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The development of noninvasive methods for bladder cancer identification remains a critical clinical need. Recent studies have shown that atomic force microscopy (AFM), combined with pattern recognition machine learning, can detect bladder cancer by analyzing cells extracted from urine. However, these promising findings were limited by a relatively small patient cohort, resulting in modest statistical significance. In this study, we corroborated the AFM technique’s capability to identify bladder cancer cells with high accuracy using a controlled model system of genetically purified human bladder epithelial cell lines, comparing cancerous cells with nonmalignant controls. By processing AFM adhesion maps through machine learning algorithms, following previously established methods, we achieved an area under the ROC curve (AUC) of 0.97, with 91% accuracy in cancer cell identification. Furthermore, we enhanced cancer detection by incorporating multiple imaging channels recorded with AFM operating in Ringing mode, achieving an AUC of 0.99 and 93% accuracy. These results demonstrated strong statistical significance (p < 0.0001) in this well-defined model system. While this controlled study does not capture the biological variation present in clinical settings, it provides independent support for AFM-based detection methods and establishes a rigorous technical foundation for further clinical development of AFM imaging-based methods for bladder cancer detection. 

The mesoporous nature of silica nanoparticles provides a novel platform for the development of ultrabright fluorescent particles, which have organic molecular fluorescent dyes physically encapsulated inside the silica pores. The close proximity of the dye molecules, which is possible without fluorescence quenching, gives an advantage of building sensors using FRET coupling between the encapsulated dye molecules. Here we present the use of this approach to demonstrate the assembly of ultrabright fluorescent ratiometric sensors capable of simultaneous acidity (pH) and temperature measurements. FRET pairs of the temperature-responsive, pH-sensitive and reference dyes are physically encapsulated inside the silica matrix of ~50 nm particles. We demonstrate that the particles can be used to measure both the temperature in the biologically relevant range (20 to 50 °C) and pH within 4 to 7 range with the error (mean absolute deviation) of 0.54 °C and 0.09, respectively. Stability of the sensor is demonstrated. The sensitivity of the sensor ranges within 0.2–3% °C−1 for the measurements of temperature and 2–6% pH−1 for acidity.