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Prediction models for neutralization activity against emerging SARS-CoV-2 variants: A cross-sectional study.
Goto, Atsushi; Miyakawa, Kei; Nakayama, Izumi; Yagome, Susumu; Xu, Juan; Kaneko, Makoto; Ohtake, Norihisa; Kato, Hideaki; Ryo, Akihide.
  • Goto A; Department of Public Health, School of Medicine, Yokohama City University, Yokohama, Japan.
  • Miyakawa K; Department of Health Data Science, Graduate School of Data Science, Yokohama City University, Yokohama, Japan.
  • Nakayama I; Department of Microbiology, Graduate School of Medicine, Yokohama City University, Yokohama, Japan.
  • Yagome S; Center for Influenza and Respiratory Virus Research, National Institute of Infectious Diseases, Musashimurayama, Japan.
  • Xu J; Department of Public Health, School of Medicine, Yokohama City University, Yokohama, Japan.
  • Kaneko M; Department of Health Data Science, Graduate School of Data Science, Yokohama City University, Yokohama, Japan.
  • Ohtake N; Integrity Healthcare Co., Ltd., Tokyo, Japan.
  • Kato H; Department of Endocrinology and Metabolism, Graduate School of Medicine, Yokohama City University, Yokohama, Japan.
  • Ryo A; Department of Health Data Science, Graduate School of Data Science, Yokohama City University, Yokohama, Japan.
Front Microbiol ; 14: 1126527, 2023.
Article en En | MEDLINE | ID: mdl-37113226
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Texto completo: 1 Banco de datos: MEDLINE Tipo de estudio: Observational_studies / Prevalence_studies / Prognostic_studies / Risk_factors_studies Idioma: En Año: 2023 Tipo del documento: Article
Texto completo
Imprimir
XML
PubMed Links
Search on Google

Texto completo: 1 Banco de datos: MEDLINE Tipo de estudio: Observational_studies / Prevalence_studies / Prognostic_studies / Risk_factors_studies Idioma: En Año: 2023 Tipo del documento: Article