Early screening and diagnosis of major diseases, such as cancer, cardiovascular diseases, and infections, are of paramount importance for ensuring human health. However, the current detection methods commonly used, such as ELISA and ECLIA, are not easily adaptable for flexible use outside laboratory settings. This limitation is particularly evident in community and home-based healthcare environments. Recognizing the potential of mobile phones in instant testing scenarios, we believe they can serve as a key solution for real-time detection ^[1]. Therefore, we have developed a hardware device called "LightCapter" with the aim of providing a simpler detection method to bridge this existing gap in the field. The device is designed to be user-friendly, requiring no special training to operate. It utilizes smartphones to capture photos, and an accompanying app to read out the values. Our goal is for LightCapter to become a simple and efficient testing equipment that can be widely utilized.

LightCapter is an affordable, customizable, and user-friendly device designed for fluorescence detection. It serves as a valuable tool for detecting biomarkers in blood samples.

In our wetlab, we have developed a biomarker detection sensor based on protein cages, each containing 60 sites. To facilitate detection, we introduced a high concentration of eGFP fluorescent reporting molecules, which effectively illuminate the protein cages. By introducing antibody molecules to the protein cages, biomarkers are captured through specific recognition, enabling efficient biomarker detection. To capture the fluorescence signals, we created LightCapter. This device incorporates an excitation light source that stimulates the green fluorescent protein (eGFP) assembled on the protein cages, resulting in fluorescence emission and signal amplification. The fluorescence signals can be captured using a mobile phone camera and analyzed with a dedicated program, providing preliminary results. As a result, LightCapter not only offers a convenient and straightforward method for detecting disease-associated biomarkers through fluorescence chromatography but also enables regular health monitoring, thereby providing valuable insights for treatment and prevention efforts.

To ensure the portability and user-friendliness of the biosensors, we have designed a straightforward optical structure that minimizes user intervention. In well-lit environments, we utilize LEDs with specific wavelengths for illumination, providing the necessary level of brightness and fluorescence excitation ^[2]. In order to stimulate the fluorescence emission of eGFP, we have incorporated a specialized excitation light source. Since eGFP responds to an excitation wavelength of approximately 488 nm, we have integrated a 488 nm excitation light (laser diode module from the reputable manufacturer gxl) into the device for fluorescence excitation. To address the issue of excessive background blue light resulting from the higher power of the excitation light, we have implemented a filter (CONTAX D25/M25mm 30nmOD525nm) within the device to mitigate this effect. This addition significantly improves the overall performance of the device.

Fig. 2. Light path.

We emit blue excitation light around 488nm using a laser. At this point, Mi3 and eGFP are exposed to the light. The green light emitted by eGFP passes through a narrow bandpass filter and is received by the phone's camera. At the same time, most of the excitation blue light is blocked. This allows the camera to capture a fluorescent image.

Immunoassay paper strip detection, recognized as a highly efficient method for real-time Point-of-Care Testing (POCT) in diagnostics, is well-established. Our strategy for enhancement involves utilizing a combination of protein cages and fluorescent proteins to replace colloidal gold, with the goal of signal amplification and quantification. In practice, due to time constraints, we've established a model detection system using glass slides to assess the detection capabilities of LightCapter.

The hardware design was created using FreeCAD, enabling its production through a 3D printer after several rounds of design and refinement. By arranging the modules below, you can gain a comprehensive understanding of the overall structure, which encompasses the smartphone camera placement area, 488 nm excitation light, 525 nm optical filter, excitation light adapter power cord, and smartphone support area. Once the printing and assembly process is complete, these components seamlessly come together to form a compact and portable device with approximate dimensions of 100mm x 160mm x 150mm.

Usage Guide:

• Left click + drag: Rotate model
• Mouse wheel: Zoom in/out
• Right click + drag: Pan model

We assessed the sensitivity of LightCapter by selecting different concentrations of eGFP as the source of our detection signal. Initially, samples were loaded into the detection chamber, and upon excitation with 488nm light, photos were taken using a smartphone's camera. We then used ImageJ to analyze the captured images, deriving grayscale values as indicators of eGFP detection signal intensity. A graph was plotted with eGFP concentration on the x-axis and grayscale values on the y-axis, showing a positive correlation between eGFP concentration and signal intensity.

Fig. 10. Relationship diagram about grayscale value and concentration of eGFP

Based on the detection sensitivity of eGFP (approximately 10^-9 10^-5 mol/L) and the optimal molecular assembly ratio obtained from wet experiments, under conditions of eGFP to SA at 5:1, the concentration of Mi3-eGFP-SA binding antibodies may reach a level of 10^-9 mol/L. Preliminary experimental results suggest that this method can be used for clinical pathogen detection. However, from the graph (figure 10), it can be observed that the linear fit between eGFP concentration and signal intensity is not ideal. We suspect this discrepancy is related to the excessive strength of the 488nm excitation light used. During image capture, in extensive areas, due to light saturation, stronger light signals could not be recognized by ImageJ, resulting in some degree of distortion in the analysis results. We plan to make two improvements: 1. Adding an optical lens in front of the blue laser to disperse light, increase the illuminated area, and reduce exposure intensity; 2. Adjusting the filtering process by using narrower bandwidth filters or stacking multiple filters to reduce the blue background, making analysis easier. In the future, we also hope to design user-friendly smartphone software for direct analysis of captured fluorescent images and output of conclusions.

LightCapter is a user-friendly, affordable and home-use fluorescence detection device. We hope that LightCapter can become a simple and easy-to-use alternative to measure the progression of cardiovascular disease and prevent sudden heart failure, filling the gap between home and heart health. Blank areas for early detection and monitoring of vascular diseases. To demonstrate how our engineered system works, we have created a video explaining how LightCapter works and its operational process.

1. Wang, B., Li, Y., Zhou, M. et al. Smartphone-based platforms implementing microfluidic detection with image-based artificial intelligence. Nat Commun 14, 1341 (2023). https://doi.org/10.1038/s41467-023-36017-x
2. Smartphone-Based Droplet Digital LAMP Device with Rapid Nucleic Acid Isolation for Highly Sensitive Point-of-Care Detection;Fei Hu, Juan Li, Zengming Zhang, Ming Li, Shuhao Zhao, Zhipeng Li, and Niancai Peng;Analytical Chemistry 2020 92 (2), 2258-2265;DOI: 10.1021/acs.analchem.9b04967