A new molecular imprinting-based mass-sensitive sensor for real-time detection of 17β-estradiol from aqueous solution

2012 ◽  
Vol 32 (4) ◽  
pp. 1164-1169 ◽  
Author(s):  
Erdoğan Özgür ◽  
Erkut Yılmaz ◽  
Gülsu Şener ◽  
Lokman Uzun ◽  
Rıdvan Say ◽  
...  
The Analyst ◽  
2016 ◽  
Vol 141 (14) ◽  
pp. 4424-4431 ◽  
Author(s):  
Yingjie Yu ◽  
Qi Zhang ◽  
Jonathan Buscaglia ◽  
Chung-Chueh Chang ◽  
Ying Liu ◽  
...  

In this study, a real time potentiometric biosensor based on the 3D surface molecular imprinting was developed for CEA detection.


1993 ◽  
Vol 277 (1) ◽  
pp. 25-30
Author(s):  
Akyhiro Iwata ◽  
Chiyoe Yamanaka ◽  
Nobuaki Nakashima ◽  
Yasukazu Izawa

Micromachines ◽  
2020 ◽  
Vol 11 (1) ◽  
pp. 59 ◽  
Author(s):  
Chang Li ◽  
Bingbing Wang ◽  
Hao Wan ◽  
Rongxiang He ◽  
Qi Li ◽  
...  

This paper presents a total phosphorus online real-time monitoring system integrated with on-chip digestion based on the merits of optofluidic technology. The integrated optofluidic device contains a hollow optical fiber employed for pretreatment and digestion of phosphorus solution samples, a polydimethylsiloxane (PDMS)-based micromixer with convergent–divergent walls designed to enable sufficient mixing and chromogenic reaction, and a couple of optical fiber collimators attached with a Z-shaped flow cell for optical detection. Details of system design and fabrication are introduced in this paper. In the experiment, on-chip digestion of four typical phosphates in aqueous solution including organophosphorus and inorganic phosphorus is investigated under different reaction conditions, such as digestion temperature, concentration of oxidant and pH value, and the optimal reaction parameters are explored under different conditions. Meanwhile, we demonstrate the online real-time monitoring function of the optofluidic device, and the digestion mechanisms of four different phosphates are analyzed and discussed. Compared with the national standard method, we find that the measurement accuracy and sensitivity are acceptable when the concentration of total phosphorus is between 0.005–0.9 mg/L (by weight of P) in aqueous solution, which covers the range defined in the national standard. The traditional digestion time of several hours is greatly reduced to less than 10 s, and the content of total phosphorus can be obtained in a few minutes. The integrated optofluidic device can significantly shorten the test time and reduce the sample amount, and also provides a versatile platform for the real-time detection and analysis of many biochemical samples.


2012 ◽  
Author(s):  
Anthony D. McDonald ◽  
Chris Schwarz ◽  
John D. Lee ◽  
Timothy L. Brown

Author(s):  
Muhammad Hanif Ahmad Nizar ◽  
Chow Khuen Chan ◽  
Azira Khalil ◽  
Ahmad Khairuddin Mohamed Yusof ◽  
Khin Wee Lai

Background: Valvular heart disease is a serious disease leading to mortality and increasing medical care cost. The aortic valve is the most common valve affected by this disease. Doctors rely on echocardiogram for diagnosing and evaluating valvular heart disease. However, the images from echocardiogram are poor in comparison to Computerized Tomography and Magnetic Resonance Imaging scan. This study proposes the development of Convolutional Neural Networks (CNN) that can function optimally during a live echocardiographic examination for detection of the aortic valve. An automated detection system in an echocardiogram will improve the accuracy of medical diagnosis and can provide further medical analysis from the resulting detection. Methods: Two detection architectures, Single Shot Multibox Detector (SSD) and Faster Regional based Convolutional Neural Network (R-CNN) with various feature extractors were trained on echocardiography images from 33 patients. Thereafter, the models were tested on 10 echocardiography videos. Results: Faster R-CNN Inception v2 had shown the highest accuracy (98.6%) followed closely by SSD Mobilenet v2. In terms of speed, SSD Mobilenet v2 resulted in a loss of 46.81% in framesper- second (fps) during real-time detection but managed to perform better than the other neural network models. Additionally, SSD Mobilenet v2 used the least amount of Graphic Processing Unit (GPU) but the Central Processing Unit (CPU) usage was relatively similar throughout all models. Conclusion: Our findings provide a foundation for implementing a convolutional detection system to echocardiography for medical purposes.


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