Automatic Classification of Thyroid Findings Using Static and Contextualized Ensemble Natural Language Processing Systems: Development Study

BackgroundIn the case of Korean institutions and enterprises that collect nonstandardized and nonunified formats of electronic medical examination results from multiple medical institutions, a group of experienced nurses who can understand the results and related contexts initially classified the reports manually. The classification guidelines were established by years of workers’ clinical experiences and there were attempts to automate the classification work. However, there have been problems in which rule-based algorithms or human labor–intensive efforts can be time-consuming or limited owing to high potential errors. We investigated natural language processing (NLP) architectures and proposed ensemble models to create automated classifiers. ObjectiveThis study aimed to develop practical deep learning models with electronic medical records from 284 health care institutions and open-source corpus data sets for automatically classifying 3 thyroid conditions: healthy, caution required, and critical. The primary goal is to increase the overall accuracy of the classification, yet there are practical and industrial needs to correctly predict healthy (negative) thyroid condition data, which are mostly medical examination results, and minimize false-negative rates under the prediction of healthy thyroid conditions. MethodsThe data sets included thyroid and comprehensive medical examination reports. The textual data are not only documented in fully complete sentences but also written in lists of words or phrases. Therefore, we propose static and contextualized ensemble NLP network (SCENT) systems to successfully reflect static and contextual information and handle incomplete sentences. We prepared each convolution neural network (CNN)-, long short-term memory (LSTM)-, and efficiently learning an encoder that classifies token replacements accurately (ELECTRA)-based ensemble model by training or fine-tuning them multiple times. Through comprehensive experiments, we propose 2 versions of ensemble models, SCENT-v1 and SCENT-v2, with the single-architecture–based CNN, LSTM, and ELECTRA ensemble models for the best classification performance and practical use, respectively. SCENT-v1 is an ensemble of CNN and ELECTRA ensemble models, and SCENT-v2 is a hierarchical ensemble of CNN, LSTM, and ELECTRA ensemble models. SCENT-v2 first classifies the 3 labels using an ELECTRA ensemble model and then reclassifies them using an ensemble model of CNN and LSTM if the ELECTRA ensemble model predicted them as “healthy” labels. ResultsSCENT-v1 outperformed all the suggested models, with the highest F1 score (92.56%). SCENT-v2 had the second-highest recall value (94.44%) and the fewest misclassifications for caution-required thyroid condition while maintaining 0 classification error for the critical thyroid condition under the prediction of the healthy thyroid condition. ConclusionsThe proposed SCENT demonstrates good classification performance despite the unique characteristics of the Korean language and problems of data lack and imbalance, especially for the extremely low amount of critical condition data. The result of SCENT-v1 indicates that different perspectives of static and contextual input token representations can enhance classification performance. SCENT-v2 has a strong impact on the prediction of healthy thyroid conditions

Enhanced electroreduction of CO2 by Ni-N-C catalysts from the interplay between valency and local coordination symmetry

Many studies have focused on atomically dispersed metal-nitrogen-carbon (Me-N-C) catalysts owing to their unique chemistry and high catalytic activities. Me-N-C catalysts have active centers resembling metalloporphyrins; thus, being heterogeneous analogs of homogeneous catalysts, their catalytic characteristics can be described by organometallic principles. In this regard, the high electrochemical activity of Ni-N-C catalysts for carbon dioxide reduction reactions (CO2RRs) is particularly difficult to understand because Ni2+ is a d8 species with a chemically inert axial site for intermediate binding in a square-planar ligand field. To resolve such a conundrum, we investigated the effects of different coordination geometries and Ni spin states on CO2RR activities—both of which influence the chemical activity of the Ni center. We used the grand-canonical density functional theory (GC-DFT) and the occupation matrix control method to properly include a finite potential effect, and to control the oxidation state of the Ni center, respectively. We elucidated that the generation of Ni+ directly impacts the CO2RR activity by providing strong intermediate binding energies to the Ni center, and a defective coordination environment is essential for stabilizing the Ni+ oxidation state. Our present study identifying governing factors for the high catalytic activity of Ni-N-C catalysts provides a design principle to develop high-performing catalysts for CO2RR.11Nsciescopu

Unravelling Catalytic Trends of Atomically Dispersed Precious Metal Catalysts for Oxygen Reduction Reaction

Atomically dispersed catalysts have emerged as a research frontier in catalysis, however a general strategy for atomically dispersed catalysts of wide compositional range is still lacking, which has impeded systematic studies unveiling the catalytic origins of atomically dispersed catalysts. In the work, we present a generalized synthetic strategy toward atomically dispersed precious metal catalysts, which consists of ???ldquo;trapping???rdquo; of precious metal precursors and ???ldquo;immobilizing???rdquo; them with SiO2 layers. Five atomically dispersed precious metals (Os, Ru, Rh, Ir, Pt) catalysts were prepared and served as model catalysts for revealing reactivity trends of atomically dispersed catalysts for oxygen reduction reaction (ORR). We found that higher H2O2 selectivity was shown in atomically dispersed catalysts compared to their nanoparticle counterparts, which originates from abnormally weakened oxygen binding energies and isolated geometric configurations of atomically dispersed sites. Furthermore, the relative binding energies of *OOH and *O species were identified as determinants that dictate the ORR selectivity of atomically dispersed catalysts

Unveiling Catalytic Trends of Atomically Dispersed Precious Metal Catalysts for Oxygen Reduction Reaction

Atomically dispersed catalysts have emerged as a research frontier in catalysis, however a general strategy for atomically dispersed catalysts of wide range of compositions is still lacking, which has limited systematic studies unravelling the catalytic origins of atomically dispersed catalysts. In the work, we present a generalized synthetic strategy to atomically dispersed catalysts of precious metals, which consists of ???trapping??? of precious metal precursors and ???immobilizing??? them with SiO2 layers. Five atomically dispersed precious metals (Os, Ru, Rh, Ir, Pt) catalysts were prepared and served as model catalysts for revealing reactivity trends of atomically dispersed catalysts for oxygen reduction reaction (ORR). We found that higher H2O2 selectivity was shown in atomically dispersed catalysts compared to their nanoparticle counterparts, which originates from abnormally weakened oxygen binding energies and isolated geometric configurations of atomically dispersed sites. Furthermore, the relative binding energies of *OOH and *O species were identified as determinants that dictate the ORR selectivity of atomically dispersed catalysts

Thermal Transformation of Molecular Ni2+-N-4 Sites for Enhanced CO2 Electroreduction Activity

Atomically dispersed nickel sites complexed on nitrogen-doped carbon (Ni-N/C) have demonstrated considerable activity for the selective electrochemical carbon dioxide reduction reaction (CO2RR) to CO. However, the high-temperature treatment typically involved during the activation of Ni-N/C catalysts makes the origin of the high activity elusive. In this work, Ni(II) phthalocyanine molecules grafted on carbon nanotube (NiPc/CNT) and heat-treated NiPc/CNT (H-NiPc/CNT) are exploited as model catalysts to investigate the impact of thermal activation on the structure of active sites and CO2RR activity. H-NiPc/CNT exhibits a similar to 4.7-fold higher turnover frequency for CO2RR to CO in comparison to NiPc/CNT. Extended X-ray absorption fine structure analysis and density functional theory (DFT) calculations reveal that the heat treatment transforms the molecular Ni2+-N-4 sites of NiPc into Ni+-N3V (V: vacancy) and Ni+-N-3 sites incorporated in the graphene lattice that concomitantly involves breakage of Ni-N bonding, shrinkage in the Ni-N-C local structure, and decrease in the oxidation state of the Ni center from +2 to +1. DFT calculations combined with microkinetic modeling suggest that the Ni-N3V site appears to be responsible for the high CO2RR activity because of its lower barrier for the formation of * COOH intermediate and optimum *CO binding energy. In situ/operando X-ray absorption spectroscopy analyses further corroborate the importance of reduced Ni+ species in boosting the CO2RR activity

A General Strategy to Atomically Dispersed Precious Metal Catalysts for Unravelling Their Catalytic Trends for Oxygen Reduction Reaction

Atomically dispersed precious metal catalysts have emerged as a frontier in catalysis. However, a robust, generic synthetic strategy toward atomically dispersed catalysts is still lacking, which has limited systematic studies revealing their general catalytic trends distinct from those of conventional nanoparticle (NP)-based catalysts. Herein, we report a general synthetic strategy toward atomically dispersed precious metal catalysts, which consists of "trapping" precious metal precursors on a heteroatom-doped carbonaceous layer coated on a carbon support and "immobilizing" them with a SiO2 layer during thermal activation. Through the "trapping-and-immobilizing" method, five atomically dispersed precious metal catalysts (Os, Ru, Rh, Ir, and Pt) could be obtained and served as model catalysts for unravelling catalytic trends for the oxygen reduction reaction (ORR). Owing to their isolated geometry, the atomically dispersed precious metal catalysts generally showed higher selectivity for H2O2 production than their NP counterparts for the ORR. Among the atomically dispersed catalysts, the H2O2 selectivity was changed by the types of metals, with atomically dispersed Pt catalyst showing the highest selectivity. A combination of experimental results and density functional theory calculations revealed that the selectivity trend of atomically dispersed catalysts could be correlated to the binding energy difference between *OOH and *O species. In terms of 2 e(-) ORR activity, the atomically dispersed Rh catalyst showed the best activity. Our general approach to atomically dispersed precious metal catalysts may help in understanding their unique catalytic behaviors for the ORR

Reversible Ligand Exchange in Atomically Dispersed Catalysts for Modulating the Activity and Selectivity of the Oxygen Reduction Reaction

Rational control of the coordination environment of atomically dispersed catalysts is pivotal to achieve desirable catalytic reactivity. We report the reversible control of coordination structure in atomically dispersed electrocatalysts via ligand exchange reactions to reversibly modulate their reactivity for oxygen reduction reaction (ORR). The CO-ligated atomically dispersed Rh catalyst exhibited ca. 30-fold higher ORR activity than the NHx-ligated catalyst, whereas the latter showed three times higher H2O2 selectivity than the former. Post-treatments of the catalysts with CO or NH3 allowed the reversible exchange of CO and NHx ligands, which reversibly tuned oxidation state of metal centers and their ORR activity and selectivity. DFT calculations revealed that more reduced oxidation state of CO-ligated Rh site could further stabilize the *OOH intermediate, facilitating the two- and four-electron pathway ORR. The reversible ligand exchange reactions were generalized to Ir- and Pt-based catalysts

Bioconverted Orostachys japonicas Extracts Suppress Angiogenic Activity of Ms-1 Endothelial Cells

Orostachys japonicus A. Berger (), known as Wa-song in Korea, has been reported to exert various biological effects, such as anti-tumor, anti-oxidant, and anti-febrile effects. However, the anti-angiogenic effects of O. japonicus extracts remain to be investigated. In the present study, we demonstrated the anti-angiogenic effects of bioconverted O. japonicus extract (BOE) in Ms-1 mouse endothelial cells and compared them with the bioactivities of O. japonicus extract (OE). BOE, but not OE, were found to exert anti-angiogenic effects, including inhibition of cell migration, cell adhesion, tube formation of Ms-1 cells, and blood vessel formation of matrigel plug assay in vivo. Furthermore, protein levels of phosphorylated Src kinase were lower in BOE-treated cells than in OE-treated cells. Treatment with OE or BOE did not influence cell viability during the experimental period. Bioconverted extract of O. japonicus have anti-angiogenic effects in vitro and vivo, but non-bioconverted extract do not. We suggest that these observed anti-angiogenic effects are caused by the changes in the composition of bioactive compounds in the extracts as a result of biological conversion