Endometriosis occurs when endometrial tissue grows outside the uterus, affecting millions of women worldwide. Despite extensive research, its cellular mechanisms remain unclear, complicating both diagnosis and treatment. This study presents the development and validation of a Raman tweezers platform that combines optical trapping and Raman spectroscopy for label-free biochemical profiling of single endometriosis-derived VK2/E6E7 epithelial cells. To assess discriminatory capacity, VK2/E6E7 spectra were compared against the epithelial cancer cell line A549. The Raman tweezers system was calibrated with polystyrene beads, and spectral data were preprocessed using the self-supervised deep learning model (RSPSSL) to ensure reproducible single-cell measurements. The Raman spectrum of VK2/E6E7 cells displays characteristic peaks corresponding to lipids, collagen (418, 606, 1312, and 1447 cm-1), proteins (538, 938, 998, 1258, and 1447 cm-1), and nucleic acids (737, 1093, 1187, and 1258 cm-1). Random Forest and XGBoost for classifying VK2/E6E7 and A549 cells achieved over 80% accuracy without signs of overfitting. SHAP (SHapley Additive exPlanations) analysis highlighted lower lipid, amino acid, and amide III signals alongside higher saccharide signals as key drivers of cell differentiation. This is the first study to apply Raman tweezers for single-cell analysis of endometriosis cells, integrating deep-learning preprocessing and explainable machine learning. It offers a promising approach for probing endometriosis pathophysiology and supporting a less invasive diagnostic strategy.
Rapid developments in defense-related science and technology have made the identification of chemical, biological, radiological, and nuclear (CBRN) warfare agents increasingly critical. CBRN warfare agents pose significant threats in both military and terrorist contexts. CBRN warfare agents have been used historically and continue to pose a potential threat due to their possible use by both state and non-state actors. Effective identification of warfare agents is necessary for both CBRN management and rapid on-site diagnosis and identification of these threats. Therefore, the highly sensitive and rapid detection of chemical and biological warfare agents is essential for the strategic management of risks to human health, the environment, and public safety, as well as for effective CBRN incident management. In order to develop biosensor and detector systems, as well as for incident management, the categories of these agents or their effects on human health must be fundamentally known. General analytical methods, biodetector systems and biosensors are widely used in the identification of CBRN warfare agents. This review critically examines current biosensing strategies and highlights their applicability in real-time CBRN defense scenarios.
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