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Small extracellular vesicles in follicular fluids for predicting reproductive outcomes in assisted reproductive technology. | LitMetric

AI Article Synopsis

Article Abstract

Background: Assisted reproductive technology accounts for an increasing proportion of infertility treatments, and assessments to predict clinical pregnancy outcomes are desired. Extracellular vesicles exist in follicular fluid, and small non coding RNAs in extracellular vesicles underline the possibility of reflecting pregnancy potential.

Methods: Follicular fluid samples are collected from 20 ovarian follicles of 15 infertile patients undergoing assisted reproductive technology. Extracellular vesicles are isolated by serial centrifugation and small RNA sequencing is performed to investigate the profiles of microRNAs and P-element-induced wimpy testis-interacting RNAs.

Results: Small extracellular vesicles with a size range of approximately 100 nm are successfully isolated, and the small non coding RNA profiles of pregnant samples (n = 8) are different from those of non-pregnant samples (n = 12). Fourteen dysregulated small non coding RNAs are selected to identify the independent candidates [mean read count >100, area under the curve >0.8]. Among them, we find that a specific combination of small non coding RNAs (miR-16-2-3p, miR-378a-3p, and miR-483-5p) can predict the pregnant samples more precisely using a receiver operating characteristics curves analysis (area under the curve: 0.96). Furthermore, even in the same patients, the three microRNAs are differentially expressed between pregnant and non-pregnant samples.

Conclusions: Our results demonstrate that small non coding RNAs derived from small extracellular vesicles in follicular fluid can be potential non-invasive biomarkers for predicting pregnancy, leading to their probable application in assisted reproductive technology. Further large-scale studies are required to validate the clinical usefulness of these small non coding RNAs.

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Source
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC10902298PMC
http://dx.doi.org/10.1038/s43856-024-00460-8DOI Listing

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