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.

The impact of COVID-19 pandemic on revisits to emergency department.

AIM: This study presents the impact of COVID-19 on revisits to the emergency department comparing revisit rates and characteristics between the pre-COVID-19 and COVID-19 periods. METHODS: This multi-center retrospective study included patients over 18 years of age who visited emergency departments during the pre-COVID-19 period and the COVID-19 pandemic. The revisit rates were analyzed according to five age groups; 18-34, 35-49, 50-64, 65-79, and ≥ 80 years, and three revisit time intervals; 3, 9, and 30 days. Also, we compared the diagnosis and disposition at revisit between the study periods. RESULTS: The revisit rates increased with age in both study periods and the revisit rates among all age groups were higher in the COVID-19 period. The proportion of infectious and respiratory diseases decreased during the COVID-19 period. The ICU admission rate and mortality at the revisit among patients aged ≥ 80 years were lower in the COVID-19 period than in the pre-COVID-19 period. CONCLUSION: The revisit rates increased with age in both study periods and there were several changes in the diagnosis and disposition at the revisit in the COVID-19 period.

A Comparison of Emergency Department Revisit Rates of Pediatric Patients between Pre-COVID-19 and COVID-19 Periods.

Unscheduled revisits to emergency departments (EDs) are important because they indicate the quality of emergency care. However, the characteristics of pediatric patients visiting EDs changed during the coronavirus disease (COVID-19) pandemic, and these changes may have affected their revisit patterns. Therefore, we aimed to compare the ED revisit patterns of pediatric patients between the pre-COVID-19 and COVID-19 periods. This retrospective multicenter study included patients aged below 18 years who visited the ED in the pre-COVID-19 and COVID-19 periods. ED revisit rates were analyzed using five age groups and three visit-revisit intervals. In the pre-COVID-19 period, the revisit rates decreased with increasing age. In the COVID-19 period, the revisit rates were the lowest for the group aged 4-6 years, and the rates increased for those aged ≥7 years. In conclusion, there were changes in the patterns of revisit rates of pediatric patients according to age between the pre-COVID-19 and COVID-19 periods. Therefore, it is necessary to identify the reasons for revisits according to age and establish strategies to reduce the revisit rates of pediatric patients.

Hyaluronic microparticle-based biomimetic artificial neighbors of cells for three-dimensional cell culture.

3D spheroids, which have the potential to bridge the gap between 2D cell culture and native tissue, are used as tissue models in many applications, particularly in cancer, stem cell, and pharmaceutical research. A considerable amount of effort has focused on the development of more relevant physiological models. However, spheroids still have limitations in that they cannot replicate the components and structure of the ECM in the natural environment. In this study, we proposed new concept of scaffold-based techniques for the generation of spheroids. Spheroids were successfully generated by single cell or small number of aggregated cells between HA particles. The size of each spheroid was uniform, a necrotic core didn't form, and the system showed high viability. The expression levels of the proteins and genes required to maintain cell-specific functions increased. Thus, our system provides more physiologically relevant models and could be applied to regenerative medicine or drug screening.

Clinical Characteristics Associated with Physical Violence in the Elderly: A Retrospective Multicenter Analysis.

BACKGROUND: Elder abuse is predicted to increase with the rapid population ageing in many countries. Violent injury is influenced by individual factors as well as interpersonal and social relationships, with different manifestations based on changes in the socioeconomic position of older adults. We comparatively investigated the clinical and injury characteristics of physical violence in the elderly with those in another age group. METHODS: We included elderly patients (age ≥65 years) who visited six emergency departments (ED) with violence-induced injuries in 2017. The control group comprised patients aged 45-64 years, selected by 1:2 matching based on hospital and sex. Data were extracted from the National Emergency Department Information System and electronic medical records. Both groups were compared for injury mechanism, injury location, activity during injury, diagnosis, and clinical outcomes. RESULTS: Among the 316,944 patients who presented to the 6 ED, 89,178 (28.1%) had traumatic injuries, and 1.6% and 4.5% of injuries were sustained due to violence in the ≥65 and 45-64 year age groups, respectively. There were no significant intergroup differences in the perpetrator (P=0.27), body parts affected (P=0.63), and diagnosis (P=0.23), whereas the older adult group had a significantly higher proportion of traumatic injury by fall (P=0.01), at road and traffic facilities (P=0.01), during work (P=0.01), and multiple injuries (P<0.01). CONCLUSION: The increase in non-regular workers in the elderly after retirement may have increased the risk of traumatic workplace injuries. As workplace injuries may be a new risk factor for physical violence in the elderly, institutional workplace injury prevention policy is needed.

Three-in-One Strategy to Improve Both Catalytic Activity and Selectivity: Nonconcentric Pd-Au Nanoparticles.

In direct H2O2 synthesis, the Pd-Au alloy was considered as a potential catalyst because of its much better performance compared to the prototype Pd; unfortunately, achieving both high activity and selectivity remains a challenge. Here, we synthesized nonconcentric Pd-Au NPs in which Au domain shells are formed only partially on Pd domain cores and tested them for direct H2O2 synthesis. It has three exposed regions of Pd, Au domains, and Pd-Au interfaces in a single NP (hence, a 3-in-1 strategy). Creating nonconcentric forms was demonstrated convincingly by density functional theory calculations. The nonconcentric Pd-Au particles exhibit high and well-balanced performances that are hard to achieve with traditional alloyed Pd-Au. The number of Pd/Au interfaces was found to be the key factor and thus was optimized by controlling the Au precursor concentrations. The hitherto underutilized structure of nonconcentric bimetallic alloys can be useful and thus should be more actively investigated for catalyst development.

In-Vitro Characterization of Growth Inhibition against the Gut Pathogen of Potentially Probiotic Lactic Acid Bacteria Strains Isolated from Fermented Products.

Lactic acid bacteria (LAB) are probiotic candidates that may restore the balance of microbiota populations in intestinal microbial ecosystems by controlling pathogens and thereby promoting host health. The goal of this study was to isolate potential probiotic LAB strains and characterize their antimicrobial abilities against pathogens in intestinal microbiota. Among 54 LAB strains isolated from fermented products, five LAB strains (NSMJ15, NSMJ16, NSMJ23, NSMJ42, and NFFJ04) were selected as potential probiotic candidates based on in vitro assays of acid and bile salt tolerance, cell surface hydrophobicity, adhesion to the intestinal epithelium, and antagonistic activity. Phylogenetic analysis based on 16S rRNA genes showed that they have high similarities of 99.58-100% to Lacticaseibacillus paracasei strains NSMJ15 and NFFJ04, Lentilactobacillus parabuchneri NSMJ16, Levilactobacillus brevis NSMJ23, and Schleiferilactobacillus harbinensis NSMJ42. To characterize their antimicrobial abilities against pathogens in intestinal microbiota, the impact of cell-free supernatant (CFS) treatment in 10% (v/v) fecal suspensions prepared using pooled cattle feces was investigated using in vitro batch cultures. Bacterial community analysis using rRNA amplicon sequencing for control and CFS-treated fecal samples at 8 and 16 h incubation showed the compositional change after CFS treatment for all five LAB strains. The changed compositions were similar among them, but there were few variable increases or decreases in some bacterial groups. Interestingly, as major genera that could exhibit pathogenicity and antibiotic resistance, the members of Bacillus, Escherichia, Leclercia, Morganella, and Vagococcus were decreased at 16 h in all CFS-treated samples. Species-level classification suggested that the five LAB strains are antagonistic to gut pathogens. This study showed the probiotic potential of the five selected LAB strains; in particular, their antimicrobial properties against pathogens present in the intestinal microbiota. These strains would therefore seem to play an important role in modulating the intestinal microbiome of the host.

Current Scenario and Challenges in the Direct Identification of Microorganisms Using MALDI TOF MS.

MALDI TOF MS-based microbial identification significantly lowers the operational costs because of minimal requirements of substrates and reagents for extraction. Therefore, it has been widely used in varied applications such as clinical, food, military, and ecological research. However, the MALDI TOF MS method is laced with many challenges including its limitation of the reference spectrum. This review briefly introduces the background of MALDI TOF MS technology, including sample preparation and workflow. We have primarily discussed the application of MALDI TOF MS in the identification of microorganisms. Furthermore, we have discussed the current trends for bioaerosol detection using MALDI TOF MS and the limitations and challenges involved, and finally the approaches to overcome these challenges.

Deep Learning-Based Prediction of Material Properties Using Chemical Compositions and Diffraction Patterns as Experimentally Accessible Inputs.

We report a deep learning (DL) model that predicts various material properties while accepting directly accessible inputs from routine experimental platforms: chemical compositions and diffraction data, which can be obtained from the X-ray or electron-beam diffraction and energy-dispersive spectroscopy, respectively. These heterogeneous forms of inputs are treated simultaneously in our DL model, where the novel chemical composition vector is proposed by developing element embedding with the normalized composition matrix. With 1524 binary samples available in the Materials Project database, the model predicts formation energies and band gaps with mean absolute errors of 0.29 eV/atom and 0.66 eV, respectively. According to the weighing test between these two inputs, the properties tend to be more influenced by the chemical composition than the crystal structure. This work intentionally avoids using inputs that are not directly accessible (e.g., atomic coordinates) in experimental platforms, and thus is expected to substantially improve the practical use of DL models.

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).

Electrochemically Modeling a Nonelectrochemical System: Hydrogen Peroxide Direct Synthesis on Palladium Catalysts.

Nonelectrochemical hydrogen peroxide direct synthesis (HPDS) under ambient conditions is an environmentally benign and energy-efficient process that produces a green oxidizer, yet the reaction mechanism of HPDS is still controversial. Inspired by the recently suggested heterolytic mechanism that involves electron and proton transfer at Pd catalysts, we propose a new electrochemical density functional theory (DFT) model that combines the Butler-Volmer equation and constant-potential DFT with hybrid explicit-implicit solvent treatment. Application of this model to Pd surfaces showed that the heterolytic mechanism has a lower barrier for the protonation steps for H2O2 production than for the nonelectrochemical hydrogenation steps, leading to advantageous kinetics for H2O2 production over H2O production, while the conventionally accepted Langmuir-Hinshelwood mechanism fails to explain the experimental kinetics. This work resolves the unanswered discrepancies between previous experimental and DFT results, and we expect that these results will readily help the systematic development of improved catalysts for HPDS.

Atomistic Insights into H2O2 Direct Synthesis of Ni-Pt Nanoparticle Catalysts under Water Solvents by Reactive Molecular Dynamics Simulations.

In computational catalysis, density-functional theory (DFT) calculations are usually utilized, although they suffer from high computational costs. Thus, it would be challenging to explicitly predict the catalytic properties of nanoparticles (NPs) at the nanoscale under solvents. Using molecular dynamics (MD) simulations with a reactive force field (ReaxFF), we investigated the catalytic performance of Ni-Pt NPs for the direct synthesis of hydrogen peroxide (H2O2), in which water solvents were explicitly considered along with the effects of the sizes (1.5, 2.0, 3.0, and 3.5 nm) and compositions (Ni90Pt10, Ni80Pt20, and Ni50Pt50) of the NPs. Among the Ni-Pt NPs, 3.0 nm NPs show the highest activity and selectivity for the direct synthesis of H2O2, revealing that the catalytic performance is not well correlated with the surface areas of NPs. The superior catalytic performance results from the high H2 dissociation and low O2 dissociation properties, which are correlated with the numbers of NiNiPt-fcc and NiNi-bridge sites on the surface of Ni-Pt NPs, respectively. The ReaxFF-MD simulations propose the optimum composition (Ni80Pt20) of 3.0 nm Ni-Pt NPs, which is also explained by the numbers of NiNiPt-fcc and NiNi-bridge sites. Furthermore, from the ReaxFF-MD simulations, the direct synthesis of H2O2 for the Ni-Pt NPs can be achieved not only with the Langmuir-Hinshelwood mechanism, which has been conventionally considered, but also with the water-induced mechanism, which is unlikely to occur on pure Pd and Pd-based alloy catalysts; these results are supported by DFT calculations. These results reveal that the ReaxFF-MD method provides significant information for predicting the catalytic properties of NPs, which could be difficult to provide with DFT calculations; thus, it can be a useful framework for the design of nanocatalysts through complementation with a DFT method.

Complete Genome Sequence of Bisphenol A-Degrading Bacterium Sphingobium sp. Strain A3, Isolated from Contaminated Soil.

This study reports the complete genome sequence of bisphenol A-degrading bacterium Sphingobium sp. strain A3, which was isolated from a contaminated soil sample from the site of a factory fire in South Korea. The genome consists of a 6.53-Mbp chromosome and eight plasmid contigs (532,947 bp), with 6,406 protein-coding sequences and a GC content of 63.82%.

Complete Genome Sequence of Gordonia rubripertincta SD5, a Soil Bacterium Isolated from a Di-(2-Ethylhexyl) Phthalate-Degrading Enrichment Culture.

We report the complete genome sequence of Gordonia rubripertincta SD5, isolated from a soil-derived di-(2-ethylhexyl) phthalate-degrading enrichment culture. The final genome assembly consists of a 5.10-Mbp chromosome and a plasmid (159 kbp). A total of 4,814 coding sequences were predicted, including 4,741 protein-coding sequences.

Effects of nanopatterned-surface dishes on chondrocyte growth and cell cycle progression.

Discovering and developing ideal cell culture methods is important for cell biology, drug development, and cell therapy. Recent studies have explored and demonstrated the use of nanoscale structures and patterns that influence cell behavior, such as 3D scaffolds. In this study, we analyzed the effects of nanopatterned-surface dishes using chondrocytes as model cells. Chondrocytes grown on nanopatterned dishes exhibited rounded shapes. Interestingly, chondrocytes have a lower COL10 mRNA level when cultured using nanopatterned dishes. The nanopatterned dishes induced G0-/G1-phase cell cycle arrest and reduced the rate of proliferation. Our results suggest that nanoscale structures can directly control cellular behaviors and can be used for chondrocyte cell culture without causing chondrocytes to lose their functions. These results help to elucidate cellular responses and behaviors in native-like environments, and this information can be used to improve human health.

Metabolic Labeling of Live Stem Cell for In Vitro Imaging and In Vivo Tracking.

Stem cell therapy offers promising solutions to diseases and injuries that traditional medicines and therapies can't effectively cure. To get and explain their full therapeutic potentials, the survival, viability, integration, homing, and differentiation of stem cells after transplant must be clearly understood. To meet these urgent needs, noninvasive stem cell imaging and tracking technologies have been developed. Metabolic labeling technique is one of the most powerful tools for live cell imaging and tracking. In addition, it has many advantages for in vivo live cell imaging and tracking such as low background, correlation of survival, and very toxic and nontoxic by-products. Herein, we described the fundamental information and process of metabolic labeling techniques and suggested optimal condition for in vitro and in vivo imaging and tracking of human umbilical cord blood-derived endothelial progenitor cells (hUCB-EPCs). Based on this study, metabolic labeling techniques can be helpful for understanding the safety and effectiveness of stem cell-based therapy and determining the utility of stem cells in downstream experiments.

Facile aqueous-phase synthesis of Ag-Cu-Pt-Pd quadrometallic nanoparticles.

Ag-Cu-Pt-Pd quadrometallic nanoparticles which small Pt and Pd nanoparticles were attached on the surface of AgCu Janus nanoparticles were firstly synthesized by sequential reduction of Pt and Pd precursor in the presence of Janus AgCu bimetallic nanoparticles as seeds in an aqueous solution. Even though there was a small amount of Cu2O on the surface, the synthesized nanoparticles were mainly composed of four independent metallic part, not alloy parts. By theoretical calculation and growth mechanism study, we found that different reducing rate between Ag+ and Cu2+ and sequential reduction of Pt and Pd precursors would be key roles for the formation of the quadrometallic nanoparticles.

Improved Description of a Coordinate Bond in the ReaxFF Reactive Force Field.

To improve the description of a coordinate bond of the reactive force field (ReaxFF), we have developed ReaxFFcoord by explicitly incorporating the coordinate bond contribution, Ecoord, into the original ReaxFF ( Chenoweth et al. J. Phys. Chem. A 2008 , 112 , 1040 - 1053 ), in which the auxiliary functions are newly suggested to describe the plug-in behavior of lone-pair electrons from a donor atom to a vacant orbital of an acceptor atom. To validate the developed ReaxFFcoord, we tested it in various systems, including a representative coordinate bond-containing molecule, namely, carbon monoxide or ammonia borane. Although the fitting abilities of the ReaxFFcoord and original ReaxFF are similar, their molecular dynamics (MD) simulations are significantly different, where MD simulations employing ReaxFFcoord provide more realistic dynamic behaviors of atoms. It is expected that the ReaxFFcoord will significantly help ReaxFF to successfully extend its applicability to the material and biological systems, including coordinate bonds in organometallic systems.

Complete Genome Sequence of Lactobacillus harbinensis Strain NSMJ42, Isolated from Makgeolli, a Traditional Korean Alcoholic Beverage.

In the present work, we report the complete genome sequence of Lactobacillus harbinensis NSMJ42, isolated from makgeolli (a Korean traditional alcoholic beverage) in South Korea. The final genome assembly consists of a 3.29-Mbp chromosome with 3,082 protein-coding sequences and a G+C content of 53.36%.

Reproducible hindlimb ischemia model based on photochemically induced thrombosis to evaluate angiogenic effects.

Critical limb ischemia is one of the most common types of peripheral arterial disease. Preclinical development of ischemia therapeutics relies on the availability of a relevant and reproducible in vivo disease model. Thus, establishing appropriate animal disease models is essential for the development of new therapeutic strategies. Currently, the most commonly employed model of hindlimb ischemia is the surgical induction method with ligation of the femoral artery and its branches after skin incision. However, the efficiency of the method is highly variable depending on the availability of skilled technicians. In addition, after surgical procedures, animals can quickly and spontaneously recover from damage, limiting observations of the therapeutic effect of potential agents. The aim of this study was to develop a hindlimb ischemia mouse model with similarities to human ischemic disease. To that end, a photochemical reaction was used to induce thrombosis in the hindlimb. After the photochemical reaction was induced by light irradiation, thrombotic plugs and adjacent red blood cell stasis were observed in hindlimb vessels in the light-irradiated zone. Additionally, the photochemically induced thrombosis maintained the ischemic condition and did not cause notable side effects in mice.