Deep tumor-penetrating nano-delivery strategy to improve diagnosis and therapy in patient-derived xenograft(PDX)oral cancer model and patient tissue

Nanoprodrugs that are directly assembled by prodrugs attract considerable attention with high anticancer potentials.However,their stability and efficiency of tumor-targeted delivery remain a major challenge in practical biomedical applications.Here,we report a new deep tumor-penetrating nano-delivery strategy to achieve enhanced anti-cancer performance by systematic optimization of a porphyrin-doxorubicin-based nanoprodrug using various PEGylations/crosslinks and co-administration of targeting peptide iRGD.Polyethylene glycols(PEGs)with different molecular weights and grafts are employed to crosslink the nanoprodrug and optimize size,charge,tumor accumulation and penetration,and anti-cancer efficiency,etc.The tumor penetration was validated in syngeneic oral cancer mouse models,patient-derived xenograft(PDX)models,and oral cancer tissue from patients.The optimized nanoprodrug co-administrated with iRGD remarkably enhances the accumulation and penetration both in tumor vascular and PDX tumor tissue.It is effective and safe to improve in vivo therapeutic efficacy via the passive tumor targeting dependent and independent mode.Our tumor-penetrating nano-delivery strategy is promising to strengthen the nanoprodrugs in clinical implementation.

Citywide package deliveries via crowdshipping:minimizing the efforts from crowdsourcers

Most current crowdsourced logistics aim to minimize systems cost and maximize delivery capacity,but the efforts of crowdsourcers such as drivers are almost ignored.In the delivery process,drivers usually need to take long-distance detours in hitchhiking rides based package deliveries.In this paper,we propose an approach that integrates offline trajectory data mining and online route-and-schedule optimization in the hitchhiking ride scenario to find optimal delivery routes for packages and drivers.Specifically,we propose a two-phase framework for the delivery route planning and scheduling.In the first phase,the historical trajectory data are mined offline to build the package transport network.In the second phase,we model the delivery route planning and package-taxi matching as an integer linear programming problem and solve it with the Gurobi optimizer.After that,taxis are scheduled to deliver packages with optimal delivery paths via a newly designed scheduling strategy.We evaluate our approach with the realworld datasets;the results show that our proposed approach can complete citywide package deliveries with a high success rate and low extra efforts of taxi drivers.