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2,059 results on '"Imaizumi, T"'

2. Toll-Like Receptor 3 as a Recurrence Risk Factor and a Potential Molecular Therapeutic Target in Colorectal Cancer

Author

Yoshida T, Miura T, Matsumiya T, Yoshida H, Morohashi H, Sakamoto Y, Kurose A, Imaizumi T, and Hakamada K

Subjects
colorectal cancer, toll-like receptor 3, recurrence, ccl2, ccl5, il-8, Diseases of the digestive system. Gastroenterology, RC799-869
Abstract

Tatsuya Yoshida,1 Takuya Miura,1 Tomoh Matsumiya,2 Hidemi Yoshida,2 Hajime Morohashi,1 Yoshiyuki Sakamoto,1 Akira Kurose,3 Tadaatsu Imaizumi,2 Kenichi Hakamada1 1Department of Gastroenterological Surgery, Hirosaki University Graduate School of Medicine, Hirosaki, Aomori 036-8562, Japan; 2Department of Vascular Biology, Hirosaki University Graduate School of Medicine, Hirosaki, Aomori 036-8562, Japan; 3Department of Anatomic Pathology, Hirosaki University Graduate School of Medicine, Hirosaki, Aomori 036-8562, JapanCorrespondence: Kenichi HakamadaDepartment of Gastroenterological Surgery, Hirosaki University Graduate School of Medicine, 5 Zaifu-cho, Hirosaki, Aomori 036-8562, JapanTel +81 172 39 5079Fax +81 172 39 5080Email hakamada@hirosaki-u.ac.jpPurpose: Colorectal cancer (CRC) often recurs after curative resection. Identification of major risk factors for CRC recurrence is important for effective prevention and treatment. In this study, we examined the potential relationship between CRC and TLR3 as this remains unclear.Patients and Methods: Correlations between TLR3 immunostaining and clinicopathological factors and prognosis were examined in 50 samples that were randomly extracted from 264 patients with CRC from January 2010 to December 2011. Chemokines induced by TLR3 agonist stimulation were also examined using TLR3-positive human CRC cell lines. Furthermore, the association between TLR3 and chemokine expression was assessed by analyzing the immunohistochemistry of surgical specimens.Results: Of the 50 patients, 14 (28%) were TLR3-negative. In the comparison of clinicopathological factors between the TLR3-negative and -positive groups, there were more lymph node metastasis-positive cases in the TLR3-negative group, and this difference was significant. Furthermore, there was no difference in overall survival rates between the two groups, but the 5-year recurrence-free survival (RFS) was significantly lower in the TLR3-negative group (46.2%) than in the TLR3-positive group (78.1%). Analysis of 5-year RFS using factors thought to be related to recurrence identified a high tumor budding and a TLR3-negative status as independent risk factors for recurrence. TLR3 activation of CRC cell lines induced expression of C-C motif chemokine ligand 2 (CCL2), C-C motif chemokine ligand 5 (CCL5), and interleukin-8. The expressions of CCL2, CCL5, and IL-8 were observed in the TLR3-positive tumor cells of surgical specimens.Conclusion: Non-expression of TLR3 in CRC cells was associated with lymph node metastasis and was an independent risk factor for recurrence. These results suggest that TLR3 may not only be used as a prognostic factor and a risk factor for recurrence, but further studies on the involvement of TLR3 with tumor growth may provide new therapeutic strategies.Keywords: colorectal cancer, toll-like receptor 3, recurrence, CCL2, CCL5, IL-8

Published
2020

5. Impact of Cerebral Microbleeds in Stroke Patients with Atrial Fibrillation.

Author

Soo, Y., Zietz, A., Yiu, B., Mok, V.C.T., Polymeris, A.A., Seiffge, D., Ambler, G., Wilson, D., Leung, T.W.H., Tsang, S.F., Chu, W., Abrigo, J., Cheng, C., Lee, K.J., Lim, J.S., Shiozawa, M., Koga, M., Chabriat, H., Hennerici, M., Wong, Y.K., Mak, H., Collet, R., Inamura, S., Yoshifuji, K., Arsava, E.M., Horstmann, S., Purrucker, J., Lam, B.Y.K., Wong, A., Kim, Y.D., Song, T.J., Lemmens, R., Eppinger, S., Gattringer, T., Uysal, E., Demirelli, D.S., Bornstein, N.M., Assayag, E.B., Hallevi, H., Molad, J., Nishihara, M., Tanaka, J., Coutts, S.B., Kappelle, L.J., Al-Shahi Salman, R., Jager, Rolf, Lip, G.Y.H., Goeldlin, M.B., Panos, L.D., Mas, J.L., Legrand, Laurence, Karayiannis, C., Phan, T., Bellut, M., Chappell, F., Makin, S., Hayden, D., Williams, D., Dam-Nolen, D.H.K. Van, Nederkoorn, P.J., Barbato, C., Browning, S., Wiegertjes, K., Tuladhar, A.M., Mendyk, A.M., Köhler, S., Oostenburgge, R. van, Zhou, Ying, Xu, Chao, Hilal, S., Gyanwali, B., Chen, C, Lou, M., Staals, J., Bordet, R., Kandiah, N., Leeuw, F.E. de, Simister, R., Hendrikse, Jeroen, Wardlaw, J., Kelly, P., Fluri, F., Srikanth, V., Calvet, D., Jung, S., Kwa, V.I.H., Smith, E.E., Hara, H., Yakushiji, Y., Orken, D.N., Fazekas, F., Thijs, V., Heo, J.H., Veltkamp, R., Ay, H., Imaizumi, T., Lau, K.K., Jouvent, E., Toyoda, K., Yoshimura, S., Bae, H.J., Martí-Fàbregas, J., Prats-Sánchez, L., Lyrer, P., Best, J. de, Werring, D., Engelter, S.T., Peters, Nils, Soo, Y., Zietz, A., Yiu, B., Mok, V.C.T., Polymeris, A.A., Seiffge, D., Ambler, G., Wilson, D., Leung, T.W.H., Tsang, S.F., Chu, W., Abrigo, J., Cheng, C., Lee, K.J., Lim, J.S., Shiozawa, M., Koga, M., Chabriat, H., Hennerici, M., Wong, Y.K., Mak, H., Collet, R., Inamura, S., Yoshifuji, K., Arsava, E.M., Horstmann, S., Purrucker, J., Lam, B.Y.K., Wong, A., Kim, Y.D., Song, T.J., Lemmens, R., Eppinger, S., Gattringer, T., Uysal, E., Demirelli, D.S., Bornstein, N.M., Assayag, E.B., Hallevi, H., Molad, J., Nishihara, M., Tanaka, J., Coutts, S.B., Kappelle, L.J., Al-Shahi Salman, R., Jager, Rolf, Lip, G.Y.H., Goeldlin, M.B., Panos, L.D., Mas, J.L., Legrand, Laurence, Karayiannis, C., Phan, T., Bellut, M., Chappell, F., Makin, S., Hayden, D., Williams, D., Dam-Nolen, D.H.K. Van, Nederkoorn, P.J., Barbato, C., Browning, S., Wiegertjes, K., Tuladhar, A.M., Mendyk, A.M., Köhler, S., Oostenburgge, R. van, Zhou, Ying, Xu, Chao, Hilal, S., Gyanwali, B., Chen, C, Lou, M., Staals, J., Bordet, R., Kandiah, N., Leeuw, F.E. de, Simister, R., Hendrikse, Jeroen, Wardlaw, J., Kelly, P., Fluri, F., Srikanth, V., Calvet, D., Jung, S., Kwa, V.I.H., Smith, E.E., Hara, H., Yakushiji, Y., Orken, D.N., Fazekas, F., Thijs, V., Heo, J.H., Veltkamp, R., Ay, H., Imaizumi, T., Lau, K.K., Jouvent, E., Toyoda, K., Yoshimura, S., Bae, H.J., Martí-Fàbregas, J., Prats-Sánchez, L., Lyrer, P., Best, J. de, Werring, D., Engelter, S.T., and Peters, Nils

Abstract

01 juli 2023, Contains fulltext : 294348.pdf (Publisher’s version ) (Open Access), OBJECTIVES: Cerebral microbleeds are associated with the risks of ischemic stroke and intracranial hemorrhage, causing clinical dilemmas for antithrombotic treatment decisions. We aimed to evaluate the risks of intracranial hemorrhage and ischemic stroke associated with microbleeds in patients with atrial fibrillation treated with vitamin K antagonists, direct oral anticoagulants, antiplatelets, and combination therapy (i.e. concurrent oral anticoagulant and antiplatelet). METHODS: We included patients with documented atrial fibrillation from the pooled individual patient data analysis by the Microbleeds International Collaborative Network. Risks of subsequent intracranial hemorrhage and ischemic stroke were compared between patients with and without microbleeds, stratified by antithrombotic use. RESULTS: A total of 7,839 patients were included. The presence of microbleeds was associated with an increased relative risk of intracranial hemorrhage (adjusted hazard ratio [aHR] = 2.74, 95% confidence interval = 1.76-4.26) and ischemic stroke (aHR = 1.29, 95% confidence interval = 1.04-1.59). For the entire cohort, the absolute incidence of ischemic stroke was higher than intracranial hemorrhage regardless of microbleed burden. However, for the subgroup of patients taking combination of anticoagulant and antiplatelet therapy, the absolute risk of intracranial hemorrhage exceeded that of ischemic stroke in those with 2 to 4 microbleeds (25 vs 12 per 1,000 patient-years) and ≥ 11 microbleeds (94 vs 48 per 1,000 patient-years). INTERPRETATION: Patients with atrial fibrillation and high burden of microbleeds receiving combination therapy have a tendency of higher rate of intracranial hemorrhage than ischemic stroke, with potential for net harm. Further studies are needed to help optimize stroke preventive strategies in this high-risk group. ANN NEUROL 2023;94:61-74.

Published
2023

7. Cancer of the Duodenum — Surgical Treatment

Author

Imaizumi, T., Ishii, M., Tobita, K., Douwaki, S., Makuuchi, H., Beger, Hans G., editor, Matsuno, Seiki, editor, Cameron, John L., editor, Rau, Bettina M., editor, Sunamura, Makoto, editor, and Schulick, Richard D., editor

Published
2008
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11. Constructing Graphical Models for Multi-Source Data: Sparse Network and Component Analysis

Author

Tio, P., Waldorp, L., VanDeun, K., Imaizumi, T., Okada, A., Miyamoto, S., Sakaori, F., Yamamoto, Y., Vichi, M., Psychology Other Research (FMG), and Psychologische Methodenleer (Psychologie, FMG)

Subjects
Complex data type, Structure (mathematical logic), Computer science, Gaussian, computer.software_genre, symbols.namesake, Lasso (statistics), Component analysis, Multi source data, Component (UML), symbols, Data mining, Graphical model, computer
Abstract

Gaussian graphical models (GGMs) are a popular method for analysing complex data by modelling the unique relationships between variables. Recently, a shift in interest has taken place from investigating relationships within a (sub)discipline (e.g. genetics) to estimating relationships between variables from various subdisciplines (e.g. how gene expression relates to cognitive performance). It is thus not surprising that there is an increasing need for analysing large, so-called multi-source datasets, each containing detailed information from many data sources on the same individuals. GGMs are a straightforward statistical candidate for estimating unique cross-source relationships from such network-oriented data. However, the multi-source nature of the data poses two challenges: First, different sources may inherently differ from one another, biasing the estimation of the relations. Second, GGMs are not cut out for separating cross-source relationships from all other, source-specific relationships. In this paper we propose the addition of a simultaneous-component-model pre-processing step to the Gaussian graphical model, the combination of which is suitable for estimating cross-source relationships from multi-source data. Compared to the graphical lasso (a commonly used GGM technique), this Sparse Network and Component (SNAC) model more accurately estimates the unique cross-source relationships from multi-source data. This holds in particular when the multi-source data contains more variables than observations (p > n). Neither differences in sparseness of the underlying component structure of the data nor in the relative dominance of the cross-source compared to source-specific relationships strongly affect the relationship estimates. Sparse Network and Component analysis, a hybrid component-graphical model, is a promising tool for modelling unique relationships between different data sources, thus providing insight into how various disciplines are connected to one another.

Published
2020

20. Quantitative assessment of multiple fish species around artificial reefs using environmental DNA metabarcoding

Author

Inoue N, Furuich N, Sato M, Imaizumi T, Masayuki Ushio, and Ryogen Nambu

Subjects
Fishery, Echo sounding, Quantitative assessment, Fish species, Biodiversity, Sampling (statistics), Environmental science, Environmental DNA, Bay, Artificial reefs
Abstract

Since the early 1970s, many artificial reefs (ARs) have been deployed in Japanese coastal waters to create fisheries grounds. Recently, researchers began to use environmental DNA (eDNA) methods for biodiversity monitoring of aquatic species. A metabarcoding approach using internal standard DNAs (i.e., quantitative MiSeq sequencing) makes it possible to monitor eDNA concentrations of multiple species simultaneously. This method can improve the efficiency of monitoring AR effects on fishes. Our study investigated distributions of marine fishes at ARs and surrounding stations in the open oceanographic environment of Tateyama Bay, central Japan, using quantitative MiSeq sequencing and echo sounder survey. Using the quantitative metabarcoding method, we found higher quantities of fish eDNAs at the ARs than at surrounding stations and different fish species compositions between them. Comparisons with eco sounder survey also showed positive correlations between fish eDNA concentration and echo intensity, which indicates a highly localized signal of eDNA at each sampling station. These results suggest that quantitative MiSeq sequencing is a promising technique to complement conventional methods to monitor distributions of multiple fish species.

Published
2021
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21. Development of imaging-based risk scores for prediction of intracranial haemorrhage and ischaemic stroke in patients taking antithrombotic therapy after ischaemic stroke or transient ischaemic attack: a pooled analysis of individual patient data from cohort studies.

Author

Best J.G., Ambler G., Wilson D., Lee K.-J., Lim J.-S., Shiozawa M., Koga M., Li L., Lovelock C., Chabriat H., Hennerici M., Wong Y.K., Mak H.K.F., Prats-Sanchez L., Martinez-Domeno A., Inamura S., Yoshifuji K., Arsava E.M., Horstmann S., Purrucker J., Lam B.Y.K., Wong A., Kim Y.D., Song T.-J., Lemmens R., Eppinger S., Gattringer T., Uysal E., Tanriverdi Z., Bornstein N.M., Ben Assayag E., Hallevi H., Molad J., Nishihara M., Tanaka J., Coutts S.B., Polymeris A., Wagner B., Seiffge D.J., Lyrer P., Algra A., Kappelle L.J., Al-Shahi Salman R., Jager H.R., Lip G.Y.H., Fischer U., El-Koussy M., Mas J.-L., Legrand L., Karayiannis C., Phan T., Gunkel S., Christ N., Abrigo J., Leung T., Chu W., Chappell F., Makin S., Hayden D., Williams D.J., Mess W.H., Nederkoorn P.J., Barbato C., Browning S., Wiegertjes K., Tuladhar A.M., Maaijwee N., Guevarra A.C., Yatawara C., Mendyk A.-M., Delmaire C., Kohler S., van Oostenbrugge R., Zhou Y., Xu C., Hilal S., Gyanwali B., Chen C., Lou M., Staals J., Bordet R., Kandiah N., de Leeuw F.-E., Simister R., Hendrikse J., Kelly P.J., Wardlaw J., Soo Y., Fluri F., Srikanth V., Calvet D., Jung S., Kwa V.I.H., Engelter S.T., Peters N., Smith E.E., Hara H., Yakushiji Y., Orken D.N., Fazekas F., Thijs V., Heo J.H., Mok V., Veltkamp R., Ay H., Imaizumi T., Gomez-Anson B., Lau K.K., Jouvent E., Rothwell P.M., Toyoda K., Bae H.-J., Marti-Fabregas J., Werring D.J., Harkness K., Shaw L., Sword J., Mohd Nor A., Sharma P., Kelly D., Harrington F., Randall M., Smith M., Mahawish K., Elmarim A., Esisi B., Cullen C., Nallasivam A., Price C., Barry A., Roffe C., Coyle J., Hassan A., Birns J., Cohen D., Sekaran L., Parry-Jones A., Parry A., Hargroves D., Proschel H., Datta P., Darawil K., Manoj A., Burn M., Patterson C., Giallombardo E., Smyth N., Mansoor S., Anwar I., Marsh R., Ispoglou S., Chadha D., Prabhakaran M., Meenakishundaram S., O'Connell J., Scott J., Krishnamurthy V., Aghoram P., McCormick M., Sprigg N., O'Mahony P., Cooper M., Choy L., Wilkinson P., Leach S., Caine S., Burger I., Gunathilagan G., Guyler P., Emsley H., Davis M., Manawadu D., Pasco K., Mamun M., Luder R., Sajid M., Okwera J., Warburton E., Saastamoinen K., England T., Putterill J., Flossman E., Power M., Dani K., Mangion D., Suman A., Corrigan J., Lawrence E., Vahidassr D., Shakeshaft C., Brown M., Charidimou A., Cohen H., Banerjee G., Houlden H., White M., Yousry T., Flossmann E., Muir K., Gratz P., Mattle H., Panos L., Korczyn A., Kliper E., Maeder P., Gass A., Pachai C., Bracoub L., Douste-Blazy M.-Y., Fratacci M.D., Vicaut E., Sato S., Miwa K., Fujita K., Ide T., Ma H., Ly J., Singhal S., Chandra R., Slater L.-A., Soufan C., Moran C., Traenka C., Thilemann S., Fladt J., Gensicke H., Bonati L., Kim B.J., Han M.-K., Kang J., Ko E., Yang M.H., Jang M.S., Murphy S., Carty F., Akijian L., Thornton J., Schembri M., Douven E., Delgado-Mederos R., Marin R., Camps-Renom P., Guisado-Alonso D., Nunez F., Medrano-Martorell S., Merino E., Iida K., Ikeda S., Irie H., Demirelli D.S., Medanta J.M., Zerna C., Hernandez M.V., Armitage P., Heye A., Munoz-Maniega S., Sakka E., Thrippleton M., Dennis M., Beigneux Y., Silva M., Venketasubramanian N., Ho S.L., Cheung R.T.F., Chan K.H., Teo K.C., Hui E., Kwan J.S.K., Chang R., Tse M.Y., Hoi C.P., Chan C.Y., Chan O.L., Cheung R.H.K., Wong E.K.M., Leung K.T., Tsang S.F., Ip H.L., Ma S.H., Ma K., Fong W.C., Li S.H., Li R., Ng P.W., Wong K.K., Liu W., Wong L., Ramos L., De Schryver E., Jobsis J., van der Sande J., Brouwers P., Roos Y., Stam J., Bakker S., Verbiest H., Schoonewille W., Linn C., Hertzberger L., van Gemert M., Berntsen P., Van Dam-Nolen D., Kooi M.E., Van der Lugt A., Koudstaal P., Leff A., Ward N., Nachev P., Perry R., Ozkan H., Mitchell J., Best J.G., Ambler G., Wilson D., Lee K.-J., Lim J.-S., Shiozawa M., Koga M., Li L., Lovelock C., Chabriat H., Hennerici M., Wong Y.K., Mak H.K.F., Prats-Sanchez L., Martinez-Domeno A., Inamura S., Yoshifuji K., Arsava E.M., Horstmann S., Purrucker J., Lam B.Y.K., Wong A., Kim Y.D., Song T.-J., Lemmens R., Eppinger S., Gattringer T., Uysal E., Tanriverdi Z., Bornstein N.M., Ben Assayag E., Hallevi H., Molad J., Nishihara M., Tanaka J., Coutts S.B., Polymeris A., Wagner B., Seiffge D.J., Lyrer P., Algra A., Kappelle L.J., Al-Shahi Salman R., Jager H.R., Lip G.Y.H., Fischer U., El-Koussy M., Mas J.-L., Legrand L., Karayiannis C., Phan T., Gunkel S., Christ N., Abrigo J., Leung T., Chu W., Chappell F., Makin S., Hayden D., Williams D.J., Mess W.H., Nederkoorn P.J., Barbato C., Browning S., Wiegertjes K., Tuladhar A.M., Maaijwee N., Guevarra A.C., Yatawara C., Mendyk A.-M., Delmaire C., Kohler S., van Oostenbrugge R., Zhou Y., Xu C., Hilal S., Gyanwali B., Chen C., Lou M., Staals J., Bordet R., Kandiah N., de Leeuw F.-E., Simister R., Hendrikse J., Kelly P.J., Wardlaw J., Soo Y., Fluri F., Srikanth V., Calvet D., Jung S., Kwa V.I.H., Engelter S.T., Peters N., Smith E.E., Hara H., Yakushiji Y., Orken D.N., Fazekas F., Thijs V., Heo J.H., Mok V., Veltkamp R., Ay H., Imaizumi T., Gomez-Anson B., Lau K.K., Jouvent E., Rothwell P.M., Toyoda K., Bae H.-J., Marti-Fabregas J., Werring D.J., Harkness K., Shaw L., Sword J., Mohd Nor A., Sharma P., Kelly D., Harrington F., Randall M., Smith M., Mahawish K., Elmarim A., Esisi B., Cullen C., Nallasivam A., Price C., Barry A., Roffe C., Coyle J., Hassan A., Birns J., Cohen D., Sekaran L., Parry-Jones A., Parry A., Hargroves D., Proschel H., Datta P., Darawil K., Manoj A., Burn M., Patterson C., Giallombardo E., Smyth N., Mansoor S., Anwar I., Marsh R., Ispoglou S., Chadha D., Prabhakaran M., Meenakishundaram S., O'Connell J., Scott J., Krishnamurthy V., Aghoram P., McCormick M., Sprigg N., O'Mahony P., Cooper M., Choy L., Wilkinson P., Leach S., Caine S., Burger I., Gunathilagan G., Guyler P., Emsley H., Davis M., Manawadu D., Pasco K., Mamun M., Luder R., Sajid M., Okwera J., Warburton E., Saastamoinen K., England T., Putterill J., Flossman E., Power M., Dani K., Mangion D., Suman A., Corrigan J., Lawrence E., Vahidassr D., Shakeshaft C., Brown M., Charidimou A., Cohen H., Banerjee G., Houlden H., White M., Yousry T., Flossmann E., Muir K., Gratz P., Mattle H., Panos L., Korczyn A., Kliper E., Maeder P., Gass A., Pachai C., Bracoub L., Douste-Blazy M.-Y., Fratacci M.D., Vicaut E., Sato S., Miwa K., Fujita K., Ide T., Ma H., Ly J., Singhal S., Chandra R., Slater L.-A., Soufan C., Moran C., Traenka C., Thilemann S., Fladt J., Gensicke H., Bonati L., Kim B.J., Han M.-K., Kang J., Ko E., Yang M.H., Jang M.S., Murphy S., Carty F., Akijian L., Thornton J., Schembri M., Douven E., Delgado-Mederos R., Marin R., Camps-Renom P., Guisado-Alonso D., Nunez F., Medrano-Martorell S., Merino E., Iida K., Ikeda S., Irie H., Demirelli D.S., Medanta J.M., Zerna C., Hernandez M.V., Armitage P., Heye A., Munoz-Maniega S., Sakka E., Thrippleton M., Dennis M., Beigneux Y., Silva M., Venketasubramanian N., Ho S.L., Cheung R.T.F., Chan K.H., Teo K.C., Hui E., Kwan J.S.K., Chang R., Tse M.Y., Hoi C.P., Chan C.Y., Chan O.L., Cheung R.H.K., Wong E.K.M., Leung K.T., Tsang S.F., Ip H.L., Ma S.H., Ma K., Fong W.C., Li S.H., Li R., Ng P.W., Wong K.K., Liu W., Wong L., Ramos L., De Schryver E., Jobsis J., van der Sande J., Brouwers P., Roos Y., Stam J., Bakker S., Verbiest H., Schoonewille W., Linn C., Hertzberger L., van Gemert M., Berntsen P., Van Dam-Nolen D., Kooi M.E., Van der Lugt A., Koudstaal P., Leff A., Ward N., Nachev P., Perry R., Ozkan H., and Mitchell J.

Abstract

Background: Balancing the risks of recurrent ischaemic stroke and intracranial haemorrhage is important for patients treated with antithrombotic therapy after ischaemic stroke or transient ischaemic attack. However, existing predictive models offer insufficient performance, particularly for assessing the risk of intracranial haemorrhage. We aimed to develop new risk scores incorporating clinical variables and cerebral microbleeds, an MRI biomarker of intracranial haemorrhage and ischaemic stroke risk. Method(s): We did a pooled analysis of individual-patient data from the Microbleeds International Collaborative Network (MICON), which includes 38 hospital-based prospective cohort studies from 18 countries. All studies recruited participants with previous ischaemic stroke or transient ischaemic attack, acquired baseline MRI allowing quantification of cerebral microbleeds, and followed-up participants for ischaemic stroke and intracranial haemorrhage. Participants not taking antithrombotic drugs were excluded. We developed Cox regression models to predict the 5-year risks of intracranial haemorrhage and ischaemic stroke, selecting candidate predictors on biological relevance and simplifying models using backward elimination. We derived integer risk scores for clinical use. We assessed model performance in internal validation, adjusted for optimism using bootstrapping. The study is registered on PROSPERO, CRD42016036602. Finding(s): The included studies recruited participants between Aug 28, 2001, and Feb 4, 2018. 15 766 participants had follow-up for intracranial haemorrhage, and 15 784 for ischaemic stroke. Over a median follow-up of 2 years, 184 intracranial haemorrhages and 1048 ischaemic strokes were reported. The risk models we developed included cerebral microbleed burden and simple clinical variables. Optimism-adjusted c indices were 0.73 (95% CI 0.69-0.77) with a calibration slope of 0.94 (0.81-1.06) for the intracranial haemorrhage model and 0.63 (0.62-0.65) w

Published
2021

23. POS-599 EXTENDED-HOURS HEMODIALYSIS WITH LIBERALIZED DIET IS ASSOCIATED WITH REDUCED RISK OF NON-CARDIOVASCULAR MORTALITY IN ELDERLY DIALYSIS PATIENTS

Author

OKAZAKI, M., primary, Imaizumi, T., additional, Hishida, M., additional, Kurasawa, S., additional, Nishibori, N., additional, Kubo, Y., additional, Yasuda, Y., additional, Kato, S., additional, Inaguma, D., additional, Kaneda, F., additional, Kaneda, H., additional, and Maruyama, S., additional

Published
2021
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24. The Globality of Brands – A Question of Methods?

Author

Löffler, Michael, Decker, Reinhold, Imaizumi, T., Nakayama, A., and Yokoyama, S.

Subjects
Brand image measurement, Global brands, Multi-method approach, Brand associations, Correspondence analysis
Abstract

To develop a successful global brand strategy, a profound understanding of consumers’ perceptions about the brand in the main markets or countries is needed. Measuring the image of global brands includes at least two challenges. First, the market researcher has to select an appropriate method, knowing that different methods may yield different outcomes. Second, measuring the image of global brands differs categorically from measuring the image of local ones, as the global perspective requires measuring the brand image in diverse markets that may differ according to various cultural and socioeconomic characteristics. To meet these challenges, this paper proposes a two-factor approach, including a combination of the factors “method” and “country.” The data used for illustration purposes were collected in three leading target countries using a multi-method approach, including the free association method, the brand concept maps (BCM) method, and a usergenerated content (UGC) method. Multiple correspondence analysis (MCA) shows how the two-factor approach enables the uncovering of method and country effects and howthe suggested procedure leads to a holistic picture of the global brand image.

Published
2020

27. Exploring hierarchical concepts: theoretical and application comparisons

Author

Imaizumi, T, Nakayama, A, Yokoyama, S, Cavicchia, C, Vichi, M, Zaccaria, G, Imaizumi, T, Nakayama, A, Yokoyama, S, Cavicchia, C, Vichi, M, and Zaccaria, G

Abstract

Phenomena are usually multidimensional and their complexity cannot be directly explored via observable variables. For this reason, a hierarchical structure of nested latent concepts representing different levels of abstraction of the phenomenon under study may be considered. In this paper, we provide a comparison between a procedure based on hierarchical clustering methods and a novelty model recently proposed, called Ultrametric Correlation Matrix (UCM) model. The latter aims at reconstructing the data correlation matrix via an ultrametric correlation matrix and supplies a parsimonious representation of multidimensional phenomena through a partition of the observable variables defining a reduced number of latent concepts. Moreover, the UCM model highlights two main features related to concepts: the correlation among concepts and the internal consistency of a concept. The performances of the UCM model and the procedure based on hierarchical clustering methods are illustrated by an application to the Holzinger data set which represents a real demonstration of a hierarchical factorial structure. The evaluation of the different methodological approaches—the UCM model and the procedure based on hierarchical clustering methods—is provided in terms of classification of variables and goodness of fit, other than of their suitability to analyse bottom-up latent structures of variables.

Published
2020

28. Multilevel Model-Based Clustering: A New Proposal of Maximum-A-Posteriori Assignment

Author

Imaizumi, T, Okada, A, Miyamoto, S, Sakaori, F, Yamamoto, Y, Vichi, M, Bacci, S, Bartolucci, F, Pennoni, F, Imaizumi, T, Okada, A, Miyamoto, S, Sakaori, F, Yamamoto, Y, Vichi, M, Bacci, S, Bartolucci, F, and Pennoni, F

Abstract

We deal with the problem of latent variable prediction in the context of multilevel latent class models for categorical responses provided by individuals nested in groups. In particular, we propose a posterior assignment rule that jointly predicts the individual- and group-level latent variables. This proposal is alternative to the common maximum- a-posteriori rule, which is based on first predicting the latent variables at cluster level and, then, those at individual level. To illustrate the proposal, we show the results of two simulation studies and two applications on data related to the national and the international assessment of student skills.

Published
2020

48. 5th Japanese Association of Hepato-Biliary-Pancreatic Surgery: October 14–15, 1993, Arcadia Ichigaya, Tokyo, Japan President: Fujio Hanyu

Author

Miyazaki, Kohji, Tsutsumi, Nobuo, Kitahara, Kenji, Mori, Michito, Sasatomi, Eizaburo, Hirohashi, Yoshimi, Mashima, Hideaki, Hisatsugu, Takeharu, Sugiura, Yoshiaki, Kamiya, Takashi, Hiroyasu, Shungo, Takatsu, Izuru, Yoshizumi, Yutaira, Shima, Shingo, Tanaka, Susumu, Tashiro, Seiki, Tsuji, Tatsuya, Oda, Tsutomu, Kanemitsu, Keiichiro, Kamimoto, Yukio, Hiraoka, Takehisa, Miyauchi, Yoshimasa, Tanaka, Junji, Arii, Shigeki, Fujita, Ken Ichi, Imamura, Masayuki, Kanai, Michio, Nimura, Yuji, Hayakawa, Naokazu, Kamiya, Junichi, Kondoh, Satoshi, Nagino, Masato, Miyachi, Masahiko, Tatsuya, Yoshikawa, Hanyu, Fujio, Nakamura, Mitsuji, Araida, Tatsuo, Azuma, Tsukasa, Mutou, Hiroaki, Moriyama, Toru, Kogure, Michio, Asano, Takehide, Enomoto, Kazuo, Yamamoto, Hiroshi, Kobayashi, Susumu, Nagashima, Tohru, Uematsu, Takeshi, Amano, Hodaka, Nakagouri, Toshio, Kainuma, Osamu, Tokoro, Yoshiharu, Matsui, Yoshifumi, Jinguh, Kazuhiko, Miyauchi, Hideaki, Maruyama, Michihiro, Iwashita, Chikara, Isono, Kaichi, Mimura, Hisashi, Hamazaki, Keisuke, Tsuge, Hiromu, Kawada, Noriyuki, Yamamoto, Junji, Yamasaki, Susumu, Kosuge, Tomoo, Shimada, Kazuaki, Takayama, Tadatoshi, Ozaki, Hideo, Tomioka, Tsutomu, Ikematsu, Yoshito, Matsukawa, Shunichi, Tajima, Yoshitsugu, Sasaki, Makoto, Segawa, Tohru, Tsunoda, Tsukasa, Kanematsu, Takashi, Yamanaka, Naoki, Okamoto, Eizo, Oriyama, Tsuyoshi, Andoh, Tatsuya, Furukawa, Kazutaka, Todoroki, Takeshi, Kawamoto, Toru, Orii, Kazuo, Fukao, Katashi, Saida, Yukihisa, Yamamoto, Masakazu, Takasaki, Ken, Ootsubo, Takehito, Katsuragawa, Hideo, Maruyama, Chifumi, Yoshikawa, Tatsuya, Saisho, Hiromitsu, Sumita, Masatoshi, Okada, Shuichi, Tsuyuguchi, Toshio, Tsuchiya, Yukihiro, Ohto, Masao, Hirohashi, Kazuhiro, Kinoshita, Hiroaki, Kubo, Shoji, Tanaka, Hiromu, Tsukamoto, Tadashi, Hamba, Hiroyuki, Shuto, Taichi, Takemura, Shigekazu, Ikeda, Kazuo, Okuda, Toyokazu, Nakamura, Satoshi, Nishiyama, Raisuke, Konno, Hiroyuki, Baba, Shozo, Ogura, Yoshifumi, Tabata, Masanti, Takahashi, Koji, Ido, Masayoshi, Isaji, Shuji, Yokoi, Hajime, Noguchi, Takashi, Kawarada, Yoshifumi, Mizumoto, Ryuji, Nakao, Akimasa, Takagi, Hiroshi, Nagakawa, Takukazu, Ueno, Keiichi, Ohta, Tetsuo, Kayahara, Masato, Yoshimitsu, Yutaka, Suzaki, Yuhkei, Tukioka, Yuhji, Miyazaki, Itsuo, Imaizumi, T., Hanyu, F., Suzuki, M., Nakasako, T., Harada, N., Arai, T., Hirose, T., Fukuda, A., Munakata, S., Ozawa, F., Nagai, Hideo, Kondo, Yasuo, Yasuda, Toshihiko, Ishibashi, Toshimitsu, Kasahara, Kogoro, Kanazawa, Kyotaro, Ishikawa, Yasuro, Konagaya, Ichiro, Naoe, Teturo, Amano, Hitoshi, Kasugai, Hisashi, Sakai, Shigeru, Kano, Nobuyasu, Yamakawa, Tatsuo, Tagaya, Nobumi, Kaneko, Mitsuo, Takagi, Kazutoshi, Itoh, Makoto, Kogure, Hiroaki, Tajima, Yoshio, Yamamoto, Hideo, Kondo, Satoshi, Hamaguchi, Katsura, Komatu, Shunichiro, Yoshida, Masayuki, Kimura, Taizo, Tsugawa, Kouichiro, Konishi, Kohji, Takada, Osamu, Nagata, Takuya, Futagami, Fumio, Yamamoto, Seiichi, Maeda, Kiichi, Yabushita, Kazuhisa, Kuroda, Yoshitaka, Tsuji, Masahiko, Takada, Johji, Saji, Yutaka, Arisato, Hitoshi, Kurauchi, Nobuaki, Kazui, Keizo, Yamaga, Shoji, Nakayama, Masato, Uchino, Junichi, Takahasi, Shin, Tsuzuki, Toshiharu, Nakano, Akira, Sekido, Hitoshi, Endo, Itaru, Mochizuki, Yasuhisa, Fukushima, Tadao, Fukasawa, Shingo, Yoda, Kohei, Ogura, Kensuke, Shimada, Hiroshi, Tsukada, Kazuhiro, Hatakeyama, Katsuyosi, Kurosaki, Isao, Uchida, Katsuyuki, Shirai, Yosio, Muto, Terukazu, Takano, Seigo, Seki, Noriyoshi, Ooishi, Toshihiko, Watanabe, Yoshihiro, Higaki, Tokio, Moriguchi, Masanori, Itou, Yutaka, Tanaka, Takashi, Sasaki, Ryoko, Kanno, Senji, Hayakawa, Yoshiro, Murakami, Masahiko, Kawamura, Hidenobu, Shimada, Yutaka, Suto, Takayuki, Tamasawa, Yoshiyuki, Saito, Kazuyoshi, Sasatomi, Eizaburoh, Okuno, Toshitaka, Nakayama, Shinji, Fukuhara, Toshiyuki, Hashimoto, Takashi, Takamine, Yoshikazu, Konishi, Yutaka, Tani, Tomohiko, Kajiwara, Tatehiro, Okada, Setuo, Hashimoto, Tetuaki, Wakabayashi, Hisao, Maeba, Takashi, Tanaka, Satoshi, Oishi, Toshinori, Kumazawa, Ken ichi, Kubota, Koichi, Hosokawa, Toshihiko, Yoshizawa, Shuichi, Asami, Yoshiaki, Shiozawa, Shun ichi, Otani, Yoichi, Haga, Shunsuke, Kajiwara, Tetsuro, Harada, Akio, Kurokawa, Tsuyoshi, Maruyama, Hirotaka, Kobayashi, Hiroyuki, Nonami, Toshiaki, Katsuramaki, Tadashi, Kimura, Masami, Takasaka, Hajime, Mukaiya, Mitsuhiro, Yamashiro, Kazuhiro, Mikami, Toshihiko, Kimura, Hiromichi, Hirata, Koichi, Mimura, Takuji, Koguchi, Keiji, Hamazaki, Keisuke, Mori, Masanobu, Edahiro, Tooru, Orita, Kunzo, Koyama, Hiroshi, Taniguchi, Hiroki, Oguro, Atsushi, Takeuchi, Kazumi, Miyata, Keigo, Tanaka, Hiroki, Takahashi, Toshio, Ishii, Takahiro, Kim, Yang Il, Aramaki, Masanori, Nakashima, Kimihiro, Yochida, Takanori, Kobayashi, Michio, Ohura, Shinsuke, Beppu, Tomoe, Ohashi, Kaoru, Watanabe, Isamu, Kojima, Kuniaki, Fukasawa, Masaki, Futagawa, Shunji, Watanabe, Xoshihiro, Takahashi, Tomohide, Shimazu, Motohide, Aoki, Haruo, Naomoto, Yoshio, Kawata, Noriyuki, Hamazaki, Keisuke, Mori, Seiji, Misawa, Atushi, Kawamura, Eisuke, Tanaka, Tsuneo, Tomoda, Fumihito, Ichikawa, Nobutaka, Tanaka, Wataru, Yasui, Chiaki, Kuroda, Youichi, Mori, Naoharu, Yamaguchi, Akihiro, Isogai, Masatoshi, Hori, Akihiro, Maeda, Atsuyuki, Kawai, Masami, Takano, Manabu, Yamaguchi, Ryuzo, Kubota, Tomoyuki, Yamada, Tatsuharu, Yokoyama, Yukihiro, Matsunaga, Kazuya, Takeuchi, Yuuki, Ogawa, Tomoko, Tabata, Masami, Noguchi, Takashi, Nagashima, Ikuo, Sumita, Toshiyuki, Sodeyama, Motohide, Saito, Hideaki, Kawano, Nobuhiro, Muto, Tetsuichiro, Yoshida, Muneki, Takahashi, Tsuyoshi, Furuta, Kazunori, Izumika, Hisanao, Omiya, Harumi, Hiki, Yoshiki, Kakita, Akira, Hata, Tsunetake, Une, Yoshie, Nakajima, Yasuaki, Sato, Naoki, Matsushita, Michiaki, Kamiyama, Toshiya, Shimamura, Tsuyoshi, Nakagawa, Takahiro, Misawa, Kazuhito, Akiyama, Takayoshi, Seto, Keitarou, Saitou, Hitoshi, Kiriyama, Masato, Tomita, Fujio, Kosaka, Takeo, Kita, Ichirou, Takashima, Shigeki, Misuta, Koichiro, Endo, Ataru, Shimozawa, Eiji, Kato, Hiroyuki, Ooura, Takehiko, Nohira, Kunihiko, Shintomi, Yoshinao, Yuzawa, Kenji, Otsuka, Masaaki, Nozue, Mutsumi, Kato, Shuji, Kuramoto, Kenmei, Kasano, Yasuo, Tanimura, Hiroshi, Taniguchi, Katsutoshi, Hayashido, Motonori, Takifuji, Katsunari, Ozaki, Takashi, Sakamoto, Tomosaburo, Miyata, Masahiko, Izukura, Masaaki, Tanaka, Yasuhiro, Mizutani, Shin, Tominaga, Harumi, Yosidome, Katsuhide, Matsuda, Hikaru, Shimahara, Yasuyuki, Ichimiya, Masato, Jin, Maeng Bong, Kinoshita, Koichi, Mori, Keiichiro, Yamaoka, Yoshio, Kobayashi, Nobuaki, Ozawa, Kazue, Kawabe, Norihiko, Matsumoto, Sumio, Matsumoto, Kiyoshi, Yoshida, Yoshihiko, Sano, Makoto, Mori, Kenji, Tsukada, Norio, Kitagawa, Hiroaki, Mizuno, Yuhou, Kimura, Tadahiro, Suzuki, Hiroichirou, Banno, Tetsuya, Hiraoka, Hiroshi, Monta, Nobuyoshi, Noshima, Shinji, Shinagawa, Hidetaka, Esato, Kensuke, Miki, Tosio, Ito, Yutaka, Moriguti, Masanori, Takahasi, Tomohide, Kusano, Toshiomi, Honma, Kaneatsu, Tokumine, Fumio, Tamaki, Satoshi, Okushima, Norihiko, Muto, Yoshihiro, Furukawa, Masato, Kin, Yukon, Uesaka, Katsuhiko, Sugimoto, Yoshihiro, Tabuse, Katsuyoshi, Shimotsuma, Nakahiro, Yamamoto, Seiki, Sakaguchi, Masahiro, Mori, Kazunari, Oka, Masami, Asano, Satoshi, Kinoshita, Hiroyuki, Nagahama, Miho, Arii, Kazuo, Minami, Koji, Mizusaki, Kaoru, Takahasi, Tsuneo, Yanagisawa, Satoru, Naruse, Masaru, Akita, Haruyuki, Touyama, Youichi, Cho, Takemasa, Tanaka, Jun, Aoki, Teruaki, Yamauchi, Junichiro, Takeda, Kazunori, Shibuya, Kazuhiko, Sunamura, Makoto, Arai, Kousuke, Kawaguchi, Shinya, Kobari, Masao, Matsuno, Seiki, Shiobara, Masayuki, Miyazaki, Masaru, Itoh, Hiroshi, Kaiho, Takashi, Andoh, Katsuhiko, Anbiru, Satoshi, Hayashi, Shinichi, Gohchi, Eiji, Nagai, Motoki, Takanishi, Kijurou, Togawa, Akira, Ohtsuka, Masayuki, Sasada, Kazuhiro, Shimizu, Yoshiaki, Yoshioka, Shigeru, Yoshidome, Hiroyuki, Katoh, Atsushi, Nakamura, Syunta, Nakajima, Nobuyuki, Sutoh, Takayuki, Sasaki, Kohsuke, Sasaki, Ryoukou, Takayama, Wataru, Watanabe, Kazuo, Asahara, Toshimasa, Dohi, Kiyohiko, Nakahara, Hideki, Okamoto, Yusou, Ochi, Makoto, Katayama, Kouji, Itamoto, Toshiyuki, Nomura, Shinnya, Marubayashi, Seiji, Ono, Eiji, Yahata, Hiroshi, Fukuda, Yasuhiko, Ooshiro, Hisashi, Sasaki, Makoto, Tsunoda, Tsukasa, Matsunaga, Naofumi, Maki, Atsuhiko, Takabayashi, Arimichi, Kurumi, Yoshimasa, Kishida, Akihiro, Haeuchi, Kazuo, Yamamoto, Takumi, Watanabe, Hidejiro, Hase, Takanobu, Kodama, Masashi, Marugami, Yoshihisa, Matubara, Toshiki, Futawatari, Hisatomo, Nakamura, Kohji, Imazu, Hiroki, Arai, Kazufumi, Mori, Kikuo, Suganima, Masashi, Morishita, Hirosi, Sasayama, Yoshinori, Tsuda, Singo, Maruta, Yuuji, Muraoka, Masaaki, Ochiai, Masahiro, Funabiki, Takahiko, Siobara, Masayuki, Kondoh, Yoichiro, Hayashi, Jitsuo, Tanigawa, Kanji, Noguchi, Takashi, Okamoto, Yuzo, Nomura, Shinya, Okamoto, Tomoyoshi, Inagaki, Yoshinori, Takahashi, Tsuneo, Shinagawa, Hidenori, Morita, Nobuyoshi, Kato, Tomohiro, Hamasaki, Keisuke, Tuge, Hiroshi, Isozaki, Hiroshi, Okajima, Kunio, Ishibashi, Takashi, Morita, Shinsho, Hara, Hitoshi, Tanimura, Masakazu, Tezuka, Kiyotaka, Akimoto, Hiroshi, Niki, Masami, Okuda, Junji, Kogure, Kimitaka, Ishizaki, Masatoshi, Nemoto, Masaaki, Muratani, Mitsugu, Kosugi, Mitsuyo, Kiyohara, Kaoru, Yano, Takashi, Iida, Toshio, Kida, Hideya, Akasaka, Yoshikazu, Amano, Kazuya, Fujimori, Kenji, Ishihara, Akinori, Kakizaki, Kenji, Yamauchi, Hidemi, Uno, Satomi, Osada, Shinji, Kanno, Akihiro, Katou, Motohisa, Sugiyama, Yasuyuki, Miya, Kiichi, Saji, Shigetoyo, Horimi, Tadashi, Ishikawa, Tadanori, Takasaki, Motohiro, Morita, Sojiro, Nakamura, Tohru, Suganuma, Yasushi, Aoki, Hideki, Hatsuse, Kazuo, Terahata, Shintaro, Irie, Toshiyuki, Murayama, Michinori, Aihara, Tsukasa, Tamakuma, Shoetsu, Uyama, Ichiro, Iida, Shuhei, Ogiwara, Hiroyuki, Kikuchi, Kaichiro, Takahara, Tetsuya, Katou, Yutaro, Kwon, A Hon, Uetsuji, Shoji, Yamada, Osamu, Inoue, Tomohisa, Kurimoto, Shuji, Komada, Hisanao, Kamiyama, Yasuo, Boku, Tsunehide, Kohno, Hitoshi, Nagasue, Naofumi, Hayashi, Takafumi, Yamanoi, Akira, Uchida, Masaaki, Takemoto, Yoshinari, Makino, Yoshinari, Ono, Takashi, Hayashi, Junko, Nakamura, Teruhisa, Kikuchi, Toshiyuki, Okazuini, Shin ichi, Iwasaki, Koutarou, Makino, Harufumi, Niimoto, Syuichi, Hirose, Kazurou, Seki, Hiroaki, Isobe, Yoshiaki, Yamaguchi, Akio, Nakagawara, Gizou, Idota, Nozomi, Shono, Satoshi, Sakai, Yoshihiro, Kakihara, Minoru, Suzuki, Hideaki, Konishi, Masaru, Ryu, Munemasa, Shimamura, Yoshiyuki, Kinoshita, Taira, Kawano, Noriaki, Tanizaki, Hiroshi, Arai, Yoshihide, Ohashi, Kazuo, Nakajima, Yoshiyuki, Masahiro, Tsutsumi, Kanehiro, Hiromiti, Fukuoka, Toshiyuki, Naito, Azusa, Konishi, Yoichi, Nakano, Hiroshige, Kawamura, Hiroshi, Ishiyama, Shuichi, Seo, Nobuo, Fuse, Akira, Kuzu, Hiroshi, Sutoh, Koichi, Tsukamoto, Masaru, Takamori, Shigeru, Fukazawa, Masaki, Okuyama, Kouichi, Watanabe, Shin, Takamatsu, Susumu, Goseki, Narihide, Teramoto, Kenichi, Katoh, Shouichi, Hara, Yuzuru, Dobashi, Yasuyuki, Endoh, Mitsuo, Ohta, Kyosuke, Kanamaru, Taich, Usami, Makoto, Sakata, Kazuya, Haji, Seiji, Kotani, Jorge, Iso, Atsunori, Kasahara, Hiroshi, Ku, Eisei, Yamamoto, Masahiro, Saitoh, Yoichi, Hirano, Satoshi, Katoh, Hiroyuki, Okajima, Susumu, Miyasaka, Yuji, Nishibe, Toshiya, Narita, Yoshiaki, Doke, Mitsuru, Okushiba, Shunichi, Koyama, Hiroshi, Mitsui, Takeshi, Miura, Shoji, Munemoto, Yoshinori, Asada, Yasuyuki, Kasahara, Yoshio, Nagasato, Atsushi, Iida, Yoshiro, Saito, Hideo, Fujisawa, Masakiyo, Hazama, Shoichi, Oka, Masaaki, Hiwaki, Kazuhisa, Hiraki, Sakurao, Tangoku, Akira, Shimoda, Kouji, Suzuki, Michinari, Iizuka, Norio, Wadamori, Kenji, Suzuki, Takashi, Kimura, Wataru, Kanazawa, Gyotaro, Oka, Masatni, Iwagaki, T., Takada, T., Yasuda, H., Uchiyama, K., Hasegawa, H., Ushitani, H., Yamakawa, Y., Fujii, Shoichi, Ooki, Shigeo, Sugita, Akira, Togo, Shinji, Imai, Shinsuke, Fukazawa, Shingo, Ishihara, Shinichi, Masui, Hidenobu, Yamamoto, Masayoshi, Yoda, Kouhei, Yanaga, Katsuhiko, Shimada, Mitsuo, Higashi, Hidefumi, Taketomi, Akinobu, Matsumata, Takashi, Sugimachi, Keizo, Nishida, Minekatsu, Yagyu, Takashi, Nakashima, Kazutaka, Shimizu, Ryoichi, Uchiyama, Tetsushi, Miyazaki, Masaru, Ambiru, Satoshi, Takanishi, Kijuro, Togawa, Akira, Yoshitome, Hiroyuki, Nakamura, Shunta, Sano, Yoshiaki, Suwa, Toshikazu, Kondo, Satosi, Koide, Shinji, Torimoto, Katsuji, Denno, Rhyuichi, and Hamazaki, Keisuke

Published
1993
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