Measurement of SARS-CoV-2 Antibody Titers Improves the Prediction Accuracy of COVID-19 Maximum Severity by Machine Learning in Non-Vaccinated Patients.

MLA

Kurano, Makoto, et al. “Measurement of SARS-CoV-2 Antibody Titers Improves the Prediction Accuracy of COVID-19 Maximum Severity by Machine Learning in Non-Vaccinated Patients.” Frontiers in Immunology, vol. 13, Jan. 2022, pp. 1–13. EBSCOhost, https://doi.org/10.3389/fimmu.2022.811952.

APA

Kurano, M., Ohmiya, H., Kishi, Y., Okada, J., Nakano, Y., Yokoyama, R., Qian, C., Xia, F., He, F., Zheng, L., Yu, Y., Jubishi, D., Okamoto, K., Moriya, K., Kodama, T., & Yatomi, Y. (2022). Measurement of SARS-CoV-2 Antibody Titers Improves the Prediction Accuracy of COVID-19 Maximum Severity by Machine Learning in Non-Vaccinated Patients. Frontiers in Immunology, 13, 1–13. https://doi.org/10.3389/fimmu.2022.811952

Chicago

Kurano, Makoto, Hiroko Ohmiya, Yoshiro Kishi, Jun Okada, Yuki Nakano, Rin Yokoyama, Chungen Qian, et al. 2022. “Measurement of SARS-CoV-2 Antibody Titers Improves the Prediction Accuracy of COVID-19 Maximum Severity by Machine Learning in Non-Vaccinated Patients.” Frontiers in Immunology 13 (January): 1–13. doi:10.3389/fimmu.2022.811952.