The DOI matches its Crossref record; 78 of the 78 references it cites were found in a registry or an index.
The vaginal microbiota associates with the regression of untreated cervical intraepithelial neoplasia 2 lesions
Open accessAbstract
Emerging evidence suggests associations between the vaginal microbiota (VMB) composition, human papillomavirus (HPV) infection, and cervical intraepithelial neoplasia (CIN); however, causal inference remains uncertain. Here, we use bacterial DNA sequencing from serially collected vaginal samples from a cohort of 87 adolescent and young women aged 16–26 years with histologically confirmed, untreated CIN2 lesions to determine whether VMB composition affects rates of regression over 24 months. We show that women with a Lactobacillus-dominant microbiome at baseline are more likely to have regressive disease at 12 months. Lactobacillus spp. depletion and presence of specific anaerobic taxa including Megasphaera, Prevotella timonensis and Gardnerella vaginalis are associated with CIN2 persistence and slower regression. These findings suggest that VMB composition may be a future useful biomarker in predicting disease outcome and tailoring surveillance, whilst it may offer rational targets for the development of new prevention and treatment strategies.
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Metadata
- Authors
- Mitra, A., MacIntyre, D. A., Ntritsos, G., Smith, A., Tsilidis, K. K., Marchesi, J. R., Bennett, P. R., Moscicki, A.-B., & Kyrgiou, M.
- Venue
- Nature Communications
- Published
- 2020-04-24
- Volume
- 11
- Issue
- 1
- Pages
- 1999-1999
- ISSN
- 2041-1723 (ISSN-L)
- OpenAlex
- W3018767052
- Cited by
- 257
- References
- 78
- FWCI
- 18.25
- Type
- journal article
- Language
- en
- Open access
- gold
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- Mitra, A., MacIntyre, D. A., Ntritsos, G., Smith, A., Tsilidis, K. K., Marchesi, J. R., Bennett, P. R., Moscicki, A.-B., & Kyrgiou, M. (2020). The vaginal microbiota associates with the regression of untreated cervical intraepithelial neoplasia 2 lesions. Nature Communications. https://doi.org/10.1038/s41467-020-15856-y
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Contains information from the Semantic Scholar Academic Graph Datasets, made available under the ODC Attribution License (ODC-BY 1.0).
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