Continual learning framework for a multicenter study with an application to electrocardiogram.

Deep learning has been increasingly utilized in the medical field and achieved many goals. Since the size of data dominates the performance of deep learning, several medical institutions are conducting joint research to obtain as much data as possible. However, sharing data is usually prohibited owing to the risk of privacy invasion.

Development of an artificial intelligence bacteremia prediction model and evaluation of its impact on physician predictions focusing on uncertainty.

Prediction of bacteremia is a clinically important but challenging task. An artificial intelligence (AI) model has the potential to facilitate early bacteremia prediction, aiding emergency department (ED) physicians in making timely decisions and reducing unnecessary medical costs. In this study, we developed and externally validated a Bayesian neural network-based AI bacteremia prediction model (AI-BPM).

A practical approach based on learning-based model predictive control with minimal prior knowledge of patients for artificial pancreas.

Background And Objectives: Complete identification of the glucose dynamics for a patient generally requires prior clinical procedures and several measurements for the patient. However, these steps may not be always feasible. To address this limitation, we propose a practical approach integrating learning-based model predictive control (MPC), adaptive basal and bolus injections, and suspension with minimal requirements of prior knowledge of the patient.

A development of a graph-based ensemble machine learning model for skin sensitization hazard and potency assessment.

Many defined approaches (DAs) for skin sensitization assessment based on the adverse outcome pathway (AOP) have been developed to replace animal testing because the European Union has banned animal testing for cosmetic ingredients. Several DAs have demonstrated that machine learning models are beneficial. In this study, we have developed an ensemble prediction model utilizing the graph convolutional network (GCN) and machine learning approach to assess skin sensitization.

Multi-Task Disentangled Autoencoder for Time-Series Data in Glucose Dynamics.

The objective of this study is to propose MD-VAE: a multi-task disentangled variational autoencoders (VAE) for exploring characteristics of latent representations (LR) and exploiting LR for diverse tasks including glucose forecasting, event detection, and temporal clustering. We applied MD-VAE to one virtual continuous glucose monitoring (CGM) data from an FDA-approved Type 1 Diabetes Mellitus simulator (T1DMS) and one publicly available CGM data of real patients for glucose dynamics of Type 1 Diabetes Mellitus. LR captured meaningful information to be exploited for diverse tasks, and was able to differentiate characteristics of sequences with clinical parameters.

3D cell culture using a clinostat reproduces microgravity-induced skin changes.

Exposure to microgravity affects human physiology in various ways, and astronauts frequently report skin-related problems. Skin rash and irritation are frequent complaints during space missions, and skin thinning has also been reported after returning to Earth. However, spaceflight missions for studying the physiological changes in microgravity are impractical.

Hyoid kinematic features for poor swallowing prognosis in patients with post-stroke dysphagia.

Identification of prognostic factors for swallowing recovery in patients with post-stroke dysphagia is crucial for determining therapeutic strategies. We aimed at exploring hyoid kinematic features of poor swallowing prognosis in patients with post-stroke dysphagia. Of 122 patients who experienced dysphagia following ischemic stroke, 18 with poor prognosis, and 18 age- and sex-matched patients with good prognosis were selected and retrospectively reviewed.

Development of a Novel Prognostic Model to Predict 6-Month Swallowing Recovery After Ischemic Stroke.

Background and Purpose- The aim of this study was to explore clinical and radiological prognostic factors for long-term swallowing recovery in patients with poststroke dysphagia and to develop and validate a prognostic model using a machine learning algorithm. Methods- Consecutive patients (N=137) with acute ischemic stroke referred for swallowing examinations were retrospectively reviewed. Dysphagia was monitored in the 6 months poststroke period and then analyzed using the Kaplan-Meier method and Cox regression model for clinical and radiological factors.

Crack Width Estimation of Mortar Specimen Using Gas Diffusion Experiment.

Maintenance of structures using self-healing concrete technologies has recently been actively studied. However, unlike the technological development of self-healing concrete, research focused on evaluating the self-healing performance is insufficient. Although water permeability experiments are widely used, the reliability of the test results may be reduced due to the viscosity of water and the possibility of elution of material inside the specimen.

Differential kinematic features of the hyoid bone during swallowing in patients with Parkinson's disease.

This study aimed to investigate spatiotemporal characteristics of the hyoid bone during swallowing in patients with Parkinson's disease (PD) and dysphagia. Spatiotemporal data of the hyoid bone was obtained from videofluoroscopic images of 69 subjects (23 patients with PD, 23 age- and sex-matched healthy elderly controls, and 23 healthy young controls). Normalized profiles of displacement/velocity were analyzed during different periods (percentile) of swallowing using functional regression analysis, and the maximal values were compared between the groups.

Microgravity induces autophagy via mitochondrial dysfunction in human Hodgkin's lymphoma cells.

Gravitational forces can impose physical stresses on the human body as it functions to maintain homeostasis. It has been reported that astronauts exposed to microgravity experience altered biological functions and many subsequent studies on the effects of microgravity have therefore been conducted. However, the anticancer mechanisms of simulated microgravity remain unclear.

Fimasartan increases glucose-stimulated insulin secretion in patients with type 2 diabetes and hypertension compared with amlodipine.

Aim: To study the effects of angiotensin receptor blockers (ARBs) on insulin secretion in hypertensive patients with type 2 diabetes.

Materials And Methods: A total of 41 patients were enrolled in this open-label, active comparator-controlled, crossover study. After a 2-week run-in period with amlodipine, the participants were assigned to receive either fimasartan (60-120 mg daily) or amlodipine (5-10 mg daily) for 16 weeks.

Synthesis and characterization of heteroleptic titanium MOCVD precursors for TiO thin films.

Heteroleptic titanium alkoxides with three different ligands, i.e., [Ti(OPr)(X)(Y)] (X = tridentate, Y = bidentate ligands), were synthesized to find efficient metal organic chemical vapor deposition (MOCVD) precursors for TiO thin films.

Application of the Oral Minimal Model to Korean Subjects with Normal Glucose Tolerance and Type 2 Diabetes Mellitus.

Background: The oral minimal model is a simple, useful tool for the assessment of β-cell function and insulin sensitivity across the spectrum of glucose tolerance, including normal glucose tolerance (NGT), prediabetes, and type 2 diabetes mellitus (T2DM) in humans.

Methods: Plasma glucose, insulin, and C-peptide levels were measured during a 180-minute, 75-g oral glucose tolerance test in 24 Korean subjects with NGT (n=10) and T2DM (n=14). The parameters in the computational model were estimated, and the indexes for insulin sensitivity and β-cell function were compared between the NGT and T2DM groups.

Computational Discrimination of Breast Cancer for Korean Women Based on Epidemiologic Data Only.

Breast cancer is the second leading cancer for Korean women and its incidence rate has been increasing annually. If early diagnosis were implemented with epidemiologic data, the women could easily assess breast cancer risk using internet. National Cancer Institute in the United States has released a Web-based Breast Cancer Risk Assessment Tool based on Gail model.