Relationships between physicians and pharmaceutical companies have received considerable attention regarding the professionalism of health care professionals in recent years. Pharmacists may also have a conflict of interest if they are provided some benefits from pharmaceutical companies. We conducted a survey of pharmacists in hospitals, clinics, and pharmacies in mid 2011. The response rate was 58.8％（235/400）. Most pharmacists（94％） reported some type of relationship with pharmaceutical companies, which included receiving ballpoint pens and notebooks（88.5％）, or receiving food in a meeting（56.6％）. Managerial level pharmacists received more “textbooks and guidelines”, “gifts” than other pharmacists. The results of this survey indicate that relationships between pharmacists and companies is common. The formulation of guidelines and more responsible behaviors are needed.

The aim of this study was to investigate the end of life care provided by long-term care facilities and nursing homes. A cross-sectional survey of all long-term care facilities and nursing homes in Kobe was performed in July 2013. Questionnaires were sent to 350 facilities with an 89.7％ response rate. In total, 121（39％）of respondents stated that end of life care was available, and 151（48％）responded that facilities were willing to provide end of life care for terminally ill residents. One hundred fifty-two（48％）and 183（58％）of respondents answered that facilities were available for managing residents with percutaneous endoscopic gastrostomy, and transfusion, respectively. Seventy-two（23％）of respondents reported that facilities for managing the use of narcotics were available. In conclusion, a minority of long-term care facilities and nursing homes ware available for providing end of life care for residents, though approximately 50％ were willing to do so.

Recent studies have developed simple techniques for monitoring and assessing sleep. However, several issues remain to be solved for example high-cost sensor and algorithm as a home-use device. In this study, we aimed to develop an inexpensive and simple sleep monitoring system using a camera and video processing. Polysomnography (PSG) recordings were performed in six subjects for four consecutive nights. Subjects' body movements were simultaneously recorded by the web camera. Body movement was extracted by video processing from the video data and fi ve parameters were calculated for machine learning. Four sleep stages (WAKE, LIGHT, DEEP and REM) were estimated by applying these fi ve parameters to a support vector machine. The overall estimation accuracy was 70.3 ± 11.3% with the highest accuracy for DEEP (82.8 ± 4.7%) and the lowest for LIGHT (53.0 ± 4.0%) compared with correct sleep stages manually scored on PSG data by a sleep technician. Estimation accuracy for REM sleep was 68.0 ± 6.8%. The kappa was 0.19 ± 0.04 for all subjects. The present non-contact sleep monitoring system showed suffi cient accuracy in sleep stage estimation with REM sleep detection being accomplished. Low-cost computing power of this system can be advantageous for mobile application and modularization into home-device.
Machine Learning ; Methods ; Mobile Applications ; Polysomnography ; Sleep Stages ; Sleep, REM ; Support Vector Machine