- 著者
- Haruka Ozaki Kohji Takemura Rika Kizawa Takeshi Yamaguchi Chinatsu Komiyama Masato Tachi Hirotaka Maruno Yuko Tanabe Koichi Suyama Yuji Miura
- 出版者
- The Japanese Society of Internal Medicine
- 雑誌
- Internal Medicine (ISSN:09182918)
- 巻号頁・発行日
- pp.1453-22, (Released:2023-03-22)
- 参考文献数
- 12
- 被引用文献数
- 1
Aortitis is a rare adverse event associated with granulocyte colony-stimulating factor (G-CSF). Contrast-enhanced computed tomography (CECT) is widely used to diagnose G-CSF-associated aortitis. However, the usefulness of gallium scintigraphy for the diagnosis of G-CSF-associated aortitis is unknown. We herein report a set of pre- and post-treatment gallium scintigrams of a patient with G-CSF-associated aortitis. During the diagnosis, gallium scintigraphy revealed hot spots on the arterial walls that appeared inflamed on CECT. Both the CECT and gallium scintigraphy findings disappeared. Gallium scintigraphy can be a supportive diagnostic tool for G-CSF-associated aortitis, especially in patients with an impaired renal function or allergy to iodine contrast.
- 著者
- Eli Kaminuma Yukino Baba Masahiro Mochizuki Hirotaka Matsumoto Haruka Ozaki Toshitsugu Okayama Takuya Kato Shinya Oki Takatomo Fujisawa Yasukazu Nakamura Masanori Arita Osamu Ogasawara Hisashi Kashima Toshihisa Takagi
- 出版者
- The Genetics Society of Japan
- 雑誌
- Genes & Genetic Systems (ISSN:13417568)
- 巻号頁・発行日
- pp.19-00034, (Released:2020-03-26)
- 参考文献数
- 37
- 被引用文献数
- 3
Recently, the prospect of applying machine learning tools for automating the process of annotation analysis of large-scale sequences from next-generation sequencers has raised the interest of researchers. However, finding research collaborators with knowledge of machine learning techniques is difficult for many experimental life scientists. One solution to this problem is to utilise the power of crowdsourcing. In this report, we describe how we investigated the potential of crowdsourced modelling for a life science task by conducting a machine learning competition, the DNA Data Bank of Japan (DDBJ) Data Analysis Challenge. In the challenge, participants predicted chromatin feature annotations from DNA sequences with competing models. The challenge engaged 38 participants, with a cumulative total of 360 model submissions. The performance of the top model resulted in an area under the curve (AUC) score of 0.95. Over the course of the competition, the overall performance of the submitted models improved by an AUC score of 0.30 from the first submitted model. Furthermore, the 1st- and 2nd-ranking models utilised external data such as genomic location and gene annotation information with specific domain knowledge. The effect of incorporating this domain knowledge led to improvements of approximately 5%–9%, as measured by the AUC scores. This report suggests that machine learning competitions will lead to the development of highly accurate machine learning models for use by experimental scientists unfamiliar with the complexities of data science.
- 著者
- Eli Kaminuma Yukino Baba Masahiro Mochizuki Hirotaka Matsumoto Haruka Ozaki Toshitsugu Okayama Takuya Kato Shinya Oki Satoshi Ogasawara Hisashi Kashima Toshihisa Takagi
- 出版者
- The Molecular Biology Society of Japan
- 雑誌
- The 39th Annual Meeting of the Molecular Biology Society of Japan
- 巻号頁・発行日
- 2016-11-08