A case of gastroduodenal ulcer complicating Kawasaki disease

Kawasaki disease (KD) is a systemic vasculitis associated with various clinical manifestations and complications, such as gastrointestinal abnormalities. We report a 3-year-old boy who presented with hematemesis and diffuse gastroduodenal ulcerations complicating KD. He received standard medical therapy for the disease and gastric ulcer, which showed effect after a few days. Although rare, peptic ulcers should be considered a complication of KD to ensure early diagnosis and treatment as it may cause severe morbidity

Melting Domain Size and Recrystallization Dynamics of Ice Revealed by Time-Resolved X-ray Scattering

The phase transition between water and ice is ubiquitous and one of the most important phenomena in nature. Here, we performed time-resolved x-ray scattering experiments capturing the melting and recrystallization dynamics of ice. The ultrafast heating of ice I is induced by an IR laser pulse and probed with an intense x-ray pulse, which provided us with direct structural information on different length scales. From the wide-angle x-ray scattering (WAXS) patterns, the molten fraction, as well as the corresponding temperature at each delay, were determined. The small-angle x-ray scattering (SAXS) patterns, together with the information extracted from the WAXS analysis, provided the time-dependent change of the size and the number of the liquid domains. The results show partial melting (~13 %) and superheating of ice occurring at around 20 ns. After 100 ns, the average size of the liquid domains grows from about 2.5 nm to 4.5 nm by the coalescence of approximately six adjacent domains. Subsequently, we capture the recrystallization of the liquid domains, which occurs on microsecond timescales due to the cooling by heat dissipation and results to a decrease of the average liquid domain size

Validation of Biomarker-Based ABCD Score in Atrial Fibrillation Patients with a Non-Gender CHA2DS2-VASc Score 0-1:A Korean Multi-Center Cohort

PURPOSE: Atrial fibrillation (AF) patients with low to intermediate risk, defined as non-gender CHA(2)DS(2)-VASc score of 0–1, are still at risk of stroke. This study verified the usefulness of ABCD score [age (≥60 years), B-type natriuretic peptide (BNP) or N-terminal pro-BNP (≥300 pg/mL), creatinine clearance (<50 mL/min/1.73 m(2)), and dimension of the left atrium (≥45 mm)] for stroke risk stratification in non-gender CHA(2)DS(2)-VASc score 0–1. MATERIALS AND METHODS: This multi-center cohort study retrospectively analyzed AF patients with non-gender CHA(2)DS(2)-VASc score 0–1. The primary endpoint was the incidence of stroke with or without antithrombotic therapy (ATT). An ABCD score was validated. RESULTS: Overall, 2694 patients [56.3±9.5 years; female, 726 (26.9%)] were followed-up for 4.0±2.8 years. The overall stroke rate was 0.84/100 person-years (P-Y), stratified as follows: 0.46/100 P-Y for an ABCD score of 0; 1.02/100 P-Y for an ABCD score ≥1. The ABCD score was superior to non-gender CHA(2)DS(2)-VASc score in the stroke risk stratification (C-index=0.618, p=0.015; net reclassification improvement=0.576, p=0.040; integrated differential improvement=0.033, p=0.066). ATT was prescribed in 2353 patients (86.5%), and the stroke rate was significantly lower in patients receiving non-vitamin K antagonist oral anticoagulant (NOAC) therapy and an ABCD score ≥1 than in those without ATT (0.44/100 P–Y vs. 1.55/100 P-Y; hazard ratio=0.26, 95% confidence interval 0.11–0.63, p=0.003). CONCLUSION: The biomarker-based ABCD score demonstrated improved stroke risk stratification in AF patients with non-gender CHA(2)DS(2)-VASc score 0–1. Furthermore, NOAC with an ABCD score ≥1 was associated with significantly lower stroke rate in AF patients with non-gender CHA(2)DS(2)-VASc score 0–1

Development and Verification of Time-Series Deep Learning for Drug-Induced Liver Injury Detection in Patients Taking Angiotensin II Receptor Blockers: A Multicenter Distributed Research Network Approach

Objectives The objective of this study was to develop and validate a multicenter-based, multi-model, time-series deep learning model for predicting drug-induced liver injury (DILI) in patients taking angiotensin receptor blockers (ARBs). The study leveraged a national-level multicenter approach, utilizing electronic health records (EHRs) from six hospitals in Korea. Methods A retrospective cohort analysis was conducted using EHRs from six hospitals in Korea, comprising a total of 10,852 patients whose data were converted to the Common Data Model. The study assessed the incidence rate of DILI among patients taking ARBs and compared it to a control group. Temporal patterns of important variables were analyzed using an interpretable time-series model. Results The overall incidence rate of DILI among patients taking ARBs was found to be 1.09%. The incidence rates varied for each specific ARB drug and institution, with valsartan having the highest rate (1.24%) and olmesartan having the lowest rate (0.83%). The DILI prediction models showed varying performance, measured by the average area under the receiver operating characteristic curve, with telmisartan (0.93), losartan (0.92), and irbesartan (0.90) exhibiting higher classification performance. The aggregated attention scores from the models highlighted the importance of variables such as hematocrit, albumin, prothrombin time, and lymphocytes in predicting DILI. Conclusions Implementing a multicenter-based time-series classification model provided evidence that could be valuable to clinicians regarding temporal patterns associated with DILI in ARB users. This information supports informed decisions regarding appropriate drug use and treatment strategies

Development and application of microbial fuel cells to relax reduced bottom layer in coastal areas

広島大学(Hiroshima University)博士(工学)Doctor of Engineeringdoctora

A Compact Memristor Model Based on Physics-Informed Neural Networks

Memristor devices have diverse physical models depending on their structure. In addition, the physical properties of memristors are described using complex differential equations. Therefore, it is necessary to integrate the various models of memristor into an unified physics-based model. In this paper, we propose a physics-informed neural network (PINN)-based compact memristor model. PINNs can solve complex differential equations intuitively and with ease. This methodology is used to conduct memristor physical analysis. The weight and bias extracted from the PINN are implemented in a Verilog-A circuit simulator to predict memristor device characteristics. The accuracy of the proposed model is verified using two memristor devices. The results show that PINNs can be used to extensively integrate memristor device models

Enhanced power performance of an in situ sediment microbial fuel cell with steel-slag as the redox catalyst: I. electricity generation

Steel-slag was added as a catalytic material of redox reaction to sediment microbial fuel cells (SMFCs) in an in situ sediment biodegradation experiment to determine its benefits in the long-term power production of the cells and to elucidate the external factors affecting power production. The steel-slag was found to consistently increase the power generated, and over a period of two years, the combination produced six times more charge than that of an SMFC alone (1549 C per day), which was superior to that of conventional SMFCs reported in the literature. After two years of electron-harvesting, the redox potential of pore water rose to the level at which hydrogen sulfide and ammonia can be oxidized. Upon investigation, rapid changes or fluctuations in power production were found to be cathode-related, for example, if it was damaged, or variations in the ambient environmental conditions, such as dissolved oxygen, algal activity, etc., affected its performance. In particular, algal activity appeared within nine days, which led to a 68% increase in the average current density during the day vs. the night.アクセプト後にタイトル・アブストラクト等変更あり、著者最終稿は変更前のタイトル"In situ sediment biodegradation via a sediment microbial fuel cell and steel-slag: I. Electricity generation