Clinically, cancer drug resistance to chemotherapy, targeted therapy or immunotherapy remains the main impediment towards curative cancer therapy, which leads directly to treatment failure along with extended hospital stays, increased medical costs and high mortality. Therefore, increasing attention has been paid to nanotechnology-based delivery systems for overcoming drug resistance in cancer. In this respect, novel tumor-targeting nanomedicines offer fairly effective therapeutic strategies for surmounting the various limitations of chemotherapy, targeted therapy and immunotherapy, enabling more precise cancer treatment, more convenient monitoring of treatment agents, as well as surmounting cancer drug resistance, including multidrug resistance (MDR). Nanotechnology-based delivery systems, including liposomes, polymer micelles, nanoparticles (NPs), and DNA nanostructures, enable a large number of properly designed therapeutic nanomedicines. In this paper, we review the different mechanisms of cancer drug resistance to chemotherapy, targeted therapy and immunotherapy, and discuss the latest developments in nanomedicines for overcoming cancer drug resistance.

Clustering uncertain graphs based on the probabilistic graph model has sparked extensive research and widely varying applications. Existing structural clustering methods rely heavily on the computation of pairwise reliable structural similarity between vertices, which has proven to be extremely costly, especially in large uncertain graphs. In this paper, we develop a new, decomposition-based method, ProbSCAN, for efficient reliable structural similarity computation with theoretically improved complexity. We further design a cost-effective index structure UCNO-Index, and a series of powerful pruning strategies to expedite reliable structural similarity computation in uncertain graphs. Experimental studies on eight real-world uncertain graphs demonstrate the effectiveness of our proposed solutions, which achieves orders of magnitude improvement of clustering efficiency, compared with the state-of-the-art structural clustering methods in large uncertain graphs.