Warning: fopen(/home/virtual/enm-kes/journal/upload/ip_log/ip_log_2024-04.txt): failed to open stream: Permission denied in /home/virtual/lib/view_data.php on line 88 Warning: fwrite() expects parameter 1 to be resource, boolean given in /home/virtual/lib/view_data.php on line 89 Corrigendum: Correction of Acknowledgments. Association between Serum Fibroblast Growth Factor 21 Levels and Bone Mineral Density in Postmenopausal Women
Skip Navigation
Skip to contents

Endocrinol Metab : Endocrinology and Metabolism

clarivate
OPEN ACCESS
SEARCH
Search
Advanced Search
mobile menu button

Articles

Page Path
HOME > Endocrinol Metab > Volume 33(3); 2018 > Article
Corrigendum
Corrigendum: Correction of Acknowledgments. Association between Serum Fibroblast Growth Factor 21 Levels and Bone Mineral Density in Postmenopausal Women
Hoon Sung Choi1, Hyang Ah Lee2, Sang-Wook Kim1, Eun-Hee Cho1
Endocrinology and Metabolism 2018;33(3):428.
DOI: https://doi.org/10.3803/EnM.2018.33.3.428
Published online: August 14, 2018

1Department of Internal Medicine, Kangwon National University School of Medicine, Chuncheon, Korea.

2Department of Obstetrics and Gynecology, Kangwon National University School of Medicine, Chuncheon, Korea.

Corresponding author: Eun-Hee Cho. Department of Internal Medicine, Kangwon National University Hospital, Kangwon National University School of Medicine, 156 Baengnyeong-ro, Chuncheon 24289, Korea. Tel: +82-33-258-9167, Fax: +82-33-258-2455, ehcho@kangwon.ac.kr

Copyright © 2018 Korean Endocrine Society

This is an Open Access article distributed under the terms of the Creative Commons Attribution Non-Commercial License (http://creativecommons.org/licenses/by-nc/4.0/) which permits unrestricted non-commercial use, distribution, and reproduction in any medium, provided the original work is properly cited.

  • 2,880 Views
  • 37 Download
  • 1 Crossref
Full Article

Download PDF Download PDF

Endocrinol Metab 2018;33:273–277 https://doi.org/10.3803/EnM.2018.33.2.273
In our recently published article, there were some wrong contents in the Acknowledgments section, which should be properly revised as follows:
ACKNOWLEDGMENTS
This research was supported by research fund of Kangwon Branch of Korean Endocrine Society (2018).
We would like to apologize for any inconvenience or misunderstanding.

Figure & Data

References

    Citations

    Citations to this article as recorded by  
    • A novel residual graph convolution deep learning model for short-term network-based traffic forecasting
      Yang Zhang, Tao Cheng, Yibin Ren, Kun Xie
      International Journal of Geographical Information Science.2020; 34(5): 969.     CrossRef

    Endocrinol Metab : Endocrinology and Metabolism
    © Korean Endocrine Society.