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8. Short- versus long-segment posterior spinal fusion with vertebroplasty for osteoporotic vertebral collapse with neurological impairment in thoracolumbar spine: a multicenter study

Author

Ishikawa, Yuya, Watanabe, Kei, Katsumi, Keiichi, Ohashi, Masayuki, Shibuya, Yohei, Izumi, Tomohiro, Hirano, Toru, Endo, Naoto, Kaito, Takashi, Yamashita, Tomoya, Fujiwara, Hiroyasu, Nagamoto, Yukitaka, Matsuoka, Yuji, Suzuki, Hidekazu, Nishimura, Hirosuke, Terai, Hidetomi, Tamai, Koji, Tagami, Atsushi, Yamada, Shuta, Adachi, Shinji, Yoshii, Toshitaka, Ushio, Shuta, Harimaya, Katsumi, Kawaguchi, Kenichi, Yokoyama, Nobuhiko, Oishi, Hidekazu, Doi, Toshiro, Kimura, Atsushi, Inoue, Hirokazu, Inoue, Gen, Miyagi, Masayuki, Saito, Wataru, Nakano, Atsushi, Sakai, Daisuke, Nukaga, Tadashi, Ikegami, Shota, Shimizu, Masayuki, Futatsugi, Toshimasa, Ohtori, Seiji, Furuya, Takeo, Orita, Sumihisa, Imagama, Shiro, Ando, Kei, Kobayashi, Kazuyoshi, Kiyasu, Katsuhito, Murakami, Hideki, Yoshioka, Katsuhito, Seki, Shoji, Hongo, Michio, Kakutani, Kenichiro, Yurube, Takashi, Aoki, Yasuchika, Oshima, Masashi, Takahata, Masahiko, Iwata, Akira, Endo, Hirooki, Abe, Tetsuya, Tsukanishi, Toshinori, Nakanishi, Kazuyoshi, Watanabe, Kota, Hikata, Tomohiro, Suzuki, Satoshi, Isogai, Norihiro, Okada, Eijiro, Funao, Haruki, Ueda, Seiji, Shiono, Yuta, Nojiri, Kenya, Hosogane, Naobumi, and Ishii, Ken

Published
2020
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10. Surgical outcomes of spinal fusion for osteoporotic thoracolumbar vertebral fractures in patients with Parkinson’s disease: what is the impact of Parkinson’s disease on surgical outcome?

Author

Watanabe, Kei, Katsumi, Keiichi, Ohashi, Masayuki, Shibuya, Yohei, Izumi, Tomohiro, Hirano, Toru, Endo, Naoto, Kaito, Takashi, Yamashita, Tomoya, Fujiwara, Hiroyasu, Nagamoto, Yukitaka, Matsuoka, Yuji, Suzuki, Hidekazu, Nishimura, Hirosuke, Terai, Hidetomi, Tamai, Koji, Tagami, Atsushi, Yamada, Syuta, Adachi, Shinji, Yoshii, Toshitaka, Ushio, Shuta, Harimaya, Katsumi, Kawaguchi, Kenichi, Yokoyama, Nobuhiko, Oishi, Hidekazu, Doi, Toshiro, Kimura, Atsushi, Inoue, Hirokazu, Inoue, Gen, Miyagi, Masayuki, Saito, Wataru, Nakano, Atsushi, Sakai, Daisuke, Nukaga, Tadashi, Ikegami, Shota, Shimizu, Masayuki, Futatsugi, Toshimasa, Ohtori, Seiji, Furuya, Takeo, Orita, Sumihisa, Imagama, Shiro, Ando, Kei, Kobayashi, Kazuyoshi, Kiyasu, Katsuhito, Murakami, Hideki, Yoshioka, Katsuhito, Seki, Shoji, Hongo, Michio, Kakutani, Kenichiro, Yurube, Takashi, Aoki, Yasuchika, Oshima, Masashi, Takahata, Masahiko, Iwata, Akira, Endo, Hirooki, Abe, Tetsuya, Tsukanishi, Toshinori, Nakanishi, Kazuyoshi, Watanabe, Kota, Hikata, Tomohiro, Suzuki, Satoshi, Isogai, Norihiro, Okada, Eijiro, Funao, Haruki, Ueda, Seiji, Shiono, Yuta, Nojiri, Kenya, Hosogane, Naobumi, and Ishii, Ken

Published
2019
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18. Additional file 1: of Surgical outcomes of spinal fusion for osteoporotic thoracolumbar vertebral fractures in patients with Parkinsonâ s disease: what is the impact of Parkinsonâ s disease on surgical outcome?

Author

Watanabe, Kei, Katsumi, Keiichi, Ohashi, Masayuki, Shibuya, Yohei, Izumi, Tomohiro, Hirano, Toru, Endo, Naoto, Kaito, Takashi, Yamashita, Tomoya, Fujiwara, Hiroyasu, Yukitaka Nagamoto, Matsuoka, Yuji, Suzuki, Hidekazu, Hirosuke Nishimura, Hidetomi Terai, Tamai, Koji, Tagami, Atsushi, Syuta Yamada, Adachi, Shinji, Toshitaka Yoshii, Shuta Ushio, Harimaya, Katsumi, Kawaguchi, Kenichi, Yokoyama, Nobuhiko, Oishi, Hidekazu, Doi, Toshiro, Kimura, Atsushi, Inoue, Hirokazu, Inoue, Miyagi, Masayuki, Saito, Wataru, Nakano, Atsushi, Sakai, Daisuke, Nukaga, Tadashi, Ikegami, Shota, Shimizu, Masayuki, Futatsugi, Toshimasa, Ohtori, Seiji, Furuya, Takeo, Sumihisa Orita, Imagama, Shiro, Ando, Kei, Kobayashi, Kazuyoshi, Kiyasu, Katsuhito, Murakami, Hideki, Yoshioka, Katsuhito, Seki, Shoji, Hongo, Michio, Kakutani, Kenichiro, Yurube, Takashi, Yasuchika Aoki, Oshima, Masashi, Takahata, Masahiko, Iwata, Akira, Hirooki Endo, Abe, Tetsuya, Tsukanishi, Toshinori, Nakanishi, Kazuyoshi, Watanabe, Kota, Hikata, Tomohiro, Suzuki, Satoshi, Isogai, Norihiro, Eijiro Okada, Funao, Haruki, Ueda, Seiji, Shiono, Yuta, Nojiri, Kenya, Naobumi Hosogane, and Ishii, Ken

Abstract

The assessment Scale Proposed by the Japanese Orthopaedic Association. The Japanese Orthopaedic Association Scoring system (JOA score) consists of 2 categories (subjective and objective symptoms), ranging from 0 (worst condition) to 15 (best condition). (DOCX 22 kb)

Published
2019
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19. Risk Factors for Proximal Junctional Fracture Following Fusion Surgery for Osteoporotic Vertebral Collapse with Delayed Neurological Deficits: A Retrospective Cohort Study of 403 Patients

Author

Tamai, Koji, primary, Terai, Hidetomi, additional, Suzuki, Akinobu, additional, Nakamura, Hiroaki, additional, Watanabe, Kei, additional, Katsumi, Keiichi, additional, Ohashi, Masayuki, additional, Shibuya, Yohei, additional, Izumi, Tomohiro, additional, Hirano, Toru, additional, Kaito, Takashi, additional, Yamashita, Tomoya, additional, Fujiwara, Hiroyasu, additional, Nagamoto, Yukitaka, additional, Matsuoka, Yuji, additional, Suzuki, Hidekazu, additional, Nishimura, Hirosuke, additional, Tagami, Atsushi, additional, Yamada, Syuta, additional, Adachi, Shinji, additional, Yoshii, Toshitaka, additional, Ushio, Shuta, additional, Harimaya, Katsumi, additional, Kawaguchi, Kenichi, additional, Yokoyama, Nobuhiko, additional, Oishi, Hidekazu, additional, Doi, Toshiro, additional, Kimura, Atsushi, additional, Inoue, Hirokazu, additional, Inoue, Gen, additional, Miyagi, Masayuki, additional, Saito, Wataru, additional, Nakano, Atsushi, additional, Sakai, Daisuke, additional, Nukaga, Tadashi, additional, Ikegami, Shota, additional, Shimizu, Masayuki, additional, Futatsugi, Toshimasa, additional, Ohtori, Seiji, additional, Furuya, Takeo, additional, Orita, Sumihisa, additional, Imagama, Shiro, additional, Ando, Kei, additional, Kobayashi, Kazuyoshi, additional, Kiyasu, Katsuhito, additional, Murakami, Hideki, additional, Yoshioka, Katsuhito, additional, Seki, Shoji, additional, Hongo, Michio, additional, Kakutani, Kenichiro, additional, Yurube, Takashi, additional, Aoki, Yasuchika, additional, Oshima, Masashi, additional, Takahata, Masahiko, additional, Iwata, Akira, additional, Endo, Hirooki, additional, Abe, Tetsuya, additional, Tsukanishi, Toshinori, additional, Nakanishi, Kazuyoshi, additional, Watanabe, Kota, additional, Hikata, Tomohiro, additional, Suzuki, Satoshi, additional, Isogai, Norihiro, additional, Okada, Eijiro, additional, Funao, Haruki, additional, Ueda, Seiji, additional, Shiono, Yuta, additional, Nojiri, Kenya, additional, Hosogane, Naobumi, additional, and Ishii, Ken, additional

Published
2019
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20. Pressure Estimation using PIV Measurement at Wing Wakes in Transonic Flows

Author

Matsushima, Kisa, Izumi, Tomohiro, and Kato, Hiroyuki

Abstract

第45回流体力学講演会/航空宇宙数値シミュレーション技術シンポジウム2013 (2013年7月4日-5日. タワーホール船堀), 東京, 45th Fluid Dynamics Conference / Aerospace Numerical Simulation Symposium 2013 (July 4-5, 2013. Tower Hall Funabori), Tokyo, Japan, A pressure estimation system using velocity data measured by PIV experiment has been developed and validated. The pressure estimation particularly aims to predict pressure distribution on a wake plane behind a wing. The system uses numerical computations of fluid dynamic equations called a 2.5-Dimensional model. The model has been implemented to recover three dimensional flow patterns from limited data on two-dimensional stereo-PIV measurements. For pressure estimation with the 2.5D model, three PIV measurement planes are used. In this article, the 2.5D model estimation is applied to wing wake of compressible flows. Instead of PIV experiment, three-dimensional CFD simulation is conducted about a rectangular wing. It provides velocity data on several wake planes. In addition to it, it gives pressure values which should be standard to evaluate the accuracy of pressures estimation. The free stream speed of the flow is transonic and shock wavesare generated on a wing surface. The estimation results by 2.5D model has been evaluated by being compared with accurate pressure distributions as well as the estimation by a commonly used existing model. It has been found that 2.5D one works well for a plane normal to the free stream direction in wing wake flows. The accuracy of its estimation is less than 0.5%. In addition, through this research, an effective way to impose boundary conditions for pressure estimation has been found., 形態: カラー図版あり, Physical characteristics: Original contains color illustrations, 資料番号: AA0062268014, レポート番号: JAXA-SP-13-011

Published
2014

23. 遷音速流における翼後流PIV 計測による圧力推定

Author

松島, 紀佐, 泉, 知宏, 加藤, 裕之, Matsushima, Kisa, Izumi, Tomohiro, Kato, Hiroyuki, 松島, 紀佐, 泉, 知宏, 加藤, 裕之, Matsushima, Kisa, Izumi, Tomohiro, and Kato, Hiroyuki

Abstract

A pressure estimation system using velocity data measured by PIV experiment has been developed and validated. The pressure estimation particularly aims to predict pressure distribution on a wake plane behind a wing. The system uses numerical computations of fluid dynamic equations called a 2.5-Dimensional model. The model has been implemented to recover three dimensional flow patterns from limited data on two-dimensional stereo-PIV measurements. For pressure estimation with the 2.5D model, three PIV measurement planes are used. In this article, the 2.5D model estimation is applied to wing wake of compressible flows. Instead of PIV experiment, three-dimensional CFD simulation is conducted about a rectangular wing. It provides velocity data on several wake planes. In addition to it, it gives pressure values which should be standard to evaluate the accuracy of pressures estimation. The free stream speed of the flow is transonic and shock wavesare generated on a wing surface. The estimation results by 2.5D model has been evaluated by being compared with accurate pressure distributions as well as the estimation by a commonly used existing model. It has been found that 2.5D one works well for a plane normal to the free stream direction in wing wake flows. The accuracy of its estimation is less than 0.5%. In addition, through this research, an effective way to impose boundary conditions for pressure estimation has been found., 会議情報: 第45回流体力学講演会/航空宇宙数値シミュレーション技術シンポジウム2013 (2013年7月4日-5日. タワーホール船堀), 東京, 形態: カラー図版あり, Meeting Information: 45th Fluid Dynamics Conference / Aerospace Numerical Simulation Symposium 2013 (July 4-5, 2013. Tower Hall Funabori), Tokyo, Japan, Physical characteristics: Original contains color illustrations

Published
2015

30. A Study of the Effects of Iprif lavone Administration on Hemodialysis Patients with Renal Osteodystrophy: Preliminary Report

Author

Hyodo, Toru, primary, Ono, Koji, additional, Koumi, Tsutomu, additional, Miyagawa, Ikuo, additional, Okano, Toru, additional, Kagawa, Takeshi, additional, Hagino, Hiroshi, additional, Kishimoto, Hideaki, additional, Oyama, Yukinori, additional, Inoue, Akemichi, additional, Doi, Soichiro, additional, Morita, Takatomo, additional, Izumi, Tomohiro, additional, and Tanaka, Noriaki, additional

Published
1991
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32. Method for rapid identification of oral streptococci by PCR using 16S-23S ribosomal RNA intergenic spacer gene

Author

Hoshino, Tomonori, Izumi, Tomohiro, Ooshima, Takashi, and Fujiwara, Taku

Abstract

Mutans group streptococci play an important role in the formation of dental caries, while mitis group streptococci are considered to be associated with subacute bacterial endocarditis. Both mitis and salivarius group streptococci, which are early colonizers in the human oral cavity, have an effect on the infection and colonization of mutans streptococci. In the present study, a rapid and exact PCR method to detect those oral streptococci was developed using the 16S-23S ribosomal RNA intergenic spacer gene, which is present between 16S rRNA and 23S rRNA within the RNA operon. The PCR analysis was able to correctly identify mutans and mitis group streptococcal species. Our results indicate that the combined PCR analysis method established in the present study is useful for detecting oral streptococcal species, as well as in clinical applications to predict and prevent dental caries and etiological studies of streptococcal infective endocarditis.

Published
2005
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33. Risk Factors for Proximal Junctional Fracture Following Fusion Surgery for Osteoporotic Vertebral Collapse with Delayed Neurological Deficits: A Retrospective Cohort Study of 403 Patients.

Author

Tamai K, Terai H, Suzuki A, Nakamura H, Watanabe K, Katsumi K, Ohashi M, Shibuya Y, Izumi T, Hirano T, Kaito T, Yamashita T, Fujiwara H, Nagamoto Y, Matsuoka Y, Suzuki H, Nishimura H, Tagami A, Yamada S, Adachi S, Yoshii T, Ushio S, Harimaya K, Kawaguchi K, Yokoyama N, Oishi H, Doi T, Kimura A, Inoue H, Inoue G, Miyagi M, Saito W, Nakano A, Sakai D, Nukaga T, Ikegami S, Shimizu M, Futatsugi T, Ohtori S, Furuya T, Orita S, Imagama S, Ando K, Kobayashi K, Kiyasu K, Murakami H, Yoshioka K, Seki S, Hongo M, Kakutani K, Yurube T, Aoki Y, Oshima M, Takahata M, Iwata A, Endo H, Abe T, Tsukanishi T, Nakanishi K, Watanabe K, Hikata T, Suzuki S, Isogai N, Okada E, Funao H, Ueda S, Shiono Y, Nojiri K, Hosogane N, and Ishii K

Abstract

Introduction: Approximately 3% of osteoporotic vertebral fractures develop osteoporotic vertebral collapse (OVC) with neurological deficits, and such patients are recommended to be treated surgically. However, a proximal junctional fracture (PJFr) following surgery for OVC can be a serious concern. Therefore, the aim of this study is to identify the incidence and risk factors of PJFr following fusion surgery for OVC., Methods: This study retrospectively analyzed registry data collected from facilities belonging to the Japan Association of Spine Surgeons with Ambition (JASA) in 2016. We retrospectively analyzed 403 patients who suffered neurological deficits due to OVC below T10 and underwent corrective surgery; only those followed up for　≥2 years were included. Potential risk factors related to the PJFr and their cut-off values were calculated using multivariate logistic regression analysis and receiver operating characteristic (ROC) analysis., Results: Sixty-three patients (15.6%) suffered PJFr during the follow-up (mean 45.7 months). In multivariate analysis, the grade of osteoporosis (grade 2, 3: adjusted odds ratio (aOR) 2.92; p=0.001) and lower instrumented vertebra (LIV) level (sacrum: aOR 6.75; p=0.003) were independent factors. ROC analysis demonstrated that lumbar bone mineral density (BMD) was a predictive factor (area under curve: 0.72, p=0.035) with optimal cut-off value of 0.61 g/cm 2 (sensitivity, 76.5%; specificity, 58.3%), but that of the hip was not (p=0.228)., Conclusions: PJFr was found in 16% cases within 4 years after surgery; independent risk factors were severe osteoporosis and extended fusion to the sacrum. The lumbar BMD with cut-off value 0.61 g/cm 2 may potentially predict PJFr. Our findings can help surgeons select perioperative adjuvant therapy, as well as a surgical strategy to prevent PJFr following surgery., Competing Interests: Conflicts of Interest: The authors declare that there are no relevant conflicts of interest.

Published
2018
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