Liquid biopsies use blood or urine as test samples, which are able to be continuously collected in a non-invasive manner. The analysis of cancer-related biomarkers such as circulating tumor cells (CTCs), circulating tumor DNA (ctDNA), microRNA, and exosomes provides important information in early cancer diagnosis, tumor metastasis detection, and postoperative recurrence monitoring assist with clinical diagnosis. However, low concentrations of some tumor markers, such as CTCs, ctDNA, and microRNA, in the blood limit its applications in clinical detection and analysis. Nanomaterials based on graphene oxide have good physicochemical properties and are now widely used in biomedical detection technologies. These materials have properties including good hydrophilicity, mechanical flexibility, electrical conductivity, biocompatibility, and optical performance. Moreover, utilizing graphene oxide as a biosensor interface has effectively improved the sensitivity and specificity of biosensors for cancer detection. In this review, we discuss various cancer detection technologies regarding graphene oxide and discuss the prospects and challenges of this technology.

Cancer has been one of the leading causes of death globally, with metastases and recurrences contributing to this result. The detection of circulating tumor cells (CTCs), which have been implicated as a major population of cells that is responsible for seeding and migration of tumor sites, could contribute to early detection of metastasis and recurrences, consequently increasing the chances of cure. This review article focuses on the current progress in microfluidics technology in CTCs diagnostics, extending to the use of nanomaterials and surface modification techniques for diagnostic applications, with an emphasis on the importance of integrating microchannels, nanomaterials, and surface modification techniques in the isolating and detecting of CTCs.

Organ-on-a-chip (OOC) devices mimic human organs, which can be used for many different applications, including drug development, environmental toxicology, disease models, and physiological assessment. Image data acquisition and analysis from these chips are crucial for advancing research in the field. In this study, we propose a label-free morphology imaging platform compatible with the small airway-on-a-chip system. By integrating deep learning and image recognition techniques, we aim to analyze the differentiability of human small airway epithelial cells (HSAECs). Utilizing cell imaging on day 3 of culture, our approach accurately predicts the differentiability of HSAECs after 4 weeks of incubation. This breakthrough significantly enhances the efficiency and stability of establishing small airway-on-a-chip models. To further enhance our analysis capabilities, we have developed a customized MATLAB program capable of automatically processing ciliated cell beating images and calculating the beating frequency. This program enables continuous monitoring of ciliary beating activity. Additionally, we have introduced an automated fluorescent particle tracking system to evaluate the integrity of mucociliary clearance and validate the accuracy of our deep learning predictions. The integration of deep learning, label-free imaging, and advanced image analysis techniques represents a significant advancement in the fields of drug testing and physiological assessment. This innovative approach offers unprecedented insights into the functioning of the small airway epithelium, empowering researchers with a powerful tool to study respiratory physiology and develop targeted interventions.