PurposeBisphosphonates have been used to treat osteoporosis for more than ten years. However, complications associated with long-term administration of bisphosphonates, such as nonunion after pelvic insufficiency fracture or osteonecrosis of the jaw, have been recently reported in the literature. We investigated the relationships among the mechanical properties of the intact rat femur as well as healing fracture calluses and the intraosseous concentration of pamidronate (ICP), after long-term administration of pamidronate in a rat osteoporosis model.Materials and MethodsWe performed bilateral ovariectomy in 25 3-month-old female Sprague-Dawley rats. Beginning three months after ovariectomy, disodium pamidronate (0.5mg/kg) was injected every month. After the six-month administration period, the left femoral shaft was fractured using the closed fracture technique. Five weeks after fracture, 23 rats were euthanized and both femora were removed. We checked the mechanical properties of the intact (right) and fractured (left) femora using a three-point bending technique. Intraosseous concentration of pamidronate was checked by high-performance liquid chromatography.ResultsThe mean ICP was 61.8 ± 15.7ng/mg of bone. High ICP decreased the ultimate load to failure, stiffness, and ultimate stress of the intact femora (p = 0.015, 0.027, 0.039, respectively). There was a tendency to decrease the ultimate load to failure in the healing callus when the ICP increased (p = 0.183). High ICP decreased the bone mineral density of the femoral head (p = 0.005).ConclusionHigh concentrations of pamidronate in intact bone decreased the bone mineral density and weakened the mechanical strength of the rat femora. The mechanical strength of the early healing callus was not correlated with concentration of pamidronate in the bone.

Demand forecasting in the biomedical area is becoming more important because of radical changes in the macroeconomic environment and consumption trends. Moreover, the need for big data analysis on data from wireless sensor networks and social media is increasing because it shows not only the rapidly changing environmental data such as fine dust concentration but also the responses of potential customers that are expected to affect the demand for a medicine. Therefore, demand forecasting models based on data analysis in wireless sensor networks and topic modeling of buzzwords in blog documents were suggested in this study. First, we analyzed topics of documents from blogs that describe the symptoms of certain diseases related to selected medicines. Thereafter, we extracted topic trends for a selected period and constructed demand forecasting models that consist of topic trends, environmental data from wireless sensor networks, and time-series sales data. The experiment results show that topic trends about medicines significantly affect the performance of demand forecasting for these medicines.

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