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Construction of neurospheroids via surface modified concave microwells
Lee, GeonHui,Lim, Jaeho,Park, JiSoo,Lee, Wonseok,Yoon, Dae Sung,Kim, Soo Hyun,Kim, Myung-Ki,Lee, Sang-Hoon,Kim, Dong-Hwee THE KOREAN SOCIETY OF INDUSTRIAL AND ENGINEERING 2018 JOURNAL OF INDUSTRIAL AND ENGINEERING CHEMISTRY -S Vol.62 No.-
<P><B>Abstract</B></P> <P>Developing a three-dimensional (3D) neural tissue model is important to comprehensively understand neural development and neuronal degeneration associated with various neurological disorders such as axonopathy and neuronopathy. Here, a new microplatform suitable for constructing neuronal spheroids (neurospheroids) was developed by modulating cell–surface interactions. The inner surface of a polydimethylsiloxane (PDMS) concave microwell array extensively used in <I>in vitro</I> cell aggregation was modified with typical extracellular matrix (ECM) molecules or carbon nanotubes to control neural spheroid formation. Modulating neuronal cell–ECM interactions could tune 3D intercellular interactions and spheroidal functionality. Neurite outgrowth, a neuronal marker for complex interneuronal signaling, was found to be tightly regulated by cell–ECM interactions in a confined space. Furthermore, amyloid-β (Aβ)-induced axonopathy representing a pathological feature of neurodegenerative diseases <I>in vivo</I> was examined in this study to monitor the degeneration of neurite outgrowth and alteration of neuronal morphology in these neurospheroids. The proposed neural tissue model could be used to study various neurodegenerative diseases in the future.</P> <P><B>Graphical abstract</B></P> <P>[DISPLAY OMISSION]</P>
Nonmediated, Label-Free Based Detection of Cardiovascular Biomarker in a Biological Sample
Lee, JuKyung,Shin, SuRyon,Desalvo, Anna,Lee, Geonhui,Lee, Jeong Yoon,Polini, Alessandro,Chae, Sukyoung,Jeong, Hobin,Kim, Jonghan,Choi, Haksoo,Lee, HeaYeon Wiley - VCH Verlag GmbH & Co. KGaA 2017 Advanced Healthcare Materials Vol.6 No.17
Lee, GeonHui,Jun, Yesl,Jang, HeeYeong,Yoon, Junghyo,Lee, JaeSeo,Hong, MinHyung,Chung, Seok,Kim, Dong-Hwee,Lee, SangHoon Elsevier Science B.V. Amsterdam 2018 ACTA BIOMATERIALIA Vol. No.
<P><B>Abstract</B></P> <P>Oxygen availability is a critical factor in regulating cell viability that ultimately contributes to the normal morphogenesis and functionality of human tissues. Among various cell culture platforms, construction of 3D multicellular spheroids based on microwell arrays has been extensively applied to reconstitute <I>in vitro</I> human tissue models due to its precise control of tissue culture conditions as well as simple fabrication processes. However, an adequate supply of oxygen into the spheroidal cellular aggregation still remains one of the main challenges to producing healthy <I>in vitro</I> spheroidal tissue models. Here, we present a novel design for controlling the oxygen distribution in concave microwell arrays. We show that oxygen permeability into the microwell is tightly regulated by varying the poly-dimethylsiloxane (PDMS) bottom thickness of the concave microwells. Moreover, we validate the enhanced performance of the engineered microwell arrays by culturing non-proliferated primary rat pancreatic islet spheroids on varying bottom thickness from 10 μm to 1050 μm. Morphological and functional analyses performed on the pancreatic islet spheroids grown for 14 days prove the long-term stability, enhanced viability, and increased hormone secretion under the sufficient oxygen delivery conditions. We expect our results could provide knowledge on oxygen distribution in 3-dimensional spheroidal cell structures and critical design concept for tissue engineering applications.</P> <P><B>Statement of Significance</B></P> <P>In this study, we present a noble design to control the oxygen distribution in concave microwell arrays for the formation of highly functional pancreatic islet spheroids by engineering the bottom of the microwells. Our new platform significantly enhanced oxygen permeability that turned out to improve cell viability and spheroidal functionality compared to the conventional thick-bottomed 3-D culture system. Therefore, we believe that this could be a promising medical biotechnology platform to further develop high-throughput tissue screening system as well as <I>in vivo</I>-mimicking customised 3-D tissue culture systems.</P> <P><B>Graphical abstract</B></P> <P>[DISPLAY OMISSION]</P>
정상 충방전 데이터에 기반한 리튬 이온 배터리 고장 탐지
이건희(Geonhui Lee) 한국정보과학회 2021 정보과학회 컴퓨팅의 실제 논문지 Vol.27 No.1
배터리의 고장은 탑재된 응용 전체의 성능 저하를 일으킬 뿐만 아니라, 심각한 사고로도 이어질 수 있기 때문에 고장 배터리의 진단은 매우 중요한 문제이다. 하지만 진단을 위해 고장 배터리를 확보하는 것이 현실적으로 어렵기 때문에, 정상 배터리의 데이터만을 활용해서 배터리의 고장을 찾아낼 수 있어야 한다. 이 논문에서는 머신 러닝에 기반해, 정상적인 배터리만을 사용해 고장을 진단할 수 있는 기술을 최초로 제시한다. 제안 방법론은 배터리의 정상 데이터와 이를 구성하는 인자 사이의 관계를 정의하고, 그 관계를 정확하게 예측할 수 있는 모델을 만들어 임의의 방전 프로필이 모델에 얼마나 적합한지 확인하는 방식으로 이루어진다. 제안 방법론을 검증하기 위해, NCR18650B 배터리를 사용한 실험을 진행하였고, 과방전 및 과충전 고장 데이터를 만들어 이를 구분하는지 확인했다. 그 결과, 정상 배터리 데이터의 약 96%를 정상으로 진단하는 모델을 이용해 약 97%의 정확도로 고장 배터리를 진단했다. The diagnosis of the lithium-ion battery is an important problem because of the degradation of entire application performance, as well as also the serious accidents by faulty batteries. Considering the issues of battery diagnosis, only normal battery data should be used to detect the faulty batteries, because it is practically impossible to obtain a faulty battery and its data. In this paper, we propose the first diagnosis approach using only the normal battery data based on machine learning. The approach is as follows. We define the normal battery data and the relationship between the features that constitute it, then we derive a model that can accurately predict the normal batteries. We determine the faults of the given unknown batteries by verifying how poorly the given discharging data fits the model. We evaluated the proposed methodology by conducting a case study using the NCR18650B batteries and constructing the normal battery model. We made a sample of the abnormal battery data from the overdischarge(OD) and overcharge(OC) batteries to verify if it can be diagnosed through the normal battery model. The experimental results showed that the accuracy to diagnose normal data as normal battery was approximately 96%, and the accuracy to diagnose abnormal data as faulty battery was approximately 97%.