• The Journal of Korean Medicine
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• v.44 no.4
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• pp.87-103
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• 2023

Backgrounds and objectives: Cardiotonic Pills^® (CP) are used for vascular diseases such as coronary diseases, atherosclerosis, and cerebral infarction. This study aimed to determine the transient effects of CP on cerebrovascular CO₂ reactivity (CVR) and erythrocyte deformability in normal subjects. Methods: This study had a crossover design and included 10 participants who were randomly allocated to 2 groups. The experimental group was given CP with water, while the control group was given only water. CVR was measured by hyperventilation-induced CVR of the middle cerebral artery (MCA) using transcranial Doppler (TCD). Erythrocyte deformability was measured using a Rheoscan-D microfluidic ektacytometer. All measurements were performed prior to and 1, 2, and 3 hours after CP or water administration. Blood pressure and heart rate were also measured before and after administration. Results: CP significantly improved CVR 3 hours after administration in the experimental group compared to the control group (p = 0.042). The corrected blood flow velocity at partial pressure of end-tidal carbon dioxide (PETCO₂) = 40mmHg (CV40) was also significantly improved 2 and 3 hours after administration in the CP group compared to the control group (p = 0.036 and p = 0.021, respectively). CP significantly improved erythrocyte deformability 3 hours after administration in the experimental group compared to the control group (p = 0.027). Mean heart rate and mean blood pressure showed no change. Conclusions: This study demonstrated that CP increases CVR and erythrocyte deformability. These results suggested that CP improves cerebral microcirculation which provide evidence for the future use of CP for prevention of ischemic stroke and neurodegenerative diseases.

• Korean Chemical Engineering Research
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• v.62 no.3
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• pp.274-280
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• 2024

Machine learning techniques utilizing neural networks have been employed in various fields such as disease gene discovery and diagnosis, drug development, and prediction of drug-induced liver injury. Disease features can be investigated by molecular information of DNA. In this study, we developed a neural network to predict the length of DNA and the number of DNA species in mixture solution which are representative molecular information of DNA. In order to address the time-consuming limitations of gel electrophoresis as conventional analysis, we analyzed the dynamic data of a microfluidic concentrating device. The dynamic data were reconstructed into a spatiotemporal map, which reduced the computational cost required for training and prediction. We employed a convolutional neural network to enhance the accuracy to analyze the spatiotemporal map. As a result, we successfully performed single DNA length prediction as single-variable regression, simultaneous prediction of multiple DNA lengths as multivariable regression, and prediction of the number of DNA species in mixture as binary classification. Additionally, based on the composition of training data, we proposed a solution to resolve the problem of prediction bias. By utilizing this study, it would be effectively performed that medical diagnosis using optical measurement such as liquid biopsy of cell-free DNA, cancer diagnosis, etc.

• Journal of Life Science
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• v.34 no.5
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• pp.339-355
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• 2024

The pulmonary system is a highly complex system that can only be understood by integrating its functional and structural aspects. Hence, in vivo animal models are generally used for pathological studies of pulmonary diseases and the evaluation of inhalation toxicity. However, to reduce the number of animals used in experimentation and with the consideration of animal welfare, alternative methods have been extensively developed. Notably, the Organization for Economic Co-operation and Development (OECD) and the United States Environmental Protection Agency (USEPA) have agreed to prohibit animal testing after 2030. Therefore, the latest advances in biotechnology are revolutionizing the approach to developing in vitro inhalation models. For example, lung organ-on-a-chip (OoC) and organoid models have been intensively studied alongside advancements in three-dimensional (3D) bioprinting and microfluidic systems. These modeling systems can more precisely imitate the complex biological environment compared to traditional in vivo animal experiments. This review paper addresses multiple aspects of the recent in vitro modeling systems of lung OoC and organoids. It includes discussions on the use of endothelial cells, epithelial cells, and fibroblasts composed of lung alveoli generated from pluripotent stem cells or cancer cells. Moreover, it covers lung air-liquid interface (ALI) systems, transwell membrane materials, and in silico models using artificial intelligence (AI) for the establishment and evaluation of in vitro pulmonary systems.