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The increase in medical expenditure has been worsening and poses a serious social problem. Commonly, leftover drugs are retained by patients. We estimated the possible reduction in prescription rates by appropriately reusing leftover drugs, and investigated the medication efficacy classifications that render leftover drugs due to the medication non-adherence. A retrospective cross-sectional survey of prescription data was performed at community pharmacies engaged in the appropriate reuse of leftover drugs through the SETSUYAKU-BAG campaign. We evaluated the drug costs and number of drugs originally prescribed, the reduction in expenditure and numbers after the use of leftover drugs, and then calculated the prescription reduction ratio (PRR) based on the number of drugs. Factors contributing to non-adherence were analyzed by the PRR. After reviewing the prescription information of 1792 patients, the reduction rate in drug expenditure was found to be 20.1%. Purgatives, Chinese medicines, and agents for peptic ulcer had higher PRRs and belonged to the top ten medications according to the prescription efficacy classifications. Non-adherence associated with the medication efficacy classifications was assessed by analyzing 5466 formulations. Thirty percent of formulations were found to be non-adherent. According to the medication efficacy classifications, six medications including agents for hyperlipidemias, peptic ulcer, psychotropics agents, and others, were less adherent than antihypertensives. These results suggest that adjusting prescriptions by appropriately reusing leftover drugs in community pharmacies could reduce medical costs. Further considerations are necessary for improving medication adherence in Japan. Healthcare providers should monitor medication adherence more carefully, with the results identified in this study.

In this paper, we propose a novel Driver Monitoring System (DMS) that estimates whether drivers are able to control vehicles. Provided with an on-board depth sensor and computing platform, the system keeps recognizing the driving posture. The system is trained beforehand to learn normal driving postures so that it can detect driver's inability state by calculating the “distance” between the known normal postures and the current posture. By using our relative representation, the system extracts the essential information of driving postures from depth map images. To increase the accuracy, each relative relationship among joints is weighted depending on its significance. We implemented our DMS on a real vehicle for evaluations. 10 participants drove the vehicle on three test courses and we checked our DMS performance in real time. The evaluation results show that the average false detection rate decreases to 0.01 %, so we confirmed that this novel DMS has potential performance for practical use.

Stereoselective synthesis of the C'D'E'F' ring system of maitotoxin was achieved starting from the E' ring through successive formation of the D' and C' rings based on SmI2-mediated reductive cyclization. Construction of the F' ring was accomplished via Suzuki-Miyaura cross-coupling with a side chain fragment and Pd(II)-catalyzed cyclization of an allylic alcohol. The C'D'E'F' ring system inhibited maitotoxin-induced Ca(2+) influx in rat glioma C6 cells with an IC50 value of 59 μM.