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Computer-assisted prediction of atherosclerotic intimal thickness based on weight of adrenal gland, interleukin-6 concentration, and neural networks.
Meng, Ling-Bing; Zou, Yang-Fan; Shan, Meng-Jie; Zhang, Meng; Qi, Ruo-Mei; Yu, Ze-Mou; Guo, Peng; Zheng, Qian-Wei; Gong, Tao.
Affiliation
  • Meng LB; Neurology Department, Beijing Hospital, National Center of Gerontology, Beijing, P.R. China.
  • Zou YF; *These authors contributed equally to this work.
  • Shan MJ; Department of Neurosurgery, Chinese PLA General Hospital-Sixth Medical Center, Beijing, P.R. China.
  • Zhang M; *These authors contributed equally to this work.
  • Qi RM; MOH Key Laboratory of Systems Biology of Pathogens, Institute of Pathogen Biology, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing, P.R. China.
  • Yu ZM; School of Energy Power and Mechanical Engineering, North China Electric Power University, Baoding, Hebei, P.R. China.
  • Guo P; MOH Key Laboratory of Geriatrics, Beijing Hospital, National Center of Gerontology, Beijing, P.R. China.
  • Zheng QW; Department of Neurology, Peking University First Hospital, Beijing, P. R. China.
  • Gong T; Department of Orthopedics, The Fourth Hospital of Hebei Medical University, Shijiazhuang, Hebei, P.R. China.
J Int Med Res ; 48(1): 300060519839625, 2020 Jan.
Article in En | MEDLINE | ID: mdl-31039661
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Full text: 1 Collection: 01-internacional Database: MEDLINE Main subject: Interleukin-6 / Neural Networks, Computer / Adrenal Glands / Atherosclerosis / Carotid Intima-Media Thickness Type of study: Etiology_studies / Prognostic_studies / Risk_factors_studies Limits: Animals / Humans Language: En Journal: J Int Med Res Year: 2020 Document type: Article
Fulltext
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Full text: 1 Collection: 01-internacional Database: MEDLINE Main subject: Interleukin-6 / Neural Networks, Computer / Adrenal Glands / Atherosclerosis / Carotid Intima-Media Thickness Type of study: Etiology_studies / Prognostic_studies / Risk_factors_studies Limits: Animals / Humans Language: En Journal: J Int Med Res Year: 2020 Document type: Article