-learning.

Within first-principles density functional theory (DFT) frameworks, it is challenging to predict the electronic structures of nanoparticles (NPs) accurately but fast. Herein, a machine-learning architecture is proposed to rapidly but reasonably predict electronic density of states (DOS) patterns of metallic NPs via a combination of principal component analysis (PCA) and the crystal graph convolutional neural network (CGCNN). With the PCA, a mathematically high-dimensional DOS image can be converted to a low-dimensional vector.

Aconitine Neurotoxicity According to Administration Methods.

We evaluated the toxic effects of aconitine on the human nervous system and its associated factors, and the general clinical characteristics of patients who visited the emergency room due to aconitine intoxication between 2008 and 2017. We also analyzed the differences related to aconitine processing and administration methods (oral pill, boiled in water, and alcohol-soaked), and the clinical characteristics of consciousness deterioration and neurological symptoms. Of the 41 patients who visited the hospital due to aconitine intoxication, 23 (56.

Multidimensional TiCT MXene Architectures Interfacial Electrochemical Self-Assembly.

An effective pathway to build macroscopic scale functional architectures bearing diverse structural dimensions is one of the critical challenges in the two-dimensional (2D) MXene research area. Unfortunately, assembling MXene without adhesive binder is largely limited due to its innate brittle nature and the relatively weak inter-flake van der Waals contact, in contrast to other mechanically compliant 2D materials such as graphene. Herein, an electrochemical self-assembly of pure TiCT MXenes is presented for functional multidimensional MXene structures, effectively driven by layer-by-layer spontaneous interfacial reduction at metal template surfaces and subsequent defunctionalization.

Hetero-Dimensional 2D TiCT MXene and 1D Graphene Nanoribbon Hybrids for Machine Learning-Assisted Pressure Sensors.

Hybridization of low-dimensional components with diverse geometrical dimensions should offer an opportunity for the discovery of synergistic nanocomposite structures. In this regard, how to establish a reliable interfacial interaction is the key requirement for the successful integration of geometrically different components. Here, we present 1D/2D heterodimensional hybrids via dopant induced hybridization of 2D TiCT MXene with 1D nitrogen-doped graphene nanoribbon.

Reducing Time to Discovery: Materials and Molecular Modeling, Imaging, Informatics, and Integration.

Multiscale and multimodal imaging of material structures and properties provides solid ground on which materials theory and design can flourish. Recently, KAIST announced 10 flagship research fields, which include KAIST Materials Revolution: Materials and Molecular Modeling, Imaging, Informatics and Integration (M3I3). The M3I3 initiative aims to reduce the time for the discovery, design and development of materials based on elucidating multiscale processing-structure-property relationship and materials hierarchy, which are to be quantified and understood through a combination of machine learning and scientific insights.