Team:BIT/Hardware
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Abstract
Our hardware is an open source hardware solution for pre-cancer detection in non-laboratory environments, such as community hospitals. Engineering bacteria’s cultivation and detection were integrated in one machine, low cost and convenient operation make it has good potential for promotion, which solves the problem that current market lack of such kind of detecting instrument. The process of cultivation and detection and data analysis are all automatically, and the results will present to the user by sending to the user mobile phone through the Bluetooth or showing on the screen, so that users can easily access test results, which is highly readable. This device is developed based on Raspberry Pi3 open-source platform, which offers high scalability, and most of components are mainstream and easy purchasing on market. All the source code and design drawings are available for developers in our wiki. Besides, our hardware also can be widely used in parts verification and biosensor detection systems.
Background
Pre-disease detection based on our engineering bacteria, targeted at community hospitals and home, has its advantage. But detecting disease in biological methods requires some special instrument. For detecting fluorescent proteins, prevailing way is using ELISA instrument or similar device to measure the fluorescence intensity of the samples, then calculate fluorescent protein concentration. In addition to ELISA instrument, there are some there device like Quantitative Real-time PCR or immunofluorescence detector can also able to detect fluorescent protein. But all the instrument above are too expensive for large-area promotion, and are not specially design for disease detection, which limits the use of these instruments.
Design
Our device is designed for cultivating and detecting simples on standard 96-well plate. For offering the appropriate environment of bacterial growth, a constant temperature environment must be established inside our device. Meanwhile, in order to provide an accurately quantitative detection of disease markers, cultivation time must be control. Both the temperature and cultivation time can be set by users through the interface of our hardware. We also provide default setting for specific engineering bacteria so users can start the process conveniently. The setting information will be sent to Arduino, the lower microcontroller, for temperature control. In temperature control part, we use DS18B20 as temperature sensor and heating coil as heat source, together they constitute a closed loop multi-point temperature measurement and control system. Besides, fan and air circulation path also integrated inside our instrument, ensuring temperature consistency. In order to guarantee temperature stability and heating efficiency, we use mainstream PID temperature control algorithm for temperature control. after temperature reach its target, Raspberry Pi will start to calculate the incubation time in background. Once reach the pre-set time, our device will move on to detection part.
The detection part consists of GFP brightness detection part and sliding table. CMOS camera are used as brightness detection and blue laser LED as excitation light source. Mobile platform, driven by stepper motors, can move the multi-well plate to achieve point-by-point detection.
The CMOS camera is fixed in the upper center of the instrument, above the multi-well plate. A light filter of 525nm is placed in fort of camera, filters out the excitation light. Camera captures the image and sends it to Raspberry Pi, which performs the image processing. And finally the fluorescence intensity will be calculated based on picture brightness and exposure time.
In order to meet the requirements of power consumption and excitation efficiency, we choose a blue laser LED with a wavelength of 475 nm as the excitation light source. The LED are fixed beside the camera, with an adjustable bracket for correction, making sure it is facing the target simple. Excitation light source is control by Raspberry Pi through relay switch, only light up when capturing images for saving energy.
Mobile platform consists of two sliding tables, providing degree of freedom in both XY directions. Sliding table is composed of stepper motor and screw and rail. Motor move the platform by rotating screw, provide with positioning accuracy of 0.1 mm. the control of sliding table is implemented by Arduino, which is always waiting Raspberry Pi sending the number of the sample. Once it gets the number, it will drive the mobile platform to the right position for camera detection.
After our instrument finish detection part, preliminary data will be showed at the 5-inch touch panel. User can control the instrument or access information of progress through the screen. Besides, users can use mobile app to download the detection results and read the detailed report on it.
Result
Discussion
Similar to last year, we provide a low cost and non-laboratory hardware solution for pre-disease detection. Different from last year’s project, we have integrated cultivation and detection functions on one instrument, and make it work automatically. As for detection sensor, we used CMOS camera with better linearity instead of the old monolithic photodiode, providing higher detection accuracy. And we used sliding table to build a point-by-point detection platform.
After our market research, we found that there are fewer instruments available on the market to meet the needs of biological disease detection, and most of these instruments have large volume and expensive price, which will impede large-area promotion. In contrast, our device are less costly and easier to use, more suitable for promotion.
For other use such as in parts verification or other biosensor detection systems, our hardware also provides a convenient solution. User can change detecting fluorescence wavelength by change the light filter and the excitation light source. And the cultivation time and temperature settable too.
Lab Note
Contact Us
Beijing Institute of Technology,
No. 5 South Zhong Guan Cun Street,
Haidian Beijing 100081, P. R. China
Twitter : @igem_BIT
Sina Weibo : @igem_BIT
Website : http://www.bit.edu.cn
