Team:Lambert GA/Software

FLUOROCENTS APP

Background

The FluoroCents application is used as part of our FluoroCents measurement system. To read about this measurement system for background into this app, click here.

In our wet lab process, the level of fluorescence in a liquid culture is correlated with the amount of C. elegans genetic material that is found within, as the toehold switch unbinds only in the presence of a target strand from C. elegans. While visual detection of fluorescence may alert health care workers to the possibility of helminthiasis diagnosis, we set out to create a more practical manner in which we could not only detect, but quantify fluorescence. Keeping in mind the standards of frugality, accessibility, and portability, we introduce FluoroCents: an ultra-low cost, 3-D printed fluorometer that can be used with ease in the field by healthcare workers. FluoroCents is the most portable fluorometer in the world - at just 7 grams, Fluorocents will provide a rapid and accessible platform for in-field detection. The physical portion of FluoroCents is a 3-D printed light excitation and filtration mechanism. But in order for the FluoroCents hardware device to function, it needs an Android phone with the FluoroCents app installed to function.

Overview

At its base, FluoroCents provides a simple method for detecting the presence of fluorescence in a liquid culture. However, with the revolutionary FluoroCents application, our fluorometer can go beyond this simple test and provide analytics on the level of fluorescence in a sample and use this data to extrapolate information about the relative level of helminth DNA in a sample.

The FluoroCents app makes collecting fluorescence data easy.

Ambient Light Sensor

The ambient light sensor is a photodetector found at the top of most smartphones. The sensor is intended to sense ambient light near the surface of the phone and dim the brightness of the phone accordingly. The ambient light sensor picks up electromagnetic energy in a limited range of wavelengths of visible light, but includes the wavelengths emitted by GFP (Pereira & Hosker, 2019). We will manipulate the sensor and phone device to collect data about luminescence emitted by fluorescent samples, giving us tangible information about the total fluorescence found in a sample. Furthermore, the prevalence of sensors within smartphones allow for an accessible mechanism for this data collection.

The ambient light sensor location on most smartphones is located on the top.


Process

The FluoroCents Android app takes repeated measurements of the sample's lux value by capturing the reading from the ambient light sensor as changes in the lux value occur. FluoroCents was developed in Java using the Android Studio IDE. The app outputs the mean lux value and variance of the lux onto the user interface over a 30-second interval. The number of lux measurements taken during the interval is also displayed according to user preference to run trials in a scientific setting. To ensure in-field functionality, the mobile app comes with a saving mechanism that stores the results of fluorescence tests in the cloud. By utilizing Amazon Web Service's DynamoDB NoSQL database infrastructure, many data points can be recorded in one test including the following:


  • Latitude
  • Longitude
  • Timestamp
  • Disease Status
  • Name of User
  • Nearest Body of Water
  • Source of Sample
  • Additional Notes

The latitude and longitude coordinates for each test can then be retrieved from the DynamoDB database and displayed within the FluoroCents Android app using Google Maps API. Each location marker for its respective test is labelled with characteristics as inputted by the user. These features make up the helminth diagnosis mapping tool (HDMT). This tool is built into the testing app and accessible on mobile phones across the globe. Healthcare workers can easily access data where helminth infections are prevalent and which areas are in immediate need of aid. On a broader scale, this enables healthcare workers to allocate resources and plan distribution pipelines in a way that is efficient and resourceful. Housing all parts of the diagnosis workflow, including the measurement of fluorescence, storage of data, and map access all in one app serves as a highly useful capability in the field.

The FluoroCents app helps map locations of helminthiasis incidences.

By measuring the lux value using a phone’s ambient light sensor and our FluoroCents app, we can generate more complex information regarding the detection of a helminth organism.

Verification of FluoroCents with a Plate Reader

As part of our testing, we wanted to verify that FluoroCents was a legitimate device to measure fluorescence by comparing it with an industrial plate reader. We wanted to graphically compare the lux measurements taken with FluoroCents to the arbitrary values from a plate reader for several two fold dilutions of the iGEM fluorescein standard.

This graph was made using GraphPad Prism. The graph shows the measurement of Fluoroscein dilutions by both a commercial plate reader and our very own FluoroCents device. This graph has a log2 x-axis.


With the graph of the data we collected, it was clear that there was a real correlation between lux from FluoroCents, seen on the right y-axis, and the raw fluorescence data from a plate reader, seen on the left column. This correlation to an industrial-grade instrument means that FluoroCents does have a valid claim as a fluorescence tool, giving the FluoroCents application legitimacy in the FluoroCents system.

Click here to access the FluoroCents GitHub.

ZIN-Q

Overview

Zin-Q is a smartphone app available on the Android platform that can be used to quantify the presence of zinc in blood serum. The app uses a colorimetric-based test by comparing an experimental sample’s RGB value to the RGB values of known zinc concentrations.

Zin-Q accepts an input of an image of the paper template, available as a supplement, with 6 tubes on it: 5 corresponding with the known zinc concentration controls and 1 corresponding with the experimental serum sample with an unknown zinc concentration. The user first clicks on the serum concentration of 2 and hough circle detection through OpenCV, an image processing library, is run on the image. This process involves converting the input image into a grayscale image and applying a Gaussian blur to reduce noise in the image. A minimum distance of 100 pixels is set between detected circles and the minimum and maximum radius are set to 10 pixels and 200 pixels, respectively. There are two primary parameters that are used to change the thresholds of circle detection: a gradient value for edge detection and an accumulator value for the Hough Circle Detection algorithm. Zin-Q iterates through values for each parameter and incrementing by 4 each time until a combination of the two parameters results in a single circle being detected. Once a pellet circle has been detected, an area of 200 pixels by 200 pixels around the touch of the initial image is sent to the next pellet selection screen, where the patient can click inside the pellet. The average RGB value is determined across a 10 pixel by 10 pixel region to capture a concentrated area just inside the pellet. This average RGB calculation is repeated for all 6 tubes in the sample with the patient repeating the same series of clicks for each pellet. Three linear models are fit with the range being the log base 10 of the serum concentration and the domain being the respective Red, Green, and Blue values. The experimental samples are plotted on the line of best fit to obtain a respective concentration of zinc in the experimental sample.

The Zin-Q application.

Because the overall image of all 6 tubes is taken under the same lighting conditions and the experimental sample is reliant on a linear model fit on the controls, Zin-Q accounts for variations in lighting conditions from make and model of smartphone as well as environmental conditions.

Process

A healthcare worker downloads Zin-Q onto their smartphone. The worker aligns the top of each serum sample tube with the black line corresponding with the zinc concentration in each tube. By using the smartphone’s native camera app, the worker captures a picture of the template with the two horizontal sides aligned with the top and bottom of the camera frame’s borders. Zin-Q can then be opened and the image taken with the phone’s native camera can be selected from the phone’s camera roll or gallery. The worker then chooses the colored pellet of the corresponding serum sample when prompted for each zinc concentration. By using circle detection to determine a valid area for a pellet, the user is then able to be notified of the presence of a pellet and prompted to select inside of the pellet. After the first selection of each pellet, the app provides a second screen for a zoomed selection of the center of the pellet. The app captures the average RGB value of a 10 pixel by 10 pixel area around the patient’s selection inside of the pellet. This process is repeated for each of the 5 known serum samples with known zinc concentrations as well as for the 1 zinc test. The final zinc concentration of the experimental serum sample is calculated and displayed by fitting a linear model correlating the zinc concentration of each serum sample to each of the average Red, Green, and Blue values of each of the 5 known controls.

REFERENCES

[1] Astiazarán-García, H., Iñigo-Figueroa, G., Quihui-Cota, L., & Anduro-Corona, I. (2015, June 3). Crosstalk between Zinc Status and Giardia Infection: A New Approach. Retrieved from https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4488794/

[2] Scott, E., M., Koski, & G., K. (2000, May 1). Zinc Deficiency Impairs Immune Responses against Parasitic Nematode Infections at Intestinal and Systemic Sites. Retrieved from https://academic.oup.com/jn/article/130/5/1412S/4686397