scholarly journals Real-time monitoring in three-dimensional hepatocytes reveals that insulin acts as a synchronizer for liver clock

2012 ◽  
Vol 2 (1) ◽  
Author(s):  
Daisuke Yamajuku ◽  
Takahiko Inagaki ◽  
Tomonori Haruma ◽  
Shingo Okubo ◽  
Yutaro Kataoka ◽  
...  
Keyword(s):  
Real Time ◽  
Three Dimensional ◽  
Real Time Monitoring

scholarly journals Multi-Organs-on-Chips: Towards Long-Term Biomedical Investigations

Molecules ◽  
2019 ◽  
Vol 24 (4) ◽  
pp. 675 ◽  
Author(s):  
Yi Zhao ◽  
Ranjith Kankala ◽  
Shi-Bin Wang ◽  
Ai-Zheng Chen
Keyword(s):  
Real Time ◽  
Three Dimensional ◽  
Drug Efficacy ◽  
Dynamic Responses ◽  
Physical Parameters ◽  
Real Time Monitoring ◽  
Multiple Organs ◽  
On Chip

With advantageous features such as minimizing the cost, time, and sample size requirements, organ-on-a-chip (OOC) systems have garnered enormous interest from researchers for their ability for real-time monitoring of physical parameters by mimicking the in vivo microenvironment and the precise responses of xenobiotics, i.e., drug efficacy and toxicity over conventional two-dimensional (2D) and three-dimensional (3D) cell cultures, as well as animal models. Recent advancements of OOC systems have evidenced the fabrication of ‘multi-organ-on-chip’ (MOC) models, which connect separated organ chambers together to resemble an ideal pharmacokinetic and pharmacodynamic (PK-PD) model for monitoring the complex interactions between multiple organs and the resultant dynamic responses of multiple organs to pharmaceutical compounds. Numerous varieties of MOC systems have been proposed, mainly focusing on the construction of these multi-organ models, while there are only few studies on how to realize continual, automated, and stable testing, which still remains a significant challenge in the development process of MOCs. Herein, this review emphasizes the recent advancements in realizing long-term testing of MOCs to promote their capability for real-time monitoring of multi-organ interactions and chronic cellular reactions more accurately and steadily over the available chip models. Efforts in this field are still ongoing for better performance in the assessment of preclinical attributes for a new chemical entity. Further, we give a brief overview on the various biomedical applications of long-term testing in MOCs, including several proposed applications and their potential utilization in the future. Finally, we summarize with perspectives.

scholarly journals Noninvasive Real-Time Monitoring by AlamarBlue®DuringIn VitroCulture of Three-Dimensional Tissue-Engineered Bone Constructs

2013 ◽  
Vol 19 (9) ◽  
pp. 720-729 ◽  
Author(s):  
Xiaohua Zhou ◽  
Inge Holsbeeks ◽  
Saartje Impens ◽  
Maarten Sonnaert ◽  
Veerle Bloemen ◽  
...  
Keyword(s):  
Real Time ◽  
Three Dimensional ◽  
Real Time Monitoring

scholarly journals A Microfluidic Chip Embracing a Nanofiber Scaffold for 3D Cell Culture and Real-Time Monitoring

Nanomaterials ◽  
2019 ◽  
Vol 9 (4) ◽  
pp. 588 ◽  
Author(s):  
Jeong Hwa Kim ◽  
Ju Young Park ◽  
Songwan Jin ◽  
Sik Yoon ◽  
Jong-Young Kwak ◽  
...  
Keyword(s):  
Cell Culture ◽  
Real Time ◽  
Microfluidic Chip ◽  
Three Dimensional ◽  
3D Culture ◽  
3D Cell Culture ◽  
Culture Conditions ◽  
Real Time Monitoring ◽  
Nanofiber Scaffold

Recently, three-dimensional (3D) cell culture and tissue-on-a-chip application have attracted attention because of increasing demand from the industries and their potential to replace conventional two-dimensional culture and animal tests. As a result, numerous studies on 3D in-vitro cell culture and microfluidic chip have been conducted. In this study, a microfluidic chip embracing a nanofiber scaffold is presented. A electrospun nanofiber scaffold can provide 3D cell culture conditions to a microfluidic chip environment, and its perfusion method in the chip can allow real-time monitoring of cell status based on the conditioned culture medium. To justify the applicability of the developed chip to 3D cell culture and real-time monitoring, HepG2 cells were cultured in the chip for 14 days. Results demonstrated that the cells were successfully cultured with 3D culture-specific-morphology in the chip, and their albumin and alpha-fetoprotein production was monitored in real-time for 14 days.

A wireless real-time monitoring system on intelligent underwater pollution cleaning robot

2021 ◽  
pp. 1-10
Author(s):  
Xiaoyan Wu ◽  
Shu Wang ◽  
Xinnan Wang ◽  
Guogeng He
Keyword(s):  
Real Time ◽  
Monitoring System ◽  
Three Dimensional ◽  
River Pollution ◽  
Dimensional Structure ◽  
Real Time Monitoring ◽  
Scheme Design ◽  
Wireless Monitoring ◽  
Cleaning Robot ◽  
Pollution Removal

Intelligent underwater pollution cleaning robot is used to release microbial solution which can dissolve into water slowly into polluted river, so that the solution can react fully with pollutants, so as to achieve the purpose of river pollution control. The research of robot wireless monitoring system is based on the comprehensive application of wireless communication technology and intelligent control technology, in order to achieve real-time monitoring and centralized remote control of underwater pollution removal. Through the three-dimensional structure modeling of the intelligent underwater pollution cleaning robot, the overall scheme design and debugging test of the wireless monitoring system, it is proved that the intelligent underwater pollution cleaning robot is feasible in the intelligent and efficient underwater cleaning operation, and it is a research method worthy of reference and promotion.

scholarly journals Abstract 833: Alginate hydrogels for three-dimensional culture and real-time monitoring of cancer cell metabolism and radiation response

2017 ◽  
Author(s):  
Aparna H. Kesarwala ◽  
Jenna L. Carter ◽  
Graham H. Read ◽  
Natsuko Miura ◽  
Kazutoshi Yamamoto ◽  
...  
Keyword(s):  
Real Time ◽  
Cancer Cell ◽  
Cell Metabolism ◽  
Three Dimensional ◽  
Radiation Response ◽  
Real Time Monitoring ◽  
Alginate Hydrogels ◽  
Cancer Cell Metabolism ◽  
Three Dimensional Culture

scholarly journals Real-time monitoring of driver drowsiness on mobile platforms using 3D neural networks

2019 ◽  
Vol 32 (13) ◽  
pp. 9731-9743 ◽  
Author(s):  
Jasper S. Wijnands ◽  
Jason Thompson ◽  
Kerry A. Nice ◽  
Gideon D. P. A. Aschwanden ◽  
Mark Stevenson
Keyword(s):  
Real Time ◽  
Three Dimensional ◽  
Cost Effective ◽  
Smartphone Application ◽  
Fine Tuning ◽  
Detection Methods ◽  
Crash Risk ◽  
Real Time Monitoring ◽  
Drowsiness Detection ◽  
Driver Drowsiness

Abstract Driver drowsiness increases crash risk, leading to substantial road trauma each year. Drowsiness detection methods have received considerable attention, but few studies have investigated the implementation of a detection approach on a mobile phone. Phone applications reduce the need for specialised hardware and hence, enable a cost-effective roll-out of the technology across the driving population. While it has been shown that three-dimensional (3D) operations are more suitable for spatiotemporal feature learning, current methods for drowsiness detection commonly use frame-based, multi-step approaches. However, computationally expensive techniques that achieve superior results on action recognition benchmarks (e.g. 3D convolutions, optical flow extraction) create bottlenecks for real-time, safety-critical applications on mobile devices. Here, we show how depthwise separable 3D convolutions, combined with an early fusion of spatial and temporal information, can achieve a balance between high prediction accuracy and real-time inference requirements. In particular, increased accuracy is achieved when assessment requires motion information, for example, when sunglasses conceal the eyes. Further, a custom TensorFlow-based smartphone application shows the true impact of various approaches on inference times and demonstrates the effectiveness of real-time monitoring based on out-of-sample data to alert a drowsy driver. Our model is pre-trained on ImageNet and Kinetics and fine-tuned on a publicly available Driver Drowsiness Detection dataset. Fine-tuning on large naturalistic driving datasets could further improve accuracy to obtain robust in-vehicle performance. Overall, our research is a step towards practical deep learning applications, potentially preventing micro-sleeps and reducing road trauma.

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