著者
Hiroyuki Wakiguchi Yasuhiro Okamoto Manaka Matsunaga Yuichi Kodama Akinori Miyazono Shunji Seki Naohiro Ikeda Yoshifumi Kawano
出版者
国立感染症研究所 Japanese Journal of Infectious Diseases 編集委員会
雑誌
Japanese Journal of Infectious Diseases (ISSN:13446304)
巻号頁・発行日
pp.JJID.2015.362, (Released:2015-11-13)
参考文献数
10
被引用文献数
2

Cat scratch disease (CSD) is an infectious disease caused by Bartonella henselae. Atypical clinical presentations of CSD include prolonged fever and multiple hepatosplenic lesions, although these are rare. Furthermore, multiple renal lesions are extremely rare in CSD. The patient was an 11-year-old Japanese girl who had a prolonged fever of unknown cause after being scratched and bitten by a kitten. Abdominal computed tomography (CT) revealed multiple small, round hypodense lesions in both kidneys and in the spleen. Based on her history and the results of CT, a diagnosis of CSD was made; the diagnosis was confirmed with serological tests, which indicated antibodies against Bartonella henselae. After treatment with azithromycin, her fever immediately improved. Careful history taking and imaging are essential for the diagnosis of atypical CSD. In CT images, not only hepatosplenic lesions but also renal lesions are important features indicative of a diagnosis of atypical CSD. Subsequently, a diagnosis of CSD can be confirmed with specific serological tests. To the best of our knowledge, this is the first reported Japanese case of multiple renal and splenic lesions in a patient with CSD. Although atypical CSD is difficult to diagnose, an early diagnosis is important to prevent invasive examinations.
著者
Hiroyuki Wakiguchi Yasuhiro Okamoto Manaka Matsunaga Yuichi Kodama Akinori Miyazono Shunji Seki Naohiro Ikeda Yoshifumi Kawano
出版者
National Institute of Infectious Diseases, Japanese Journal of Infectious Diseases Editorial Committee
雑誌
Japanese Journal of Infectious Diseases (ISSN:13446304)
巻号頁・発行日
vol.69, no.5, pp.424-425, 2016 (Released:2016-09-21)
参考文献数
10
被引用文献数
2 2

Cat scratch disease (CSD) is an infectious disease caused by Bartonella henselae. Atypical clinical presentations of CSD include prolonged fever and multiple hepatosplenic lesions. Furthermore, multiple renal lesions are extremely rare in CSD. An 11-year-old Japanese girl presented at our hospital with a prolonged fever of unknown cause after being scratched and bitten by a kitten. Abdominal computed tomography (CT) revealed multiple small, round hypodense lesions in both kidneys and the spleen. Based on her history and the CT results, her diagnosis was CSD. The diagnosis was confirmed by serological tests, which indicated antibodies against B. henselae. After treatment with azithromycin, her fever immediately improved. Careful history taking and imaging are essential for the diagnosis of atypical CSD. In CT images, not only hepatosplenic lesions but also renal lesions are important features indicative of a diagnosis of atypical CSD. Subsequently, a diagnosis of CSD can be confirmed by specific serological tests. This is the first reported Japanese case of multiple renal and splenic lesions in a patient with CSD. Although difficult to diagnose, an early diagnosis atypical CSD and appropriate treatment are important to prevent complications and the need for invasive examinations.
著者
Yuto Amano Hiroshi Honda Ryusuke Sawada Yuko Nukada Masayuki Yamane Naohiro Ikeda Osamu Morita Yoshihiro Yamanishi
出版者
The Japanese Society of Toxicology
雑誌
The Journal of Toxicological Sciences (ISSN:03881350)
巻号頁・発行日
vol.45, no.3, pp.137-149, 2020 (Released:2020-03-06)
参考文献数
47

In silico models for predicting chemical-induced side effects have become increasingly important for the development of pharmaceuticals and functional food products. However, existing predictive models have difficulty in estimating the mechanisms of side effects in terms of molecular targets or they do not cover the wide range of pharmacological targets. In the present study, we constructed novel in silico models to predict chemical-induced side effects and estimate the underlying mechanisms with high general versatility by integrating the comprehensive prediction of potential chemical-protein interactions (CPIs) with machine learning. First, the potential CPIs were comprehensively estimated by chemometrics based on the known CPI data (1,179,848 interactions involving 3,905 proteins and 824,143 chemicals). Second, the predictive models for 61 side effects in the cardiovascular system (CVS), gastrointestinal system (GIS), and central nervous system (CNS) were constructed by sparsity-induced classifiers based on the known and potential CPI data. The cross validation experiments showed that the proposed CPI-based models had a higher or comparable performance than the traditional chemical structure-based models. Moreover, our enrichment analysis indicated that the highly weighted proteins derived from predictive models could be involved in the corresponding functions of the side effects. For example, in CVS, the carcinogenesis-related pathways (e.g., prostate cancer, PI3K-Akt signal pathway), which were recently reported to be involved in cardiovascular side effects, were enriched. Therefore, our predictive models are biologically valid and would be useful for predicting side effects and novel potential underlying mechanisms of chemical-induced side effects.