Exfoliated Single Layers of Layered Cobalt Hydroxide for Ultrafine Co3 O4 Nanoparticles on Graphene Nanosheets as an Efficient Electrocatalyst for Oxygen Reduction.

A highly effective way to produce an oxygen reduction electrocatalyst was developed through the self-assembly of exfoliated single layers of cobalt hydroxide (Co(OH)2 ) and graphene oxide (GO). These 2D materials have complete contact with one another because of their physical flexibility and the electrostatic attraction between negatively charged GO and positively charged Co(OH)2 layers. The strong coupling induces transformation of the Co(OH)2 single layer into a discrete nanocrystal of spinel Co3 O4 with an average size of 8 nm on reduced GO (RGO) during calcination, which could not be obtained with bulk-layered cobalt hydroxide because of its rapid layer collapse. The ultrafine Co3 O4 /RGO hybrid exhibited not only comparable performance in the oxygen reduction reaction but also higher durability compared with the commercial 20 wt % Pt/C catalyst.

Characteristics of Nanosized Ni, NiCo-Y2O3 Powders Synthesized by a PVA Solution Route at Low Temperature.

Nanosized Ni, NiCo-Y2O3 powders were successfully synthesized at low temperature via a simple polymer solution route. As an organic carrier, polyvinyl alcohol (PVA) afforded an atom-scale homogeneous precursor gel, which in turn gave fully crystallized nanosized Ni, NiCo-Y2O3 powder upon calcination at a low temperature under an Ar-4% H2 atmosphere. The PVA content, calcination temperature, heating time, and reduction conditions affected the microstructure and crystallization behavior of the as-synthesized powders. The PVA content also influenced the synthesis behavior and microstructure of the final powder. The particle size increased with an increase in the calcination temperature and decrease in the PVA content. At a PVA-metal ion ratio of 4:1, the measured particle size was about 20 nm. The results of TEM mapping on the NiCo-Y (0.6 wt%) powders revealed a well-dispersed Y2O3 phase in the NiCo crystalline matrix.

Cost-Effective, High-Performance Porous-Organic-Polymer Conductors Functionalized with Sulfonic Acid Groups by Direct Postsynthetic Substitution.

We demonstrate the facile microwave-assisted synthesis of a porous organic framework 1 and the sulfonated solid (1S) through postsubstitution. Remarkably, the conductivity of 1S showed an approximately 300-fold enhancement at 30 °C as compared to that of 1, and reached 7.72×10-2  S cm-1 at 80 °C and 90 % relative humidity. The superprotonic conductivity exceeds that observed for any conductive porous organic polymer reported to date. This material, which is cost-effective and scalable for mass production, also revealed long-term performance over more than 3 months without conductivity decay.

Quantitative analysis of polyhexamethylene guanidine (PHMG) oligomers via matrix-assisted laser desorption/ionization time-of-flight mass spectrometry with an ionic-liquid matrix.

RATIONALE: Quantifying polymers by matrix-assisted laser desorption/ionization time-of-flight mass spectrometry (MALDI-TOFMS) with a conventional crystalline matrix generally suffers from poor sample-to-sample or shot-to-shot reproducibility. An ionic-liquid matrix has been demonstrated to mitigate these reproducibility issues by providing a homogeneous sample surface, which is useful for quantifying polymers. In the present study, we evaluated the use of an ionic liquid matrix, i.e., 1-methylimidazolium α-cyano-4-hydroxycinnamate (1-MeIm-CHCA), to quantify polyhexamethylene guanidine (PHMG) samples that impose a critical health hazard when inhaled in the form of droplets. METHODS: MALDI-TOF mass spectra were acquired for PHMG oligomers using a variety of ionic-liquid matrices including 1-MeIm-CHCA. Calibration curves were constructed by plotting the sum of the PHMG oligomer peak areas versus PHMG sample concentration with a variety of peptide internal standards. RESULTS: Compared with the conventional crystalline matrix, the 1-MeIm-CHCA ionic-liquid matrix had much better reproducibility (lower standard deviations). Furthermore, by using an internal peptide standard, good linear calibration plots could be obtained over a range of PMHG concentrations of at least 4 orders of magnitude. CONCLUSIONS: This study successfully demonstrated that PHMG samples can be quantitatively characterized by MALDI-TOFMS with an ionic-liquid matrix and an internal standard.

RRM2 Is a CTNNB1 Transport Regulator Promoting Colon Cancer Progression.

BACKGROUND/AIM: The cytoplasmic retention and stabilization of CTNNB1 (ß-catenin) in response to Wnt is well documented in playing a role in tumor growth. Here, through the utilization of a multiplex siRNA library screening strategy, we investigated the modulation of CTNNB1 function in tumor cell progression by ribonucleoside-diphosphate reductase subunit M2 (RRM2). MATERIALS AND METHODS: We conducted a multiplex siRNA screening assay to identify targets involved in CTNNB1 nuclear translocation. In order to examine the effect of inhibition of RRM2, selected from the siRNA screening results, we performed RRM2 knockdown and assayed for colon cancer cell viability, sphere formation assay, and invasion assay. The interaction of RRM2 with CTNNB1 and its impact on oncogenesis was examined using immunoprecipitation, immunoblotting, immunocytochemistry, and RT-qPCR. RESULTS: After a series of screening and filtration steps, we identified 26 genes that were potentially involved in CTNNB1 nuclear translocation. All candidate genes were validated in various cell lines. The results revealed that siRNA-mediated knockdown of RRM2 reduces the nuclear translocation of CTNNB1. This reduction was accompanied by a decrease in cell count, resulting in a suppressive effect on tumor cell growth. CONCLUSION: High throughput siRNA screening is an attractive strategy for identifying gene functions in cancers and the interaction between RRM2 and CTNNB1 is an attractive drug target for regulating RRM2-CTNNB1-related pathways in cancers.

Resolving the non-uniformity in the feature space of age estimation: A deep learning model based on feature clusters of panoramic images.

Age estimation is important in forensics, and numerous techniques have been investigated to estimate age based on various parts of the body. Among them, dental tissue is considered reliable for estimating age as it is less influenced by external factors. The advancement in deep learning has led to the development of automatic estimation of age using dental panoramic images. Typically, most of the medical datasets used for model learning are non-uniform in the feature space. This causes the model to be highly influenced by dense feature areas, resulting in adequate estimations; however, relatively poor estimations are observed in other areas. An effective solution to address this issue can be pre-dividing the data by age feature and training each regressor to estimate the age for individual features. In this study, we divide the data based on feature clusters obtained from unsupervised learning. The developed model comprises a classification head and multi-regression head, wherein the former predicts the cluster to which the data belong and the latter estimates the age within the predicted cluster. The visualization results show that the model can focus on a clinically meaningful area in each cluster for estimating age. The proposed model outperforms the models without feature clusters by focusing on the differences within the area. The performance improvement is particularly noticeable in the growth and aging periods. Furthermore, the model can adequately estimate the age even for samples with a high probability of classification error as they are located at the border of two feature clusters.

Discrimination of metal contaminant sources in river sediments influenced by mining and smelting activities using stable Pb and Zn isotopes.

To determine the sources and pathways of lead (Pb) and zinc (Zn) in river sediments contaminated with metals from mining and smelting activities, metal concentrations and Pb and Zn isotope ratios were measured in river water and sediment, and potential metal contaminant samples (imported Zn concentrates, smelting wastes, soils around the smelter, mine ores, and riverside tailings). Zn and cadmium (Cd) concentrations in river water and sediment samples were 30- and 11-25-fold higher, respectively, near the smelter than upstream, while a 6-fold increase in sediment Pb concentrations was detected over the same region. Sediment samples near the smelter (207Pb/206Pb = 0.8638 and 208Pb/206Pb = 2.0960) were observed to have a different Pb isotopic composition from upstream of the smelter (207Pb/206Pb = 0.8322 and 208Pb/206Pb = 2.0502), with δ66Zn values increasing from -0.01 to 0.82‰. Analysis of Pb and Zn isotopes and concentrations revealed that dust-contaminated soils were a major Pb source, and baseline sediments were found to be contaminated by regional mining tailings. For Zn in sediments, the main Zn sources were groundwater-derived Zn (δ66Zn = 1.02 ± 0.43‰, n = 4), dust-contaminated soils (δ66Zn = -0.18 ± 0.08‰, n = 3), and tailings-contaminated sediments (δ66Zn = 0.01 ± 0.07‰, n = 10). Endmember mixing model results showed that dust-contaminated soils contributed 78% and 64% of sediment Pb and Zn, respectively, within 2 km of the Zn smelter, decreasing to negligible levels after 47.1 km downstream. Downstream of the smelter, groundwater-derived Zn contributed 54% of sediment Zn, whereas tailings contaminated sediments contributed 70% and 25% of Pb and Zn, respectively.

Toxicological evidence integration to confirm the biological plausibility of the association between humidifier disinfectant exposure and respiratory diseases using the AEP-AOP framework.

Objectives: Exposure to humidifier disinfectants has been linked to respiratory diseases, including interstitial lung disease, asthma, and pneumonia. Consequently, numerous toxicological studies have explored respiratory damage as both a necessary and sufficient condition for these diseases. We systematically reviewed and integrated evidence from toxicological studies by applying the evidence integration method established in previous research to confirm the biological plausibility of the association between exposure and disease. Methods: We conducted a literature search focusing on polyhexamethylene guanidine phosphate (PHMG) and chloromethylisothiazolinone/methylisothiazolinone (CMIT/MIT), the primary ingredients in humidifier disinfectants. We selected relevant studies based on their quality and the population, exposure, comparator, outcome (PECO) statements. These studies were categorized into 3 lines of evidence: hazard information, animal studies, and mechanistic studies. Based on a systematic review, we integrated the evidence to develop an aggregate exposure pathway-adverse outcome pathway (AEP-AOP) model for respiratory damage. The reliability and relevance of our findings were assessed by comparing them with the hypothesized pathogenic mechanisms of respiratory diseases. Results: The integration of each AEP-AOP component for PHMG and CMIT/MIT led to the development of an AEP-AOP model, wherein disinfectants released from humidifiers in aerosol or gaseous form reached target sites, causing respiratory damage through molecular initiating events and key events. The model demonstrated high reliability and relevance to the pathogenesis of respiratory diseases. Conclusion: The AEP-AOP model developed in this study provides strong evidence that exposure to humidifier disinfectants causes respiratory diseases. This model demonstrates the pathways leading to respiratory damage, a hallmark of these conditions.

Automated deep learning model for estimating intraoperative blood loss using gauze images.

The intraoperative estimated blood loss (EBL), an essential parameter for perioperative management, has been evaluated by manually weighing blood in gauze and suction bottles, a process both time-consuming and labor-intensive. As the novel EBL prediction platform, we developed an automated deep learning EBL prediction model, utilizing the patch-wise crumpled state (P-W CS) of gauze images with texture analysis. The proposed algorithm was developed using animal data obtained from a porcine experiment and validated on human intraoperative data prospectively collected from 102 laparoscopic gastric cancer surgeries. The EBL prediction model involves gauze area detection and subsequent EBL regression based on the detected areas, with each stage optimized through comparative model performance evaluations. The selected gauze detection model demonstrated a sensitivity of 96.5% and a specificity of 98.0%. Based on this detection model, the performance of EBL regression stage models was compared. Comparative evaluations revealed that our P-W CS-based model outperforms others, including one reliant on convolutional neural networks and another analyzing the gauze's overall crumpled state. The P-W CS-based model achieved a mean absolute error (MAE) of 0.25 g and a mean absolute percentage error (MAPE) of 7.26% in EBL regression. Additionally, per-patient assessment yielded an MAE of 0.58 g, indicating errors < 1 g/patient. In conclusion, our algorithm provides an objective standard and streamlined approach for EBL estimation during surgery without the need for perioperative approximation and additional tasks by humans. The robust performance of the model across varied surgical conditions emphasizes its clinical potential for real-world application.

A multimodal virtual vision platform as a next-generation vision system for a surgical robot.

Robot-assisted surgery platforms are utilized globally thanks to their stereoscopic vision systems and enhanced functional assistance. However, the necessity of ergonomic improvement for their use by surgeons has been increased. In surgical robots, issues with chronic fatigue exist owing to the fixed posture of the conventional stereo viewer (SV) vision system. A head-mounted display was adopted to alleviate the inconvenience, and a virtual vision platform (VVP) is proposed in this study. The VVP can provide various critical data, including medical images, vital signs, and patient records, in three-dimensional virtual reality space so that users can access medical information simultaneously. An availability of the VVP was investigated based on various user evaluations by surgeons and novices, who executed the given tasks and answered questionnaires. The performances of the SV and VVP were not significantly different; however, the craniovertebral angle of the VVP was 16.35° higher on average than that of the SV. Survey results regarding the VVP were positive; participants indicated that the optimal number of displays was six, preferring the 2 × 3 array. Reflecting the tendencies, the VVP can be a neoconceptual candidate to be customized for medical use, which opens a new prospect in a next-generation surgical robot.

Towards optimal design of patient isolation units in emergency rooms to prevent airborne virus transmission: From computational fluid dynamics to data-driven modeling.

BACKGROUND: Patient isolation units (PIUs) can be an effective method for effective infection control. Computational fluid dynamics (CFD) is commonly used for PIU design; however, optimizing this design requires extensive computational resources. Our study aims to provide data-driven models to determine the PIU settings, thereby promoting a more rapid design process. METHOD: Using CFD simulations, we evaluated various PIU parameters and room conditions to assess the impact of PIU installation on ventilation and isolation. We investigated particle dispersion from coughing subjects and airflow patterns. Machine-learning models were trained using CFD simulation data to estimate the performance and identify significant parameters. RESULTS: Physical isolation alone was insufficient to prevent the dispersion of smaller particles. However, a properly installed fan filter unit (FFU) generally enhanced the effectiveness of physical isolation. Ventilation and isolation performance under various conditions were predicted with a mean absolute percentage error of within 13%. The position of the FFU was found to be the most important factor affecting the PIU performance. CONCLUSION: Data-driven modeling based on CFD simulations can expedite the PIU design process by offering predictive capabilities and clarifying important performance factors. Reducing the time required to design a PIU is critical when a rapid response is required.

Effects of ammonia on fertilization, development, and larval survival in the Northern Pacific asteroid, Asterias amurensis.

For developing a complementary test organism to sea urchin during winter in Korea, sensitivities of sperm, embryo, and larvae of Asterias amurensis to un-ionized ammonia were evaluated. The EC50s (Mean ± SD, n = 3) for fertilization and development were 169 ± 62 and 70 ± 19 µg/L, respectively. The 48, 72, and 96-h LC50s for larval survival were 1,674 ± 583, 498 ± 221, and 336 ± 107 µg/L, respectively. The sensitivities of fertilization, development, and larval survival tests with A. amurensis are higher than or comparable to those of sea urchin and other taxonomic groups. Therefore, fertilization, development, and larval survival tests using A. amurensis are suitable for assessing pore water toxicity of marine sediments in Korea.

Effects of Exercise Intervention (with and without Joint Mobilization) in Patients with Adhesive Capsulitis: A Systematic Review and Meta-Analysis.

This review aimed to investigate the effects of exercise and exercise with joint mobilization on shoulder range of motion (ROM) and subjective symptom recovery in patients with adhesive capsulitis (AC). Related Studies published from 2000 to 2021 that were peer-reviewed and for which pre-and post-values could be calculated were extracted from PubMed, CINAHL, SPORTDiscus, and Web of Science. Nine studies met our inclusion criteria. As a result of calculating the standard mean difference (SMD) and 95% confidence intervals (CI), both exercise and exercise with joint mobilization showed a large effect on shoulder ROM and subjective outcomes. The combination showed a more significant effect than exercise alone on shoulder flexion (SMD = -1.59 [-2.34, -0.65]), extension (SMD = -1.47 [-2.05, -0.89]), internal rotation (SMD = -1.77 [-2.17, -1.36], external rotation (SMD = -2.18 [-2.92, -1.44]), and abduction ROM (SMD = -1.99 [CI -3.86, -0.12]). Patients who performed exercise alone showed a higher effect of improvement in subjective function (SMD = 3.15 [2.06, 4.24]) and pain (SMD = 4.13 [1.86, 6.41]). Based on these results, an AC rehabilitation exercise program should be developed by adjusting the amount of exercise and joint mobilization by identifying the patient's needs, subjective symptoms, and ROM.

Development of a Machine Learning Model to Estimate the Biotic Ligand Model-Based Predicted No-Effect Concentrations for Copper in Freshwater.

The copper (Cu) biotic ligand model (BLM) has been used for ecological risk assessment by taking into account the bioavailability of Cu in freshwater. The Cu BLM requires data for many water chemistry variables, such as pH, major cations, and dissolved organic carbon, which can be difficult to obtain from water quality monitoring programs. To develop an optimized predicted no-effect concentration (PNEC) estimation model based on an available monitoring dataset, we proposed an initial model that considers all BLM variables, a second model that requires variables excluding alkalinity, and a third model using electrical conductivity as a surrogate for the major cations and alkalinity. Furthermore, deep neural network (DNN) models have been used to predict the nonlinear relationships between the PNEC (outcome variable) and the required input variables (explanatory variables). The predictive capacity of DNN models was compared with the results of other existing PNEC estimation tools using a look-up table and multiple linear and multivariate polynomial regression methods. Three DNN models, using different input variables, provided better predictions of the Cu PNECs compared with the existing tools for the following four test datasets: Korean, United States, Swedish, and Belgian freshwaters. Consequently, it is expected that Cu BLM-based risk assessment can be applied to various monitoring datasets, and that the most applicable model among the three different types of DNN models could be selected according to data availability for a given monitoring database. Environ Toxicol Chem 2023;42:2271-2283. © 2023 SETAC.

Comparison of the Estimation Methods from Acute to Chronic Biotic Ligand Model-Based Predicted No-Effect Concentrations for Nickel in Freshwater Species.

Biotic ligand models (BLMs) and the sensitivities of indigenous species are used to assess the environmental risk considering the bioavailability of metals, such as nickel. However, the BLM-based acute-to-chronic ratio (ACR) is required if the predicted no-effect concentration (PNEC) cannot be derived from the chronic species sensitivity distribution (SSD). The applicability of the ACR approach for estimating BLM-based PNEC for nickel from acute toxicity data was evaluated in the present study. The BLM-based acute SSD for nickel was built using the sensitivities of 21 indigenous species and different taxon-specific BLMs for each taxonomic group. To predict the acute sensitivity of invertebrates, the chronic crustacean nickel BLM with pH effect term, which can account for nickel toxicity at high pH levels, was used. This was used instead of the existing acute BLM for crustacean, which has too narrow a pH range to cover the pH dependency of toxicity. The final BLM-based ACR of nickel, determined within a factor of 1.53 from the species-specific acute and chronic sensitivities of the six species, was more reliable than the typical ACR estimated within a factor of 1.84. A linear relationship (r2 = 0.95) was observed between the PNECs using BLM-based ACR and the PNECs derived from the BLM-based chronic SSD of the European Union Risk Assessment Reports. In conclusion, the BLM-based PNEC for nickel could be derived using the ACR approach, unlike when copper BLM was applied. The BLM-based ACR for nickel is the first result calculated by directly comparing acute and chronic species sensitivities, and will contribute to the application of BLM-based risk assessment in broader ecoregions. Environ Toxicol Chem 2023;42:914-927. © 2023 SETAC.

Importance of hydraulic residence time for methylmercury accumulation in sediment and fish from artificial reservoirs.

Excessive methylmercury (MeHg) accumulation in dietary fish is a global concern due to its harmful effects on human health, however, environmental factors affecting MeHg accumulation in reservoir ecosystems are not clearly known. In this study, we aim to identify the main sources of MeHg in the water column and the critical factors related to MeHg concentration and methylation rate constant (km) in sediment and total Hg concentration in fish using five-year (2016-2020) monitoring data of the five artificial reservoirs. The preliminary mass budgets constructed using the measurement and online data showed that sediment transport dominated over runoff in the long residence time reservoirs (400-475 days), while runoff dominated over sediment transport in the short residence time reservoirs (10 days). Whereas the sediment km showed a comparable variation with the algal biomass, the sediment MeHg concentration and the length-normalized Hg concentration in the barbel steed and bluegill increased in the longer residence time reservoirs with lower algal biomass. As MeHg accumulation in sediment and fish tends to increase in the slowly overturning reservoirs, the hydraulic residence time should be carefully managed to meet the best protection of human health from chronic Hg exposure by fish consumption.

All-Trans Retinoic Acid-Induced Ototoxicity during Chemotherapy in Pediatric Acute Promyelocytic Leukemia.

All-trans retinoic acid (ATRA) is known to induce complete remission of acute promyelocytic leukemia (APL) and its use has significantly improved the cure rate of APL. However, ATRA also causes side effects such as differentiation syndrome or intracranial hypertension. In our case, the patient was diagnosed with APL and developed hearing loss thrice while being treated with ATRA. Therefore, we reduced the dose of ATRA instead of stopping it altogether and administered dexamethasone to the patient. A hearing test performed thereafter revealed recovery of hearing. No recurrence of hearing loss occurred after prednisolone and ATRA were combined in the maintenance phase. In conclusion, ATRA-associated hearing loss is reversible, and it is not necessary to stop ATRA. We recommend completion of a randomized clinical trial using dexamethasone in combination with ATRA to prevent hearing loss caused by ATRA.

Low-temperature fabrication of crystalline MnCoO spinel film on porous carbon paper for efficient oxygen evolution reaction.

Cubic MnxCo3-xO4 (x = 0-0.5) spinel nanocrystal thin films were fabricated on carbon fibre electrodes via one-step topotactic catalysis using Co(OH)2 nanosheets under aqueous and mild reaction conditions (<120 °C). The MnCo3O4 (Mn = 0.01)/CFP catalyst showed the best charge transport efficiency, exhibiting excellent OER activity and stability.

Spatial Uncertainty in Modeling Inhalation Exposure to Volatile Organic Compounds in Response to the Application of Consumer Spray Products.

(1) Background: Mathematical exposure modeling of volatile organic compounds (VOCs) in consumer spray products mostly assumes instantaneous mixing in a room. This well-mixed assumption may result in the uncertainty of exposure estimation in terms of spatial resolution. As the inhalation exposure to chemicals from consumer spray products may depend on the spatial heterogeneity, the degree of uncertainty of a well-mixed assumption should be evaluated under specific exposure scenarios. (2) Methods: A room for simulation was divided into eight compartments to simulate inhalation exposure to an ethanol trigger and a propellant product. Real-time measurements of the atmospheric concentration in a room-sized chamber by proton transfer reaction mass spectrometry were compared with mathematical modeling to evaluate the non-homogeneous distribution of chemicals after their application. (3) Results: The well-mixed model overestimated short-term exposure, particularly under the trigger spray scenario. The uncertainty regarding the different chemical proportions in the trigger did not significantly vary in this study. (4) Conclusions: Inhalation exposure to aerosol generating sprays should consider the spatial uncertainty in terms of the estimation of short-term exposure.

Effects of Zinc Addition on the Corrosion Behavior of Pre-Filmed Alloy 690 in Borated and Lithiated Water at 330 °C.

The purpose of this work is to quantify the effects of dissolved zinc cations on corrosion and release rates from a pre-filmed Alloy 690 steam generator tubing material that was subsequently exposed to water containing zinc. The corrosion tests were performed in circulating 2 ppm Li and 1000 ppm B water without and with 60 ppb zinc at 330 °C. Gravimetric analyses and oxide characterization revealed that the corrosion rates, release rates, and oxide thicknesses decreased by subsequent exposure of the pre-filmed Alloy 690 to zinc. These benefits are attributed to the formation of a chromium-rich inner oxide layer incorporating zinc.