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1,185 results on '"Sato, Chiaki"'

9. DDIT3-amplified or low-polysomic pleomorphic sarcomas without MDM2 amplification: Clinicopathological review and immunohistochemical profile of nine cases

Author

Mori, Taro, Iwasaki, Takeshi, Sonoda, Hiroki, Kawaguchi, Kengo, Tomonaga, Takumi, Furukawa, Hiroshi, Sato, Chiaki, Shiraishi, Sakura, Taguchi, Kenichi, Tamiya, Sadafumi, Yoneda, Reiko, Oshiro, Yumi, Matsunobu, Tomoya, Abe, Chie, Kuboyama, Yusuke, Ueki, Nozomi, Kohashi, Kenichi, Yamamoto, Hidetaka, Nakashima, Yasuharu, and Oda, Yoshinao

Published
2024
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10. Welding and Joining

Author

Hirata, Yoshinori, Fujii, Hidetoshi, Sato, Chiaki, Serizawa, Hisashi, Hull, Robert, Series Editor, Jagadish, Chennupati, Series Editor, Kawazoe, Yoshiyuki, Series Editor, Kruzic, Jamie, Series Editor, Osgood Jr., Richard, Series Editor, Parisi, Jürgen, Series Editor, Pohl, Udo W., Series Editor, Seong, Tae-Yeon, Series Editor, Uchida, Shin-ichi, Series Editor, Wang, Zhiming M., Series Editor, Kishi, Teruo, editor, Fukutomi, Hiroshi, With Contrib. by, Hyodo, Tomoaki, With Contrib. by, and Kotobuki, Sakurako, With Contrib. by

Published
2023
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17. Welding and Joining

Author

Hirata, Yoshinori, primary, Fujii, Hidetoshi, additional, Sato, Chiaki, additional, and Serizawa, Hisashi, additional

Published
2023
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19. Dedifferentiated liposarcoma in the extremity and trunk wall: A multi-institutional study of 132 cases by the Japanese Musculoskeletal Oncology Group (JMOG)

Author

Morii, Takeshi, Anazawa, Ukei, Sato, Chiaki, Iwata, Shintaro, Nakagawa, Makoto, Endo, Makoto, Nakamura, Tomoki, Ikuta, Kunihiro, Nishida, Yoshihiro, Nakayama, Robert, Udaka, Toru, Kawamoto, Teruya, Kito, Munehisa, Sato, Kenji, Imanishi, Jungo, Akiyama, Toru, Kobayashi, Hiroshi, Nagano, Akihito, Outani, Hidetatsu, Toki, Shunichi, Nishisho, Toshihiko, Sasa, Keita, Suehara, Yoshiyuki, Kawano, Hirotaka, Ueda, Takafumi, and Morioka, Hideo

Published
2023
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26. Achalasia phenotypes and prediction of peroral endoscopic myotomy outcomes using machine learning.

Author

Takahashi, Kazuya, Sato, Hiroki, Shimamura, Yuto, Abe, Hirofumi, Shiwaku, Hironari, Shiota, Junya, Sato, Chiaki, Hamada, Kenta, Ominami, Masaki, Hata, Yoshitaka, Fukuda, Hisashi, Ogawa, Ryo, Nakamura, Jun, Tatsuta, Tetsuya, Ikebuchi, Yuichiro, Yokomichi, Hiroshi, Terai, Shuji, and Inoue, Haruhiro

Subjects
ESOPHAGEAL achalasia, MACHINE learning, GASTROESOPHAGEAL reflux, PHENOTYPES, MYOTOMY, BODY mass index, ESOPHAGEAL motility
Abstract

Objectives: High‐resolution manometry (HRM) and esophagography are used for achalasia diagnosis; however, achalasia phenotypes combining esophageal motility and morphology are unknown. Moreover, predicting treatment outcomes of peroral endoscopic myotomy (POEM) in treatment‐naïve patients remains an unmet need. Methods: In this multicenter cohort study, we included 1824 treatment‐naïve patients diagnosed with achalasia. In total, 1778 patients underwent POEM. Clustering by machine learning was conducted to identify achalasia phenotypes using patients' demographic data, including age, sex, disease duration, body mass index, and HRM/esophagography findings. Machine learning models were developed to predict persistent symptoms (Eckardt score ≥3) and reflux esophagitis (RE) (Los Angeles grades A–D) after POEM. Results: Machine learning identified three achalasia phenotypes: phenotype 1, type I achalasia with a dilated esophagus (n = 676; 37.0%); phenotype 2, type II achalasia with a dilated esophagus (n = 203; 11.1%); and phenotype 3, late‐onset type I–III achalasia with a nondilated esophagus (n = 619, 33.9%). Types I and II achalasia in phenotypes 1 and 2 exhibited different clinical characteristics from those in phenotype 3, implying different pathophysiologies within the same HRM diagnosis. A predictive model for persistent symptoms exhibited an area under the curve of 0.70. Pre‐POEM Eckardt score ≥6 was the greatest contributing factor for persistent symptoms. The area under the curve for post‐POEM RE was 0.61. Conclusion: Achalasia phenotypes combining esophageal motility and morphology indicated multiple disease pathophysiologies. Machine learning helped develop an optimal risk stratification model for persistent symptoms with novel insights into treatment resistance factors. [ABSTRACT FROM AUTHOR]

Published
2024
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27. Multi-Objective Optimization of Adhesive Joint Strength and Elastic Modulus of Adhesive Epoxy with Active Learning.

Author

Kraisornkachit, Paripat, Naito, Masanobu, Kang, Chao, and Sato, Chiaki

Subjects
ELASTIC modulus, ADHESIVE joints, MACHINE learning, ADHESIVES, EPOXY resins, PROPYLENE glycols, EPOXY coatings
Abstract

Studying multiple properties of a material concurrently is essential for obtaining a comprehensive understanding of its behavior and performance. However, this approach presents certain challenges. For instance, simultaneous examination of various properties often necessitates extensive experimental resources, thereby increasing the overall cost and time required for research. Furthermore, the pursuit of desirable properties for one application may conflict with those needed for another, leading to trade-off scenarios. In this study, we focused on investigating adhesive joint strength and elastic modulus, both crucial properties directly impacting adhesive behavior. To determine elastic modulus, we employed a non-destructive indentation method for converting hardness measurements. Additionally, we introduced a specimen apparatus preparation method to ensure the fabrication of smooth surfaces and homogeneous polymeric specimens, free from voids and bubbles. Our experiments utilized a commercially available bisphenol A-based epoxy resin in combination with a Poly(propylene glycol) curing agent. We generated an initial dataset comprising experimental results from 32 conditions, which served as input for training a machine learning model. Subsequently, we used this model to predict outcomes for a total of 256 conditions. To address the high deviation in prediction results, we implemented active learning approaches, achieving a 50% reduction in deviation while maintaining model accuracy. Through our analysis, we observed a trade-off boundary (Pareto frontier line) between adhesive joint strength and elastic modulus. Leveraging Bayesian optimization, we successfully identified experimental conditions that surpassed this boundary, yielding an adhesive joint strength of 25.2 MPa and an elastic modulus of 182.5 MPa. [ABSTRACT FROM AUTHOR]

Published
2024
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30. Clinical practice guidelines for rehabilitation nutrition in cerebrovascular disease, hip fracture, cancer, and acute illness: 2020 update

Author

Nishioka, Shinta, Aragane, Hideki, Suzuki, Norio, Yoshimura, Yoshihiro, Fujiwara, Dai, Mori, Takashi, Kanehisa, Yayoi, Iida, Yuki, Higashi, Keiichiro, Yoshimura-Yokoi, Yuri, Sato, Chiaki, Toyota, Miwa, Tanaka, Mai, Ishii, Yoshimasa, Kosaka, Shintaro, Kumagae, Naoko, Fujimoto, Atsushi, Omura, Kenji, Yoshida, Sadao, Wakabayashi, Hidetaka, and Momosaki, Ryo

Published
2021
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36. Blood flow ratio in the gastric conduit measured by laser Doppler flowmetry: A predictor of anastomotic leakage after esophagectomy.

Author

Ishida, Hirotaka, Fukutomi, Toshiaki, Taniyama, Yusuke, Sato, Chiaki, Okamoto, Hiroshi, Ozawa, Yohei, Onodera, Yu, Koseki, Ken, Unno, Michiaki, and Kamei, Takashi

Subjects
BLOOD flow, ESOPHAGECTOMY, BLOOD collection, LEAKAGE, LASERS, ESOPHAGEAL cancer, URINARY diversion, GASTRIC bypass
Abstract

Background: Anastomotic leakage after esophagectomy is a common complication. Laser Doppler flowmetry (LDF) can quantitatively evaluate the blood flow in the gastric conduit. Methods: A total of 326 patients who underwent thoracoscopic/robot‐assisted esophagectomy followed by gastric conduit reconstruction and end‐to‐side anastomosis were enrolled. We divided the gastric conduit into zones I (dominated by the right gastroepiploic vessels), II (dominated by the left gastroepiploic vessels), and III (perfused with short gastric vessels). Before pulling up the gastric conduit to the neck, LDF values were measured at the pylorus, the border between zones I and II (zone I/II), the border between zones II and III (zone II/III), and the gastric conduit tip (tip). The blood flow ratio was calculated as the LDF value divided by the LDF value at the pylorus. Results: Anastomotic leakage developed in 32 of 326 patients. Leakage was significantly associated with the blood flow ratio at the tip (p < 0.001), but not at zone I/II, zone II/III, and the anastomotic site. The receiver‐operating characteristic curve analysis identified an anastomotic leakage cutoff ratio of 0.41 (at the tip). A multivariate Cox analysis showed that a blood flow ratio <0.41 at the tip was an independent risk factor for anastomotic leakage (p < 0.001). Conclusion: Anastomotic leakage after esophagectomy was significantly associated with the blood flow ratio at the tip of the gastric conduit. Preservation of the blood supply to the tip via the gastric wall might contribute to a decreased incidence of anastomotic leakage. [ABSTRACT FROM AUTHOR]

Published
2024
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41. Achalasia phenotypes and prediction of per‐oral endoscopic myotomy outcomes using machine learning

Author

Takahashi, Kazuya, primary, Sato, Hiroki, additional, Shimamura, Yuto, additional, Abe, Hirofumi, additional, Shiwaku, Hironari, additional, Shiota, Junya, additional, Sato, Chiaki, additional, Hamada, Kenta, additional, Ominami, Masaki, additional, Hata, Yoshitaka, additional, Fukuda, Hisashi, additional, Ogawa, Ryo, additional, Nakamura, Jun, additional, Tatsuta, Tetsuya, additional, Ikebuchi, Yuichiro, additional, Yokomichi, Hiroshi, additional, Terai, Shuji, additional, and Inoue, Haruhiro, additional

Published
2023
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42. Correlation between 8-OHdG and disease-free survival in patients with esophageal squamous cell carcinoma following neoadjuvant chemotherapy

Author

Kunimitsu, Atsushi, primary, Fujishima, Fumiyoshi, additional, Sasano, Hironobu, additional, Mozumi, Takeru, additional, Higashimoto, Iku, additional, Akaishi, Ryujiro, additional, Koseki¹, Ken, additional, Ishida, Hitoraka, additional, Ozawa¹, Yohei, additional, Sato, Chiaki, additional, Okamoto¹, Hiroshi, additional, Taniyama¹, Yusuke, additional, Kamei¹, Takashi, additional, and Suzuki², Takashi, additional

Published
2023
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