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. Various studies have tried to investigate and understand the pathophysiology of cancers to suggest potent treatments. In this review, we summarize how natural products from marine organisms and plants, as regulators of MMP-2 and MMP-9 expression and enzymatic activity, can operate as potent anticancer agents.

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. To address this issue, a NiFeCo─OOH has developed as an OER electrocatalyst and successfully demonstrated its efficacy in an AEM electrolyzer, which is powered by renewable electricity and integrated with a silicon solar cell.

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. We discuss a range of active pharmaceutical ingredients used in glaucoma therapy, emphasizing their chemical structure, pharmacological action, therapeutic effectiveness, and safety/tolerability profiles. Notably, most of these therapeutic agents are administered as topical formulations, a critical aspect considering patient compliance and drug delivery efficiency. The classes of glaucoma therapeutics covered in this review include prostaglandin analogs, beta blockers, alpha agonists, carbonic anhydrase inhibitors, Rho kinase inhibitors, and miotic (cholinergic) agents. This comprehensive overview highlights the importance of topical administration in glaucoma treatment, offering insights into the current state and future directions of pharmacological management in glaucoma.

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. Fat-soluble vitamins exert their therapeutic effects through multiple pathways, including regulation of matrix metalloproteinases' (MMPs) expression and enzymatic activity. As MMPs have been reported to be involved in the pathology of various diseases, such as cancers, cardiovascular diseases, and neurological disorders, regulating the expression and/or activity of MMPs could be considered as a potent therapeutic strategy. Here, we summarize the properties of fat-soluble vitamins and their potential as promising candidates capable of effectively modulating MMPs through multiple pathways to treat human diseases.

Polymer-Based Thermally Stable Chemiresistive Sensor for Real-Time Monitoring of NO2 Gas Emission.

We present a thermally stable, mechanically compliant, and sensitive polymer-based NO2 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 NO2-sensitive and thermally tolerant thin films. The resulting chemiresitive sensors exhibit stable NO2 sensing even at 170 °C over 6 h. Controlling the density of polar side chains along conjugated polymer backbone enables optimal design for coupling high NO2 sensitivity, selectivity, and thermal stability of polymer sensors. Lastly, thermally stable films are used to implement chemiresistive sensors onto flexible and heat-resistant substrates and demonstrate a reliable gas sensing response even after 500 bending cycles at 170 °C. Such unprecedented sensor performance as well as environmental stability are promising for real-time monitoring of gas emission from vehicles and industrial chemical processes.

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. Owing to the enhanced accuracy of the bond-type embedding approach, we demonstrate the construction of reliable Pourbaix diagrams for very large-size NPs involving up to 6525 atoms (approximately 4.8 nm in diameter), which enables the exploration of electrochemical stability over various NP sizes and shapes. BE-CGCNN-based Pourbaix diagrams well reproduce the experimental observations with increasing NP size. This work suggests a method for accelerated Pourbaix diagram construction for real-scale and arbitrarily shaped NPs, which would significantly open up an avenue for electrochemical stability studies.

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. Therefore, control of the levels and actions of MMPs, especially MMP-2 and MMP-9, is necessary to care for and/or cure cancer and AD. Various molecules have been examined for their potential application as regulators of MMPs expression and activity. Among the molecules, multiple metal complexes have shown advantages, including simple synthesis, less toxicity and specificity toward MMPs in cancer cells or in the brain. In this review, we summarize the recent studies and knowledge of metal complexes (e.g., Pt-, Ru-, Au-, Fe-, Cu-, Ni-, Zn-, and Sn-complexes) targeting MMPs and their potentials for treating and/or caring the most fatal human diseases, cancers and AD.

Gold single-atoms confined at the CeOx-TiO2interfaces with enhanced low-temperature activity toward CO oxidation.

We use CeOx-TiO2hetero-interfaces generated on the surface of CeOx-TiO2hybrid 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 CeOx-TiO2interfaces and the high activity of Au-SAs toward the Mars-van Krevelen type CO oxidation, we synthesized a Au/CeOx-TiO2(ACT) catalyst with 0.05 wt.% of Au content. The Au-SAs stabilized at the CeOx-TiO2interfaces by electronic coupling between Au and Ce showed improved low-temperature CO oxidation activity than the conventional Au/TiO2control group catalyst. However, the light-off profile of ACT showed that the early activated Au-SAs are not vigorously participating in CO oxidation. The large portion of the positive effect on the overall catalytic activity from the low activation energy barrier of ACT was retarded by the negative impact from the decreasing active site density at high temperatures. We anticipate that the low-temperature activity and high-temperature stability of Au-SAs that stand against each other can be optimized by controlling the electronic coupling strength between Au-SAs and oxide clusters at the Au-oxide-TiO2interfaces. Our results show that atomic-precision interface modulation could fine-tune the catalytic activity and stability of Au-SAs.

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. In this study, we developed a deep learning model that can interpret the correlation between surface electronic density of states (DOSs) of materials and their chemisorption property using the attention mechanism that provides which part of DOS is important to predict adsorption energies. The developed model constructs the well-known d-band center theory without any prior knowledge. This work shows that human-interpretable knowledge can be extracted from complex ML models.

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. However, this situation may not be applicable in the electroforming-free system, where the field-driven movement is less prominent, and the isotropic oxygen vacancy diffusion by concentration gradient is more significant, which has not been given much consideration in the conventional model. Here, we propose a modified physical model that considers the change in the oxygen vacancies' charged state depending on their concentrations and the resulting change in diffusivity during switching to interpret the electroforming-free device behaviors. The model suggests formation of an hourglass-shaped filament constituting a lower concentration of oxygen vacancies due to the fluid oxygen diffusion in the thin oxide. Consequently, the proposed model can explain the electroforming-free device behaviors, including the retention failure mechanism, and suggest an optimized filament configuration for improved retention characteristics. The proposed model can plausibly explain both the electroformed and the electroforming-free devices. Therefore, it can be a standard model for valence change memristors.

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. The effective removal of the surfactants is characterized by various analyses, and the results show that the optimal calcination temperature is 400 °C. The conductivity of the DWNTs films improves when doped by triflic acid. While the conductivity increase of the surfactants-wrapped DWNT films is 31.9%, the conductivity increase of the surfactants-removed DWNT is found to be 59.7%. Using the surfactants-removed, p-doped, solution-processed transparent electrodes, inverted-type perovskite solar cells are fabricated, resulting in a power conversion efficiency of 17.7% without hysteresis. This work advances the application of DWNTs in transparent conductors, as the efficiency obtained is the highest value achieved to date for carbon nanotube electrode-based perovskite solar cells and solution-processable transparent electrode-based solar cells.

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. Incorporation of PTI into perovskite film can be readily attained by simple solution mixing of PTI dispersions with perovskite precursor solutions, resulting in the highly selective distribution of PTI localized at the defective crystal grain boundaries and layer interfaces in the functional perovskite layer. Several chemical, optical, and electronic characterizations, in conjunction with density functional theory calculations, reveal multiple beneficial roles from PTI: passivation of undercoordinated organic cations at the surface of perovskite crystal, suppression of ion migration by blocking diffusion channels, and prevention of hole quenching at perovskite/SnO2 interfaces. Consequently, a noticeably improved power conversion efficiency is achieved in perovskite solar cells, accompanied with promoted stability under humid air and thermal stress. Our strategy highlights the potential of judiciously designed 2D materials as a simple-to-implement material for various optoelectronic devices, including solar cells, based on hybrid perovskites.

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. To control the amounts of metal ions in the brain and understand the involvement of Cu(I/II) and Fe(II/III) in the pathogenesis of AD, many chemical agents have been developed. In addition, since toxic aggregates of amyloid-ß (Aß) have been proposed as one of the major causes of the disease, the mechanism of clearing Aß is also required to be investigated to reveal the etiology of AD clearly. Multiple metalloenzymes (e.g., neprilysin, insulin-degrading enzyme, and ADAM10) have been reported to have an important role in the degradation of Aß in the brain. These amyloid degrading enzymes (ADE) could interact with redox-active metal ions and affect the pathogenesis of AD. In this review, we introduce and summarize the roles, distributions, and transportations of Cu(I/II) and Fe(II/III), along with previously invented chelators, and the structures and functions of ADE in the brain, as well as their interrelationships.

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 (56.1%) were female, and most were older. Aconitine was mainly used for pain control (28 patients, 68.3%) and taken as oral pills (19 patients, 46%). The patients showed a single symptom or a combination of symptoms; neurological symptoms were the most common (21 patients). All patients who took aconitine after processing with alcohol showed neurological symptoms and a higher prevalence of consciousness deterioration. Neurological symptoms occurred most frequently in patients with aconitine intoxication. Although aconitine intoxication presents with various symptoms, its prognosis may vary with the processing method and prevalence of consciousness deterioration during the early stages. Therefore, the administration method and accompanying symptoms should be comprehensively investigated in patients who have taken aconitine to facilitate prompt and effective treatment and better prognoses.

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. We confirmed that EuAg5, BaAg5, and SrAg5 have higher catalytic activities and durabilities than pure Ag. By following the chemical trend of the results, we further proposed LaAg5 and PrAg5, which were not included in the database, as promising candidates. All candidates are in the space group P6/mmm and contain alkaline earth metal or lanthanide elements.

Multidimensional Ti3C2Tx MXene Architectures via 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 Ti3C2Tx MXenes is presented for functional multidimensional MXene structures, effectively driven by layer-by-layer spontaneous interfacial reduction at metal template surfaces and subsequent defunctionalization. A three-dimensional open porous aerogel as well as 2D highly stacked thin film structures could be readily obtained in this approach, along with largely enhanced electrical properties induced by spontaneous removal of charge-trapping oxygen functional groups. Accordingly, supercapacitors and electromagnetic interference shielding films based on the multidimensional assembly demonstrate excellent performances.

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. The CGCNN plays a key role in reflecting the effects of local atomic structures on the DOS patterns of NPs with only a few of material features that are easily extracted from a periodic table. The PCA-CGCNN model is applicable for all pure and bimetallic NPs, in which a handful DOS training sets that are easily obtained with the typical DFT method are considered. The PCA-CGCNN model predicts the R2 value to be 0.85 or higher for Au pure NPs and 0.77 or higher for Au@Pt core@shell bimetallic NPs, respectively, in which the values are for the test sets. Although the PCA-CGCNN method showed a small loss of accuracy when compared with DFT calculations, the prediction time takes just ~ 160 s irrespective of the NP size in contrast to DFT method, for example, 13,000 times faster than the DFT method for Pt147. Our approach not only can be immediately applied to predict electronic structures of actual nanometer scaled NPs to be experimentally synthesized, but also be used to explore correlations between atomic structures and other spectrum image data of the materials (e.g., X-ray diffraction, X-ray photoelectron spectroscopy, and Raman spectroscopy).

Hetero-Dimensional 2D Ti3C2Tx 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 Ti3C2Tx MXene with 1D nitrogen-doped graphene nanoribbon. Edge abundant nanoribbon structures allow a high level nitrogen doping (∼6.8 at%), desirable for the strong coordination interaction with Ti3C2Tx MXene surface. For piezoresistive pressure sensor application, strong adhesion between the conductive layers and at the conductive layer/elastomer interface significantly diminishes the sensing hysteresis down to 1.33% and enhances the sensing stability up to 10 000 cycles at high pressure (100 kPa). Moreover, large-area pressure sensor array reveals a high potential for smart seat cushion-based posture monitoring application with high accuracy (>95%) by exploiting machine learning algorithm.

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. In this review, we begin by introducing recent progress on related initiatives around the globe, such as the Materials Genome Initiative (U.S.), Materials Informatics (U.S.), the Materials Project (U.S.), the Open Quantum Materials Database (U.S.), Materials Research by Information Integration Initiative (Japan), Novel Materials Discovery (E.U.), the NOMAD repository (E.U.), Materials Scientific Data Sharing Network (China), Vom Materials Zur Innovation (Germany), and Creative Materials Discovery (Korea), and discuss the role of multiscale materials and molecular imaging combined with machine learning in realizing the vision of M3I3. Specifically, microscopies using photons, electrons, and physical probes will be revisited with a focus on the multiscale structural hierarchy, as well as structure-property relationships. Additionally, data mining from the literature combined with machine learning will be shown to be more efficient in finding the future direction of materials structures with improved properties than the classical approach. Examples of materials for applications in energy and information will be reviewed and discussed. A case study on the development of a Ni-Co-Mn cathode materials illustrates M3I3's approach to creating libraries of multiscale structure-property-processing relationships. We end with a future outlook toward recent developments in the field of M3I3.

Fluoroquinolone Can Be an Effective Treatment Option for Acute Pyelonephritis When the Minimum Inhibitory Concentration of Levofloxacin for the Causative Escherichia coli Is ≤16 mg/L.

The purpose of this study was to determine whether the fluoroquinolone (FQ) minimum inhibitory concentration (MIC) for the causative agent Escherichia coli influences the clinical response of FQ treatment at 72 h in patients with community-acquired acute pyelonephritis (CA-APN). We prospectively collected the clinical data of women with CA-APN from 11 university hospitals from March 2010 to February 2012 as well as E. coli isolates from the urine or blood. In total, 78 patients included in this study received FQ during the initial 72 h, and the causative E. coli was detected. The clinical response at 72 h was significantly higher in patients with a levofloxacin MIC ≤ 16 mg/L than in those with an MIC > 16 mg/L (70.4% vs. 28.6%, p = 0.038). No difference was observed in clinical response at 72 h based on ciprofloxacin MIC. To summarize, FQ can be an effective treatment option for CA-APN when levofloxacin MIC against E. coli is ≤16 mg/L.