A Highly Versatile Microscope Imaging Technology Platform for the Multiplex Real-Time Detection of Biomolecules and Autoimmune Antibodies

2012 ◽  
pp. 35-74 ◽  
Author(s):  
Stefan Rödiger ◽  
Peter Schierack ◽  
Alexander Böhm ◽  
Jörg Nitschke ◽  
Ingo Berger ◽  
...  
Keyword(s):  
Real Time ◽  
Imaging Technology ◽  
Technology Platform ◽  
Autoimmune Antibodies ◽  
Microscope Imaging ◽  
Real Time Detection ◽  
Detection Of Biomolecules

scholarly journals An all-solid-state heterojunction oxide transistor for the rapid detection of biomolecules and SARS-CoV-2 spike S1 protein

2021 ◽  
Author(s):  
Yen-Hung Lin ◽  
Yang Han ◽  
Abhinav Sharma ◽  
Wejdan S. AlGhamdi ◽  
Chien-Hao Liu ◽  
...  
Keyword(s):  
Solid State ◽  
Real Time ◽  
High Sensitivity ◽  
Cost Effective ◽  
High Electron ◽  
Real Time Detection ◽  
Bioanalytical Applications ◽  
Detection Of Biomolecules ◽  
Dimensional Electron Gas ◽  
Sensitivity Specificity

AbstractSolid-state transistor sensors that can detect biomolecules in real time are highly attractive for emerging bioanalytical applications. However, combining cost-effective manufacturing with high sensitivity, specificity and fast sensing response, remains challenging. Here we develop low-temperature solution-processed In2O3/ZnO heterojunction transistors featuring a geometrically engineered tri-channel architecture for rapid real-time detection of different biomolecules. The sensor combines a high electron mobility channel, attributed to the quasi-two-dimensional electron gas (q2DEG) at the buried In2O3/ZnO heterointerface, in close proximity to a sensing surface featuring tethered analyte receptors. The unusual tri-channel design enables strong coupling between the buried q2DEG and the minute electronic perturbations occurring during receptor-analyte interactions allowing for robust, real-time detection of biomolecules down to attomolar (aM) concentrations. By functionalizing the tri-channel surface with SARS-CoV-2 (Severe Acute Respiratory Syndrome Coronavirus 2) antibody receptors, we demonstrate real-time detection of the SARS-CoV-2 spike S1 protein down to attomolar concentrations in under two minutes.

Real-Time Detection of Drowsiness Related Lane Departures Using Steering Wheel Angle

2012 ◽  
Author(s):  
Anthony D. McDonald ◽  
Chris Schwarz ◽  
John D. Lee ◽  
Timothy L. Brown
Keyword(s):  
Real Time ◽  
Steering Wheel ◽  
Real Time Detection

Real-time Detection of Aortic Valve in Echocardiography using Convolutional Neural Networks

2020 ◽  
Vol 16 (5) ◽  
pp. 584-591 ◽  
Author(s):  
Muhammad Hanif Ahmad Nizar ◽  
Chow Khuen Chan ◽  
Azira Khalil ◽  
Ahmad Khairuddin Mohamed Yusof ◽  
Khin Wee Lai
Keyword(s):  
Neural Network ◽  
Neural Networks ◽  
Heart Disease ◽  
Aortic Valve ◽  
Real Time ◽  
Convolutional Neural Networks ◽  
Valvular Heart Disease ◽  
Detection System ◽  
Processing Unit ◽  
Real Time Detection

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.

Sign in / Sign up

Export Citation Format

Share Document