This repository contains the code and resources for the paper "Ultrasensitive and Long-lasting Luminescence Cascade Sensor for Point of Care Viral Pathogen Detection".
Bioluminescence holds notable promise as a modality in diagnostics due to its high signal-to-noise ratio and absence of incident radiation. However, challenges arise from rapid signal decay and reduced enzyme activity when linked to targeting molecules, limiting its reliability in point-of-care diagnostic applications. Here, we introduce LUCAS, an enzyme cascade system capable of detecting analytes with ultrahigh sensitivity and prolonged bioluminescence. By employing an enzyme that retains its activity when conjugated to an antibody, our assay achieves more than a 500-fold increase in bioluminescence signal and maintains an 8-fold improvement in signal persistence compared to conventional bioluminescence assays. Implemented on the fully automated LUCAS, our system facilitates rapid (< 23 min) sample-to-answer analysis of viruses without an external power supply. Its accuracy surpasses 94% in the qualitative classification of 177 viral-infected patient samples and 50 viral-spiked serum samples, various pathogens including the respiratory virus SARS-CoV-2, and blood-borne pathogens such as HIV, HBV, and HCV as clinical models. The decentralized, rapid, sensitive, specific, and cost-effective nature of LUCAS positions it as a viable diagnostic tool for low-resource environments.
- Python: Version 3.7 or higher
- Libraries:
opencv-python
numpy
scipy
matplotlib
pandas
flask
serial
- Windows: Tested on Windows 10
- Linux: Tested on Ubuntu 20.04
- macOS: Tested on macOS Big Sur
- Standard Desktop or Laptop: No special hardware requirements
- Optional: Raspberry Pi 4 for portable operation
- Optional: CMOS sensor for bioluminescence detection
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Clone the Repository:
git clone https://github.com/shafieelab/BioluminescenceAssay.git cd BioluminescenceAssay
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Set Up Python Environment:
- Create and activate a virtual environment (optional but recommended):
python3 -m venv env source env/bin/activate # On Windows use `env\Scripts\activate`
- Create and activate a virtual environment (optional but recommended):
-
Install Dependencies:
pip install -r requirements.txt
- Approximately 5-10 minutes on a standard desktop computer.
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Navigate to the PythonScript Directory:
cd PythonScript
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Run the Demo Script:
python IntensityCalcCode.py
Algorithm BrightnessDetection
Input: img
Output: yellow_brightness
// Step 1: Define Region of Interest
top ← 250
right ← 1150
height ← 900
width ← 1600
img_roi ← crop_image(img, top, right, height, width)
// Step 2: Convert to HSV
hsv_img ← convert_to_hsv(img_roi)
// Step 3: Define Yellow Boundaries
lower_bound ← [20, 50, 50]
upper_bound ← [50, 255, 255]
// Step 4: Create Mask for Yellow
mask ← in_range(hsv_img, lower_bound, upper_bound)
// Step 5: Remove Noise from Mask
kernel ← create_kernel(7, 7)
mask ← morphological_close(mask, kernel)
mask ← morphological_open(mask, kernel)
// Step 6: Segment Yellow Regions
segmented_img ← bitwise_and(hsv_img, hsv_img, mask)
// Step 7: Calculate Brightness
yellow_brightness ← sum(segmented_img[:, :, 2])
return yellow_brightness
End Algorithm
- The demo script will process the sample data and display bioluminescence intensity values.
- Approximately 2-3 seconds on a standard desktop computer for ~20 images. Runtime varies with number of images.