Search

Tae Young Yeo, Keonyeup Kim, Moon Ho Park Department of Neurology, Korea University Ansan Hospital, Ansan, Republic of KoreaCorrespondence: Moon Ho ParkDepartment of Neurology, Korea University Ansan Hospital, 123 Jeokgeum-ro, Danwon-gu, Ansan-si, Gyeonggi-do 15355, Republic of KoreaTel +82 31 412 5155Fax +82 31 412 5154Email kmmse@naver.comPurpose: The ability to convert scores between cognitive measurements would facilitate the longitudinal assessment of cognition in clinical practice and the comparison and synthesis of cognitive data from international, multicenter, or longitudinal studies. The primary aim of this study was to apply a simple and reliable method for converting scores from the Korean Dementia Screening Questionnaire (KDSQ) to those of the Informant Questionnaire on Cognitive Decline in the Elderly (IQCODE).Patients and methods: A total of 627 participants, with and without cognitive dysfunction, received both the KDSQ and the IQCODE at the same visit. The scores of both tools were calculated, and equipercentile equating was used to create a method for converting scores from the KDSQ to the IQCODE.Results: KDSQ scores were highly correlated with IQCODE scores (Pearson r = 0.905, P < 0.01). We developed scores for converting the KDSQ to the IQCODE using equipercentile equating and log-linear smoothing. We provide an easy-to-use table that enables the conversion of KDSQ scores to IQCODE scores.Conclusion: We delivered a simple and reliable method for converting scores from the KDSQ to the IQCODE. The conversion score table reported here enables direct and easy comparison of these cognitive measurements in older adults.Keywords: KDSQ, IQCODE, cognition, equation

Background: Prognostic prediction and the identification of prognostic factors are critical during the early period of atrial-fibrillation (AF)-related strokes as AF is associated with poor outcomes in stroke patients., Methods: Two independent datasets, namely, the Korean Atrial Fibrillation Evaluation Registry in Ischemic Stroke Patients (K-ATTENTION) and the Korea University Stroke Registry (KUSR), were used for internal and external validation, respectively. These datasets include common variables such as demographic, laboratory, and imaging findings during early hospitalization. Outcomes were unfavorable functional status with modified Rankin scores of 3 or higher and mortality at 3 months. We developed two machine learning models, namely, a tree-based model and a multi-layer perceptron (MLP), along with a baseline logistic regression model. The area under the receiver operating characteristic curve (AUROC) was used as the outcome metric. The Shapley additive explanation (SHAP) method was used to evaluate the contributions of variables., Results: Machine learning models outperformed logistic regression in predicting both outcomes. For 3-month unfavorable outcomes, MLP exhibited significantly higher AUROC values of 0.890 and 0.859 in internal and external validation sets, respectively, than those of logistic regression. For 3-month mortality, both machine learning models exhibited significantly higher AUROC values than the logistic regression for internal validation but not for external validation. The most significant predictor for both outcomes was the initial National Institute of Health and Stroke Scale., Conclusion: The explainable machine learning model can reliably predict short-term outcomes and identify high-risk patients with AF-related strokes., Competing Interests: J-MJ has received lecture honoraria from Pfizer, Sanofi-Aventis, Ostuka, Dong-A, and Hanmi Pharmaceutical Co., Ltd. and consulting fees from Daewoong Pharmaceutical Co., Ltd. W-KS received honoraria for lectures from Pfizer, Sanofi-Aventis, Otsuka Korea, and Dong-A Pharmaceutical Co., Ltd. The remaining authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest., (Copyright © 2023 Jeon, Jung, Yeo, Seo and Jung.)

Background: Isolated middle cerebral artery dissection is uncommon and occurs in patients reporting headaches as the only symptom. This makes intracranial artery dissection challenging to diagnose and treat.Case description: We describe two cases of positional headache caused by isolated middle cerebral artery dissection, confirmed using high-resolution magnetic resonance imaging. The two patients presented with sudden-onset headache, occurring when lying in the lateral decubitus position. When lying down in the decubitus position ipsilateral to the intracranial artery dissection, the headache aggravated and middle cerebral artery flow velocity increased on transcranial Doppler ultrasonography compared to when in the supine position. Both patients were treated with antiplatelet agents, and the headache completely resolved within 1-2 weeks., Conclusion: We recommend additional imaging studies evaluating intracranial artery dissection as a cause of positional headache.

Objective: To develop a machine learning algorithm to identify cognitive dysfunction based on neuropsychological screening test results., Methods: This retrospective study included 955 participants: 341 participants with dementia (dementia), 333 participants with mild cognitive impairment (MCI), and 341 participants who were cognitively healthy. All participants underwent evaluations including the Mini-Mental State Examination and the Montreal Cognitive Assessment. Each participant's caregiver or informant was surveyed using the Korean Dementia Screening Questionnaire at the same visit. Different machine learning algorithms were applied, and their overall accuracies, Cohen's kappa, receiver operating characteristic curves, and areas under the curve (AUCs) were calculated., Results: The overall screening accuracies for MCI, dementia, and cognitive dysfunction (MCI or dementia) using a machine learning algorithm were approximately 67.8% to 93.5%, 96.8% to 99.9%, and 75.8% to 99.9%, respectively. Their kappa statistics ranged from 0.351 to 1.000. The AUCs of the machine learning models were statistically superior to those of the competing screening model., Conclusion: This study suggests that a machine learning algorithm can be used as a supportive tool in the screening of MCI, dementia, and cognitive dysfunction.

Environment-friendly and sustainable routes for municipal solid waste (MSW) incineration bottom ash (IBA) recycling and utilization is one of the major concerns for the urbanized countries like Singapore. In this research paper, the possibility of bulk utilization of MSW-IBA as a catalyst support material has been explored for sustainable syn-gas production. The change in the texture of the IBA with simple hydrothermal treatment using NaOH has also been investigated. Furthermore, with hydrothermal treatment for 24 h at 180 °C, the texture of raw IBA with respect to basicity, surface area, total pore volume and reducibility was greatly improved. These textural properties are highly significant for a material to be utilized as a catalyst or catalytic supports for reforming applications. Ni supported on hydrothermally treated IBA was tested for steam reforming of biomass tar reforming reaction between 700 °C and 800 °C at relatively low steam-to-carbon ratio of 2. Among all the catalysts, Ni supported on IBA hydrothermally treated for 24 h gave stable toluene conversion (of 40%) at 700 °C with reduced coke formation (of 7.5 mgC/g·h) than other catalysts. The superior catalytic performance of this catalyst is mainly due to the presence of high amounts of surface Ni° species and improved reducibility and basicity properties among all. The Raman, DT/TGA and XRD analyses on spent catalysts revealed the deposited carbon during steam reforming of tar reaction is majorly amorphous. Due to this, the deposition of carbon did not show any kind of deactivation within the catalyst testing period., (Copyright © 2018. Published by Elsevier Ltd.)

Carbon mineralization is one of the carbon capture utilization, and storage (CCUS) technologies that can be used to capture large quantities of CO 2 and convert it into stable carbonate products that can be stored easily. Several CO 2 mineralization processes have been proposed; however, there are no commercial-scale projects because there are still significant issues that need to be improved before commercialization can take place. In this work, we evaluate the CO 2 and energy penalties related to the most well-known types of mineralization processes developed to date, in which the mineralization reaction takes place directly under aqueous conditions, high pressures and temperatures, and compared these with newer T-P swing processes and ball-mill reactor processes, which are under development. The data used in the evaluation are taken from published literature. By comparing the three processes, we identify important variables that contribute to high CO 2 and energy penalties so that future research can focus on optimization of these variables. It is observed that slurry concentration (heating) and particle size (grinding) are critical factors, with mineral calcination and operating pressure constituting other important factors., (© 2017 Wiley-VCH Verlag GmbH & Co. KGaA, Weinheim.)

As the current cost of mineral carbonation is too high for an economically viable industrial process, it is desirable to produce value-added products from CO2 mineralization process. In this work, a facile and cost-effective process was developed for the production of high purity SiO2 from acid-leached serpentine residue. The Si extraction rate is fast even under ambient conditions due to the highly defective structure of the residue. The reaction kinetics were studied and it was found that the Si extraction rate was under a combination of chemical reaction control and film diffusion control. The SiO2 sample prepared has high purity with a nanoporous structure, which renders it a potential candidate for applications such as an adsorbent and a catalyst support.

Even low concentrations of endotoxins can be life-threatening. As such, continuous effort has been directed toward the development of sensitive and specific endotoxin detection systems. In this paper, we report the design and fabrication of a new electrochemical endotoxin sensor based on a human recombinant toll-like receptor 4 (rhTLR4) and myeloid differentiation-2 (MD-2) complex. The rhTLR4/MD-2 complex, which specifically binds to endotoxin, was immobilized on gold electrodes through a self-assembled monolayer (SAM) technique involving the use of dithiobis(succinimidyl undecanoate) (DSU). The surface topography of the electrodes at each fabrication stage was characterized with a nanosurface profiler and atomic force microscope (AFM). The electrochemical signals generated from interactions between the rhTLR4/MD-2 complex and the endotoxin were characterized by cyclic voltammetry (CV) and differential pulse voltammetry (DPV). A linear relationship between the peak current and endotoxin concentration was obtained in the range of 0.0005 to 5 EU/mL with a correlation coefficient (R(2)) of 0.978. The estimated limit of detection (LOD) was fairly low, 0.0002 EU/mL. The rhTLR4/MD-2 based sensors exhibited no current responses to dipalmitoylphosphatidylcholine (DPPC) bearing two lipid chains, which is structurally similar to endotoxin, indicating the high specificity of the sensors to endotoxin., (Copyright © 2011 Elsevier B.V. All rights reserved.)

Although hyaluronic acid (HA) has been widely used in clinics as an injectable biomaterial, it may not be appropriate as an injectable stem cell carrier because highly hydrophilic HA hydrogels provide an unfavorable environment in which the encapsulated stem cells are likely to be constrained to a round shape, thereby losing their native morphology. Herein, we hypothesized that dextran microspheres (DMs) can improve stem cell viability in HA hydrogels because they can act as substrates for stem cell adhesion, spreading and proliferation. DMs with a mean diameter of 80 μm were mixed with HA hydrogels. Human adipose-derived stem cells (hASCs) were isolated from human adipose tissue and seeded into the DM-incorporated HA hydrogels. When compared with the hydrogels alone, the number of viable cells was significantly increased in the presence of the DMs. Initially, hASCs appeared to be round in the HA hydrogels. At 12 h after seeding, the hASCs apparently attached onto the DMs and became slightly flattened. One day after seeding, the hASCs seemed to spread onto the surface of the DMs. Fluorescence micrography of live and dead cells confirmed that the cell viability was significantly improved by use of the DMs in HA hydrogels. Overall results demonstrated that the microsphere/hydrogel composite supported stem cell survival and spreading. These characteristics show the potential for use of the composite in cell-delivery and tissue-engineering applications.