Mihye Park

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48 Article
2 Comment/debate
2 Review article

Exposure to traffic-related air pollution and the risk of premature rupture of membranes: a cohort analysis of the Korean CHildren’s ENvironmental health Study (Ko-CHENS)
the Ko-CHENS Study Group, Apr 2026, In: Environmental Science and Pollution Research. 33, 13, p. 5914-5926 13 p.
Research output: Contribution to journal › Article › peer-review

Open Access
A Comparative Longitudinal Study Analyzing Vaginal Microbiota Differences Between Term and Preterm Pregnancies in Korean Women
Nam, G., Lee, K. A., Kim, S. J., Oh, K. Y., Lee, S., Lee, H. C., Kim, S. Y. & Park, M. H., Apr 2025, In: Medicina (Lithuania). 61, 4, 752.
Research output: Contribution to journal › Article › peer-review

Open Access
2 Scopus citations
Associations of pre- and postnatal levels of three heavy metals with visual impairment in 3-year-old children
Ham, D. J., Kim, S. Y., Bae, S., Kim, S., Park, C., Song, S., Lee, J., Park, H., Jo, J. H., Jung, A. R., Yu, S. D., Kim, H. J., Jung, S. W., Hong, S., Seo, H. W., Hwang, N., Kang, T. S., Jeong, D. J., Lee, S. & Ha, E. & 61 others, Kim, Y. J., Shah, S., Oh, J., Kim, H. C., Jeong, K. S., Kim, B., Lee, S., Kim, H., Kook, H., Baek, H. J., Moon, J. D., Park, W. J., Shin, M. G., Ha, M., Paik, K. C., Kwon, H. J., Lim, M. H., Yoo, S. J., Kim, J. K., Bae, J., Hong, Y. S., Kim, Y. M., Son, H., Park, M. H., Lim, D. H., Kim, J. H., Hong, S. C., Lee, K. H., Kim, S. Y., Kim, W. J., Na, S., Lim, M. N., Yoo, Y., Song, D. J., Lee, J. S., Yoon, W., Choe, S. A., Kim, S., Choung, J. T., Kim, S., Kim, J., Ahn, K., Hong, Y. C., Choi, B. Y., Ryoo, S. W., Son, S. Y., Lee, J. H., Lee, D. W., Woo, K. H., Yoon, S. Y., Hur, J., Choi, K. H., Park, M. S., Lee, S. H., Chu, Y., Kim, J. A., Kim, Y., Lee, J., Sim, C. S., Kim, A. R. & Oh, I., 15 Dec 2025, In: Environmental Research. 287, 123003.
Research output: Contribution to journal › Article › peer-review
1 Scopus citations
Maternal Uterine Artery Doppler and Serum Marker in the First Trimester as Predictive Markers for Small for Gestational Age Neonates and Preeclampsia: A Pilot Study
Lee, J. Y., Lee, K. A., Park, S. Y., Kim, S. J., Shim, S. Y., Kim, Y. J. & Park, M. H., Jan 2025, In: Diagnostics. 15, 2, 233.
Research output: Contribution to journal › Article › peer-review

Open Access
2 Scopus citations
Pregnant women's lifestyles and exposure to endocrine-disrupting chemicals: A machine learning approach
Ko-CHENS Study group, 1 Feb 2025, In: Environmental Pollution. 366, 125309.
Research output: Contribution to journal › Article › peer-review
8 Scopus citations

Mihye Park

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Collaborations and top research areas from the last five years

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Exposure to traffic-related air pollution and the risk of premature rupture of membranes: a cohort analysis of the Korean CHildren’s ENvironmental health Study (Ko-CHENS)

A Comparative Longitudinal Study Analyzing Vaginal Microbiota Differences Between Term and Preterm Pregnancies in Korean Women

Associations of pre- and postnatal levels of three heavy metals with visual impairment in 3-year-old children

Maternal Uterine Artery Doppler and Serum Marker in the First Trimester as Predictive Markers for Small for Gestational Age Neonates and Preeclampsia: A Pilot Study

Pregnant women's lifestyles and exposure to endocrine-disrupting chemicals: A machine learning approach

Mihye Park

Fingerprint

Collaborations and top research areas from the last five years

Research output

Exposure to traffic-related air pollution and the risk of premature rupture of membranes: a cohort analysis of the Korean CHildren’s ENvironmental health Study (Ko-CHENS)

A Comparative Longitudinal Study Analyzing Vaginal Microbiota Differences Between Term and Preterm Pregnancies in Korean Women

Associations of pre- and postnatal levels of three heavy metals with visual impairment in 3-year-old children

Maternal Uterine Artery Doppler and Serum Marker in the First Trimester as Predictive Markers for Small for Gestational Age Neonates and Preeclampsia: A Pilot Study

Pregnant women's lifestyles and exposure to endocrine-disrupting chemicals: A machine learning approach