Lung cancer is a malignant tumor with the highest morbidity and mortality rate. Early screening and treatment are crucial in effectively reducing lung cancer mortality. This study presents a novel label-free bioanalysis system integrating fiber optic sensing and two-dimensional (2D) scattering imaging technologies, offering real-time, high-efficiency detection capabilities. The cladding on the fiber surface, followed by immobilization of specific antibodies. Variations in the evanescent wave field at the fiber surface induce changes in the transmission intensity, enabling highly sensitive, realtime detection of target biomolecules. Simultaneously, the light beam transmitted through the fiber optic excites individual biological cells in the fluid chamber, while a micro-optical system captures their 2D light scattering patterns, enabling precise cell identification and classification. The integration of these two technologies allows the sensor system to perform visualized single-cell identification and classification, as well as efficient biomarker detection at the molecular level. Compared to traditional single-technology approaches, this innovative system offers significant advantages in sensitivity, specificity, and detection speed, opening new pathways for bioanalysis. It demonstrates broad application potential in areas such as label-free ion detection and cell classification, particularly in early tumor screening.
Lymphoma has become one of the most prevalent malignant tumors in China, with a significant rise in incidence among young people in recent years. Early diagnosis and treatment are therefore crucial for improving patient outcomes, including efficacy, survival, and quality of life. In this study, we developed a multimodal detection system that combines twodimensional (2D) light scattering and electrochemical techniques to differentiate between normal and tumor cells at the single-cell and molecular level. Using a laser microscopy detection system, we capture 2D light scattering images of individual cells, where the lymphocytes display distinctive patch-like patterns. The texture of these patterns is influenced by the internal cellular structures, and the differentiation of normal and tumor cells is achieved by extracting and analyzing the eigenvalues from the light scattering images. Additionally, electrochemical sensors detect hydrogen peroxide levels in the cellular solution by measuring changes in current, with tumor cells producing a greater current variation than normal cells. A support vector machine (SVM) algorithm was employed to distinguish between normal and tumor cells, achieving an accuracy of 88%. The results demonstrate that the multimodal detection system effectively differentiates normal and tumor cells from both physical and chemical perspectives, enhancing detection accuracy. This system offers a nondestructive, efficient, and cost-effective method for early cancer screening.
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