Nanotechnology has been widely applied in tumor imaging, diagnostic and therapy. Beside the prefabricated nanomaterials, constructing nanostructures in living cells through self-assemble provides an alternative strategy to treat cancer. In vivo self-assembly renders the conversion of compatible small molecules into assembled nanostructures with toxicity, and is expected to outperform the prefabricated nanotechnologies as the small molecules diffuse faster than their assembly form. Attributed to the specific tumor environment such as low pH, high ROS, high enzyme expression and so on, in vivo self-assembly could differentiate cancer cells from normal ones with high selectivity. The in vivo self-assembly based caner therapy has made considerable progress in the last decade with confirmed advantages such as high capacity, minimal drug resistance, high accumulation, enhanced retention and so on. This review summarized the in vivo self-assembly of nanostructures induced by the stimuli like pH, ROS, enzyme, metal ion, localized concentration, biominerization and their utilization in cancer therapy.
The development of biosensing platforms has been impressively accelerated by advancements in liquid crystal (LC) technology. High response rate, easy operation, and good stability of the LC droplet-based biosensors are all benefits of the long-range order of LC molecules. Bioprobes emerged when LC droplets were combined with biotechnology, and these bioprobes are used extensively for disease diagnosis, food safety, and environmental monitoring. The LC droplet biosensors have high sensitivity and excellent selectivity, making them an attractive tool for the label-free, economical, and real-time detection of different targets. Portable devices work well as the accessory kits for LC droplet-based biosensors to make them easier to use by anyone for on-site monitoring of targets. Herein, we offer a review of the latest developments in the design of LC droplet-based biosensors for qualitative target monitoring and quantitative target analysis.
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