ObjectivesTreating olfactory dysfunction is a challenge for physicians. One of the therapeutic options could be transplantation of stem cells. In this study, neural stem cells were transplanted into anosmic mice.MethodsNeural stem cells were generated from the olfactory bulb of green fluorescent protein (GFP)-transgenic C57BL6 mice. Anosmia were induced by injection of intraperitoneal 3-methylindole. The neural stem cells were transplanted transnasally on the next day. The olfactory function was evaluated by a food-finding test once a week. The olfactory neuroepithelium was harvested for histologic examination and protein analysis at 4 weeks.ResultsTwenty-five percent (6/24) of the control mice that were not transplanted with neural stem cells survived at 4 weeks while 67% (8/12) of the transplanted mice survived (P=0.029). The food finding test showed that the transplanted mice resumed finding food at 3 weeks while the control mice resumed finding food at 4 weeks. GFP-positive cells were observed in the olfactory neuroepithelium of the transplanted mice. Western blotting revealed that the olfactory marker protein expression was significantly lower in the control mice than that in the transplanted mice.ConclusionThis study demonstrated that improvement of mouse survival was achieved and recovery of olfactory function was promoted by transnasal transplantation of neural stem cells in the anosmic mouse model. These results indicate that stem cells might be one of the future modalities for treating olfactory impairment.
We consider a buyer’s decision problem of sustainable supplier selection and order allocation (SSS & OA) among multiple heterogeneous suppliers who sell multiple types of items. The buyer periodically orders items from chosen suppliers to refill inventory to preset levels. Each supplier is differentiated from others by the types of items supplied, selling price, and order-related costs, such as transportation cost. Each supplier also has a preset requirement for minimum order quantity or minimum purchase amount. In the beginning of each period, the buyer constructs an SSS & OA plan considering various information from both parties. The buyer’s planning problem is formulated as a mathematical model, and an efficient algorithm to solve larger instances of the problem is developed. The algorithm is designed to take advantage of the branch-and-bound method, and the special structure of the model. We perform computer experiments to test the accuracy of the proposed algorithm. The test result confirmed that the algorithm can find a near-optimal solution with only 0.82 percent deviation on average. We also observed that the use of the algorithm can increase solvable problem size by about 2.4 times.
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