研究者詳細 - 中原　龍一

論文 - 中原　龍一

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LRP4 and Agrin Are Modulated by Cartilage Degeneration and Involved in β-Catenin Signaling in Human Articular Chondrocytes. 国際誌

Shuichi Naniwa, Keiichiro Nishida, Aki Yoshida, Yoshihisa Nasu, Ryuichi Nakahara, Takashi Ohtsuki, Yoshifumi Hotta, Noriyuki Shimizu, Chinatsu Ichikawa, Deting Lin, Noriaki Otsuka, Toshifumi Ozaki

International journal of molecular sciences 26 ( 3 ) 2025年1月

記述言語：英語掲載種別：研究論文（学術雑誌）

We investigated the roles of low-density lipoprotein receptor-related protein (LRP) 4 and its ligand Agrin in the pathophysiology of cartilage degeneration. Immunohistochemical analysis of human normal articular cartilage and cartilage tissues from patients with osteoarthritis (OA) obtained during surgery of the knee joint showed marked LRP4 expression in the early stages of OA, which then decreased with cartilage degeneration, whereas Agrin was consistently increased with cartilage degeneration. In normal human articular chondrocytes (NHACs), mild cyclic tensile strain (CTS) (0.5 Hz, 5% elongation, 2 h) increased the expression of LRP4 and aggrecan (ACAN), while intense CTS (0.5 Hz, 10% elongation, 6 h) increased the expression of Agrin without affecting LRP4 expression. Treatment with recombinant human (rh) Agrin downregulated the mRNA expression of LRP4 and ACAN, but upregulated the expression of LRP5/6, SRY-box transcription factor 9 (SOX9), Runt-related transcription factor 2 (RUNX2), and a disintegrin and metalloproteinase with thrombospondin motifs-4 (ADAMTS-4). Immunocytochemistry and Western blot analysis showed that rhAgrin treatment upregulated the expression of β-catenin and SOX9. Agrin knockdown by siAGRN transfection partially reduced the nuclear protein expression of β-catenin, which was increased with intense CTS. LRP4 knockdown by siLRP4 transfection increased the expression of LRP5/6, SOX9, RUNX2, ADAMTS-4, and Agrin. These results suggested that intense CTS increases the expression of Agrin, which might interfere with the role of LRP4 in the inhibition of LRP5/6 and their downstream β-catenin signaling, leading to cartilage degeneration.

DOI： 10.3390/ijms26031007

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The Three-Class Annotation Method Improves the AI Detection of Early-Stage Osteosarcoma on Plain Radiographs: A Novel Approach for Rare Cancer Diagnosis. 国際誌

Joe Hasei, Ryuichi Nakahara, Yujiro Otsuka, Yusuke Nakamura, Kunihiro Ikuta, Shuhei Osaki, Tamiya Hironari, Shinji Miwa, Shusa Ohshika, Shunji Nishimura, Naoaki Kahara, Aki Yoshida, Tomohiro Fujiwara, Eiji Nakata, Toshiyuki Kunisada, Toshifumi Ozaki

Cancers 17 ( 1 ) 2024年12月

記述言語：英語掲載種別：研究論文（学術雑誌）

Background/Objectives: Developing high-performance artificial intelligence (AI) models for rare diseases is challenging owing to limited data availability. This study aimed to evaluate whether a novel three-class annotation method for preparing training data could enhance AI model performance in detecting osteosarcoma on plain radiographs compared to conventional single-class annotation. Methods: We developed two annotation methods for the same dataset of 468 osteosarcoma X-rays and 378 normal radiographs: a conventional single-class annotation (1C model) and a novel three-class annotation method (3C model) that separately labeled intramedullary, cortical, and extramedullary tumor components. Both models used identical U-Net-based architectures, differing only in their annotation approaches. Performance was evaluated using an independent validation dataset. Results: Although both models achieved high diagnostic accuracy (AUC: 0.99 vs. 0.98), the 3C model demonstrated superior operational characteristics. At a standardized cutoff value of 0.2, the 3C model maintained balanced performance (sensitivity: 93.28%, specificity: 92.21%), whereas the 1C model showed compromised specificity (83.58%) despite high sensitivity (98.88%). Notably, at the 25th percentile threshold, both models showed identical false-negative rates despite significantly different cutoff values (3C: 0.661 vs. 1C: 0.985), indicating the ability of the 3C model to maintain diagnostic accuracy at substantially lower thresholds. Conclusions: This study demonstrated that anatomically informed three-class annotation can enhance AI model performance for rare disease detection without requiring additional training data. The improved stability at lower thresholds suggests that thoughtful annotation strategies can optimize the AI model training, particularly in contexts where training data are limited.

DOI： 10.3390/cancers17010029

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The use of biologic disease-modifying anti-rheumatic drugs does not increase surgical site infection or delayed wound healing after orthopaedic surgeries for rheumatoid arthritis. 国際誌

Yohei Kiso, Keiichiro Nishida, Ryozo Harada, Yoshihisa Nasu, Ryuichi Nakahara, Yoshifumi Hotta, Shuichi Naniwa, Toshifumi Ozaki

Modern rheumatology 2024年9月

記述言語：英語掲載種別：研究論文（学術雑誌）

OBJECTIVE: To investigate the effect of the use of biologic disease-modifying anti-rheumatic drugs (bDMARDs) on surgical site infection (SSI) and delayed wound healing (DWH) in rheumatoid arthritis (RA) patients undergoing orthopaedic surgery. PATIENTS AND METHODS: We retrospectively reviewed the records of 965 elective orthopaedic procedures undertaken in RA patients. The incidences of SSI and DWH were compared between the bDMARDs user and non-user groups. Subsequently, univariate and multivariate logistic regression analyses were performed to evaluate risk factors for SSI and DWH after propensity score (PS) matching. The incidence of postoperative flare-up was also examined. RESULTS: In 965 procedures, SSI and DWH were identified in 12 and 28 cases, respectively. SSI and DWH were identified in 3 and 17 of 414 procedures treated with bDMARDs, respectively. Flare-up occurred in 21 cases. PS matching identified 315 cases in both groups, with no significant difference in incidence between the two groups. No risk factors for SSI were identified, whereas age, diabetes mellitus, foot and ankle surgery and a history of musculoskeletal-related infection were identified as risk factors for DWH. CONCLUSION: The use of bDMARDs was not associated with an increased incidence of SSI or DWH, with the incidence of flare-up being relatively low.

DOI： 10.1093/mr/roae089

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High-quality expert annotations enhance artificial intelligence model accuracy for osteosarcoma X-ray diagnosis. 国際誌

Joe Hasei, Ryuichi Nakahara, Yujiro Otsuka, Yusuke Nakamura, Tamiya Hironari, Naoaki Kahara, Shinji Miwa, Shusa Ohshika, Shunji Nishimura, Kunihiro Ikuta, Shuhei Osaki, Aki Yoshida, Tomohiro Fujiwara, Eiji Nakata, Toshiyuki Kunisada, Toshifumi Ozaki

Cancer science 2024年9月

記述言語：英語掲載種別：研究論文（学術雑誌）

Primary malignant bone tumors, such as osteosarcoma, significantly affect the pediatric and young adult populations, necessitating early diagnosis for effective treatment. This study developed a high-performance artificial intelligence (AI) model to detect osteosarcoma from X-ray images using highly accurate annotated data to improve diagnostic accuracy at initial consultations. Traditional models trained on unannotated data have shown limited success, with sensitivities of approximately 60%-70%. In contrast, our model used a data-centric approach with annotations from an experienced oncologist, achieving a sensitivity of 95.52%, specificity of 96.21%, and an area under the curve of 0.989. The model was trained using 468 X-ray images from 31 osteosarcoma cases and 378 normal knee images with a strategy to maximize diversity in the training and validation sets. It was evaluated using an independent dataset of 268 osteosarcoma and 554 normal knee images to ensure generalizability. By applying the U-net architecture and advanced image processing techniques such as renormalization and affine transformations, our AI model outperforms existing models, reducing missed diagnoses and enhancing patient outcomes by facilitating earlier treatment. This study highlights the importance of high-quality training data and advocates a shift towards data-centric AI development in medical imaging. These insights can be extended to other rare cancers and diseases, underscoring the potential of AI in transforming diagnostic processes in oncology. The integration of this AI model into clinical workflows could support physicians in early osteosarcoma detection, thereby improving diagnostic accuracy and patient care.

DOI： 10.1111/cas.16330

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関節軟骨におけるLRP4とAGRINの発現調節因子とβ-catenin signalへの影響

浪花崇一, 市川千夏, 林徳てい, 志水紀之, 中原龍一, 那須義久, 尾崎敏文, 西田圭一郎

日本整形外科学会雑誌 98 ( 8 ) S1934 - S1934 2024年9月

テリボンオートインジェクターの投与時反応について

是友行美, 原田遼三, 高杉幸司, 江澤和彦, 藤田慎一郎, 後藤京子, 山本渉, 中原龍一, 那須義久, 西田圭一郎

日本骨粗鬆症学会雑誌 10 ( Suppl.1 ) 588 - 588 2024年9月

Utilizing the Metaverse to Provide Innovative Psychosocial Support for Pediatric, Adolescent, and Young Adult Patients with Rare Cancer

Joe Hasei, Hisashi Ishida, Hideki Katayama, Naoko Maeda, Akihito Nagano, Motoharu Ochi, Masako Okamura, Shintaro Iwata, Kunihiro Ikuta, Shinichirou Yoshida, Tomohiro Fujiwara, Eiji Nakata, Ryuichi Nakahara, Toshiyuki Kunisada, Toshifumi Ozaki

Cancers 16 ( 15 ) 2617 - 2617 2024年7月

掲載種別：研究論文（学術雑誌）出版者・発行元：MDPI AG

This study investigated the potential of the metaverse in providing psychological support for pediatric and AYA cancer patients, with a focus on those with rare cancers. The research involved ten cancer patients and survivors from four distinct regions in Japan, who participated in metaverse sessions using customizable avatars, facilitating interactions across geographical and temporal barriers. Surveys and qualitative feedback were collected to assess the psychosocial impact of the intervention. The results demonstrated that the metaverse enabled patients to connect with peers, share experiences, and receive emotional support. The anonymity provided by avatars helped reduce appearance-related anxiety and stigma associated with cancer treatment. A case study of a 19-year-old male with spinal Ewing’s sarcoma highlighted the profound emotional relief fostered by metaverse interactions. The findings suggest that integrating virtual spaces into healthcare models can effectively address the unique needs of pediatric and AYA cancer patients, offering a transformative approach to delivering psychosocial support and fostering a global patient community. This innovative intervention has the potential to revolutionize patient care in the digital age.

DOI： 10.3390/cancers16152617

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当院における関節リウマチに対する手部手術の変遷(2004-2022)

志水紀之, 市川千夏, 浪花崇一, 中原龍一, 那須義久, 尾崎敏文, 西田圭一郎

日本関節病学会誌 43 ( 2 ) 156 - 156 2024年6月

関節リウマチ手外科手術における手術部位感染/創傷治癒遅延の発生率とb/ts DAMRDs使用の影響

市川千夏, 志水紀之, 浪花崇一, 中原龍一, 那須義久, 尾崎敏文, 西田圭一郎

日本関節病学会誌 43 ( 2 ) 156 - 156 2024年6月

マルチモーダル大規模言語を用いた自動病理画像解析

中原龍一, 竹内孔一, 那須義久, 長谷井嬢, 高橋康, 西田圭一郎, 尾崎敏文

日本関節病学会誌 43 ( 2 ) 252 - 252 2024年6月

大規模言語モデルの外部文書読み込み(RAG)を用いた医療文書の自動Q&A開発

那須義久, 竹内孔一, 中原龍一, 長谷井嬢, 西田圭一郎, 尾崎敏文

日本関節病学会誌 43 ( 2 ) 251 - 251 2024年6月

マルチモーダル大規模言語を用いた自動病理画像解析

中原龍一, 竹内孔一, 那須義久, 長谷井嬢, 高橋康, 西田圭一郎, 尾崎敏文

日本関節病学会誌 43 ( 2 ) 252 - 252 2024年6月

若年者に対する人工関節手術:克服すべき困難と成功の秘訣若年例に対する人工肘関節置換術

那須義久, 中原龍一, 西田圭一郎, 尾崎敏文

日本関節病学会誌 43 ( 2 ) 213 - 213 2024年6月

変形性膝関節症に対するCoolief疼痛管理用高周波システムを用いた治療経験

原田遼三, 西田圭一郎, 那須義久, 中原龍一

日本関節病学会誌 43 ( 2 ) 266 - 266 2024年6月

大規模言語モデルの外部文書読み込み(RAG)を用いた医療文書の自動Q&A開発

那須義久, 竹内孔一, 中原龍一, 長谷井嬢, 西田圭一郎, 尾崎敏文

日本関節病学会誌 43 ( 2 ) 251 - 251 2024年6月

関節リウマチ手外科手術における手術部位感染/創傷治癒遅延の発生率とb/ts DAMRDs使用の影響

市川千夏, 志水紀之, 浪花崇一, 中原龍一, 那須義久, 尾崎敏文, 西田圭一郎

日本関節病学会誌 43 ( 2 ) 156 - 156 2024年6月

当院における関節リウマチに対する手部手術の変遷(2004-2022)

志水紀之, 市川千夏, 浪花崇一, 中原龍一, 那須義久, 尾崎敏文, 西田圭一郎

日本関節病学会誌 43 ( 2 ) 156 - 156 2024年6月

リウマチ性疾患の手術(上肢) 関節リウマチに対するMCP人工関節置換術と手関節・手指伸筋腱同時手術症例の検討

市川千夏, 西田圭一郎, 志水紀之, 浪花崇一, 堀田佳史, 中原龍一, 那須義久, 尾崎敏文

日本リウマチ学会総会・学術集会プログラム・抄録集 68回 532 - 532 2024年3月

自動統計のための日本語大規模言語モデルを用いたカルテサマリーからの情報抽出

中原龍一, 西田圭一郎, 那須義久, 原田遼三, 浪花崇一, 志水紀之, 市川千夏, 高橋康, 尾崎敏文

日本リウマチ学会総会・学術集会プログラム・抄録集 68回 714 - 714 2024年3月

リウマチ性疾患の手術(上肢) AVANTAとINTEGRAを用いたMCP人工指関節置換術の術後短期成績の比較

浪花崇一, 市川千夏, 志水紀之, 中原龍一, 那須義久, 尾崎敏文, 西田圭一郎

日本リウマチ学会総会・学術集会プログラム・抄録集 68回 532 - 532 2024年3月

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