Phenylboronic acid-diol crosslinked 6-<i>O</i>-vinylazeloyl-d-galactose nanocarriers for insulin delivery

A new block polymer named poly 3-acrylamidophenylboronic acid-b-6-O–vinylazeloyl-d-galactose (p(AAPBA-b-OVZG)) was prepared using 3-acrylamidophenylboronic acid (AAPBA) and 6-O-vinylazeloyl-D-galactose (OVZG) via a two-step procedure involving S-1-dodecyl-S-(α', α'-dimethyl-α″-acetic acid) trithiocarbonate (DDATC) as chain transfer agent, 2,2-azobisisobutyronitrile (AIBN) as initiator and dimethyl formamide (DMF) as solvent. The structures of the polymer were examined by Fourier transform infrared spectroscopy (FT-IR) and 1H NMR and the thermal stability was determined by thermal gravimetric analysis (TG/DTG). Transmission electron microscopy (TEM) and dynamic light scattering (DLS) were utilized to evaluate the morphology and properties of the p(AAPBA-b-OVZG) nanoparticles. The cell toxicity, animal toxicity and therapeutic efficacy were also investigated. The results indicate the p(AAPBA-b-OVZG) was successfully synthesized and had excellent thermal stability. Moreover, the p(AAPBA-b-OVZG) nanoparticles were submicron in size and glucose-sensitive in phosphate-buffered saline (PBS). In addition, insulin as a model drug had a high encapsulation efficiency and loading capacity and the release of insulin was increased at higher glucose levels. Furthermore, the nanoparticles showed a low-toxicity in cell and animal studies and they were effective at decreasing blood glucose levels of mice over 96 h. These p(AAPBA-b-OVZG) nanoparticles show promise for applications in diabetes treatment using insulin or other hypoglycemic proteins

Promoting cold-start items in recommender systems

As one of major challenges, cold-start problem plagues nearly all recommender systems. In particular, new items will be overlooked, impeding the development of new products online. Given limited resources, how to utilize the knowledge of recommender systems and design efficient marketing strategy for new items is extremely important. In this paper, we convert this ticklish issue into a clear mathematical problem based on a bipartite network representation. Under the most widely used algorithm in real e-commerce recommender systems, so-called the item-based collaborative filtering, we show that to simply push new items to active users is not a good strategy. To our surprise, experiments on real recommender systems indicate that to connect new items with some less active users will statistically yield better performance, namely these new items will have more chance to appear in other users' recommendation lists. Further analysis suggests that the disassortative nature of recommender systems contributes to such observation. In a word, getting in-depth understanding on recommender systems could pave the way for the owners to popularize their cold-start products with low costs.Comment: 6 pages, 6 figure