著者

秋下 雅弘 江頭 正人 小川 純人 大田 秀隆 岡部 哲郎 喩 静 柴崎 孝二 孫 輔卿

出版者

東京大学

雑誌

基盤研究(B)

巻号頁・発行日

2012-04-01

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本研究では性ホルモン様作用を有する漢方薬の成分を用いて血管、神経、乳癌、前立腺癌の細胞に対する作用を検討し、臓器別作用を網羅的に解析・分類した。具体的に、乳癌細胞ではエストロゲンと類似した細胞増殖能をもつ生薬成分ともたない成分で分類できた。前立腺癌細胞ではテストステロンと類似した細胞増殖能をもつ成分はなかった。血管ではすべての生薬成分が平滑筋細胞の石灰化を性ホルモンと同様に抑制する効果があった。神経細胞ではginsenoside Rb1がアポトーシスによる細胞傷害を保護する作用があった。このような成果からホルモン補充療法の代替薬として新規薬剤の開発へつながることが期待できる。

著者

今枝 秀二郞 孫 輔卿 内山 瑛美子 田中 友規 スタッヴォラヴット アンヤポーン 角川 由香 馬場 絢子 田中 敏明 飯島 勝矢 大月 敏雄

出版者

日本建築学会

雑誌

日本建築学会計画系論文集 (ISSN:13404210)

巻号頁・発行日

vol.85, no.773, pp.1387-1395, 2020 (Released:2020-07-30)

参考文献数

12

被引用文献数

1 2

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[Introduction] Falls and femoral fractures are one of the most serious problems for an elderly daily life, these causes the possibility to become bedridden or forced to move to an elderly facility from their home. However, ways of falling and continuing to dwell in own houses by changing the architectural environment for the elderly people were unknown. The whole study revealed the measures of fall prevention by architectural ways at home and the purpose of this part was to clarify the architectural factors which related to falls and femoral fractures in their houses from the viewpoint of fall prevention. [Methods] This study had two steps. First, interview in hospital was conducted when elderly patients went into the University of Tokyo Hospital after they experienced falls and femoral fractures. In this interviews, basic information of patients and situation of falls were collected also by using clinical information. Second, tracking investigation by home-visit interview or interview in hospital was conducted after they went back home and it included measurement of fall places. [Results] The average age of 43 patients was 80.9 (SD 8.3) years old, the number of female was 34 (the average age was 80.6, SD 7.8) and that of male was 9 (the average age was 81.8, SD 10.4). First interviews showed that falls which caused femoral fracture happened all over places but the number of falls at home was biggest, 17 cases in 43 cases. In the houses, the number of falls at bedroom was 6 cases, at the corridor was 4 cases and at the living room was 3 cases. All 6 falls at the night time occurred going to or going back from toilet at home. In six types of falls, the number of falling by internal forces was biggest and next was falling by external forces. Fall cases at home had four types of falls. By analysis of each fall case in the house, architectural factors which caused falls and the effective architectural measures against falls were revealed. In addition, falls at home related to toilet had high risk for falls in spite of fall types and these results indicated that it was important to consider the routes and behaviors when falls happened. The home-visit interview revealed that these routes and behaviors related to housing plan such as the locations of bed and types or directions of doors. The actual routes at falls were showed on housing plane figure, how people rotated in the architectural spaces before they fell was revealed. From these second investigation, the ways of renovation which will prevents next fall at home was clarified. [Conclusion] This research showed the ways of falls which caused femoral fractures for the elderly in their houses and the possibility for the ways of architectural fall preventions by multidisciplinary specialists including architecture, medicine, nursing and physical therapy. In the next step, how people renovated their houses after they went back home in long-term care insurance system and who were involved with these renovation will be researched.

著者

内山 瑛美子 高野 渉 中村 仁彦 今枝 秀二郞 孫 輔卿 松原 全宏 飯島 勝矢

出版者

一般社団法人 日本ロボット学会

雑誌

日本ロボット学会誌 (ISSN:02891824)

巻号頁・発行日

vol.39, no.2, pp.189-192, 2021 (Released:2021-03-24)

参考文献数

5

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Interview survey is one of the options for investigations with light loads on participants to study how people fall compared with measurements by many sensors. In this paper, we aimed at predicting fall patterns from interview text data. We use k-means clustering method to confirm the validity of the labels attached to the interview data, and also confirmed the validity of the summaries of the interview data by interviewer researchers by focusing on the co-occurrence word analysis. After confirming the validity of the labels and summaries, we construct a naive Bayes model classifiers to classify the fall patterns. The average classification rate was 61.1% for 3 types of falls - falls by an unexpected external force, by losing balance or supports, and by other reasons.