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1 Ergebnisse
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Predicting preterm birth through vaginal microbiota, cervic..:
Sunwha Park
;
Jeongsup Moon
;
Nayeon Kang
...
https://www.frontiersin.org/articles/10.3389/fmicb.2022.912853/full. , 2022
Link:
https://doi.org/10.3389/fmicb.2022.912853
RT Journal T1 <Title>Predicting preterm birth through vaginal microbiota, cervical length, and WBC using a machine learning model</Title> UL https://suche.suub.uni-bremen.de/peid=base-ftdoajarticles:oai:doaj.org_article:b5da21b38fa34b1f8309365326ab7dd4&Exemplar=1&LAN=DE A1 Sunwha Park A1 Jeongsup Moon A1 Nayeon Kang A1 Young-Han Kim A1 Young-Ah You A1 Eunjin Kwon A1 AbuZar Ansari A1 Young Min Hur A1 Taesung Park A1 Young Ju Kim PB Frontiers Media S.A. YR 2022 K1 preterm birth K1 vaginal microbiome K1 pregnancy K1 16s ribosomal RNA metagenome sequencing K1 cervicovaginal fluid K1 machine learning K1 Microbiology K1 QR1-502 JF https://www.frontiersin.org/articles/10.3389/fmicb.2022.912853/full LK http://dx.doi.org/https://doi.org/10.3389/fmicb.2022.912853 DO https://doi.org/10.3389/fmicb.2022.912853 SF ELIB - SuUB Bremen
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