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On-chip physiological mimicry of neurovascular unit: challenges and perspectives

Neural Regeneration Research 2024年第3期19卷 499-500页

作者： Song Ih Ahn yongtae kim School of Mechanical Engineering Pusan National UniversityBusanSouth Korea George W.Woodruff School of Mechanical Engineering Georgia Institute of TechnologyAtlantaGAUSA Parker H.Petit Institute for Bioengineering and Bioscience Georgia Institute of TechnologyAtlantaGAUSA Wallace H.Coulter Department of Biomedical Engineering Georgia Institute of TechnologyAtlantaGAUSA Institute for Electronics and Nanotechnology Georgia Institute of TechnologyAtlantaGAUSA

Neurological disorders including neurodegenerative diseases,brain tumors,and stroke are the second leading cause of death and the greatest cause of disability ***,it remains challenging to achieve effective drug delivery to the central nervous system for treatments of neurological diseases due to the blood-brain barrier(BBB).The function of the BBB is regulated by the physiological interactions between various types of cells in the neurovascular unit(NVU).In the NVU,the brain vasculature of the BBB is surrounded by brain pericytes,brain astrocytes,neurons,and microglia(Figure 1).Moreover,the NVU at the levels of arteries and veins includes contractile smooth muscle cells(Schaeffer and Iadecola,2021).

关键词： diseases death arteries

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High-density lipoprotein mimetic nanotherapeutics for cardiovascular and neurodegenerative diseases

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Nano Research 2018年第10期11卷 5130-5143页

作者： Song Ih Ahn Hyun-Ji Park diwon Yom Taeyoung kim yongtae kim George W. Woodruff School of Mechanical Engineering Georgia Institute of Technology Atlanta GA 30332 USA Parker H. Petit Institute for Bioengineering and Bioscience Georgia Institute of Technology Atlanta GA 30332 USA Wallace H. Coulter Department of Biomedical Engineering Georgia Institute of Technology Atlanta GA 30332 USA Institute for Electronics and Nanotechnology Georgia Institute of Technology Atlanta GA 30332 USA

High-density lipoprotein （HDL） serves as a natural nanoparticle with compositional and functional heterogeneity and contributes to the maintenance of lipid metabolism and various biological functions. HDL also transports endogenous microRNAs, vitamins, hormones, and proteins through blood and interstitial fluids to various organs. These unique and diverse features of HDL have encouraged its applications for the transport of therapeutics and diagnostic reagents in the last decade. In this review, we describe the heterogeneous characteristics and biological functions of HDL and highlight HDL mimetic approaches, including apolipoprotein mimetic peptides and reconstituted HDL nanoparticles. Given the potential of HDL as a nanocarrier delivering various therapeutic agents, we discuss the current representative studies of HDL mimetic nanotherapeutics for cardiovascular and neurodegenerative diseases and analyze the current challenges and future perspective.

关键词： high-density lipoprotein nanotherapeutic cardiovascular disease neurodegenerative disease

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Deep learning framework for material design space exploration using active transfer learning and data augmentation

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npj Computational Materials 2021年第1期7卷 1261-1267页

作者： yongtae kim Youngsoo kim Charles Yang Kundo Park Grace X.Gu Seunghwa Ryu Department of Mechanical Engineering&KI for the NanoCentury Korea Advanced Institute of Science and Technology(KAIST)Yuseong-guDaejeonRepublic of Korea Department of Nature-Inspired System and Application Korea Institute of Machinery and MaterialsDaejeonRepublic of Korea Department of Mechanical Engineering University of CaliforniaBerkeleyCAUSA

Neural network-based generative models have been actively investigated as an inverse design method for finding novel materials in a vast design ***,the applicability of conventional generative models is limited because they cannot access data outside the range of training *** generative models that were devised to overcome the limitation also suffer from the weak predictive power on the unseen *** this study,we propose a deep neural network-based forward design approach that enables an efficient search for superior materials far beyond the domain of the initial training *** approach compensates for the weak predictive power of neural networks on an unseen domain through gradual updates of the neural network with active transfer learning and data augmentation *** demonstrate the potential of our framework with a grid composite optimization problem that has an astronomical number of possible design *** show that our proposed framework can provide excellent designs close to the global optima,even with the addition of a very small dataset corresponding to less than 0.5%of the initial training dataset size.

关键词： neural Deep network

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