Biomedical subjects

Jie Qiao

Publications and source records attributed to Jie Qiao.

2 recordsLinked to original sources

PubMed2026

Epigenetic safety of in vitro maturation in PCOS: genome-wide DNA methylation profiling of cord blood from a randomized controlled trial.

BACKGROUND: In vitro maturation (IVM) provides a safer alternative to conventional in vitro fertilization (IVF) for women with polycystic ovary syndrome (PCOS) by mitigating the risk of ovarian hyperstimulation. However, concerns persist regarding whether IVM perturbs epigenetic reprogramming in the offspring. Current evidence is constrained by candidate-gene approaches or a lack of parental controls. This study aimed to evaluate the genome-wide DNA methylation safety of IVM compared with conventional IVF using a rigorous trio-based design. METHODS: This secondary epigenetic analysis was nested within a randomized controlled trial (RCT) (ClinicalTrials.gov: NCT03463772). We included 10 nuclear families (trios), comprising five IVM-conceived and five IVF-conceived singleton offspring alongside their biological parents. Both groups utilized a uniform freeze-only single-blastocyst transfer strategy to minimize hormonal confounding. Genomic DNA from umbilical cord blood (UCB) and parental peripheral blood was analyzed using reduced representation bisulfite sequencing (RRBS). Genome-wide methylation patterns and differentially methylated regions (DMRs) were subsequently compared between the groups. RESULTS: Clinical characteristics were comparable between the IVM and IVF groups. Genome-wide analyses demonstrated high concordance in UCB methylation patterns, revealing no significant differences in global CpG methylation levels or distributions across key genomic features (promoters, CpG islands, and gene bodies). Only three rare DMRs were identified in UCB (representing ~ 0.0001% of the genome), none of which mapped to imprinted or developmentally critical loci. Furthermore, methylation variability remained consistent between the groups. CONCLUSIONS: Our findings provide robust mechanistic evidence supporting the epigenetic safety of IVM. The remarkable stability of the neonatal methylome confirms that specific IVM conditions do not compromise early developmental programming, thereby endorsing IVM as a safe and viable alternative for women with PCOS. TRIAL REGISTRATION: ClinicalTrials.gov registry, NCT03463772. Registered on March 13, 2018.

Humans↗

PubMed2025

Development and validation of a deep learning model based on cascade mask regional convolutional neural network to noninvasively and accurately identify human round spermatids.

INTRODUCTION: The difficulty of identifying human round spermatids (hRSs) has impeded applications of the human round spermatid injection (ROSI) technique. RSs can be accurately screened through flow cytometric analysis utilizing the Hoechst fluorescence profile reflecting DNA, but this method is not suitable for isolating hRSs due to the toxicity associated with Hoechst staining. OBJECTIVE: To evaluate the capacity of a deep learning model grounded in a cascade mask region-based convolutional neural network (R-CNN) for the noninvasive and accurate identification of hRSs. METHODS: In this study, we presented the development and validation of a deep learning model for identifying hRSs through the analysis of 3457 optical light microscope images of sorted hRSs obtained via flow cytometric analysis. The model's accuracy and specificity were evaluated by calculating the mean average precision (mAP). Furthermore, a double-blind experiment was conducted to access the reliability of the proposed model in accurately identifying hRSs. It detected the expression of protamine (PRM1) and/or peanut lectin (PNA), which are established markers for RSs. RESULTS: Our deep learning-based model demonstrated a high precision, achieving a mAP of over 0.80 for isolating hRSs in test datasets. The expression of PRM1 and/or PNA was observed in all cells noninvasively selected by our AI model during an independent double-blind test. This phenomenon confirmed the accuracy and effectiveness of the proposed model. The model's capability for noninvasive and accurate isolation of hRSs among spermatogenic cells highlighted its robustness and generalizability for clinical applications. CONCLUSION: The deep learning AI model based on a cascade R-CNN has the ability to accurately identify hRSs among spermatogenic cells. The application of this noninvasive method, which requires no additional procedures in clinical practice, is able to facilitate the widespread implementation of ROSI technique. Therefore, it can provide patients with spermatogenic arrest the opportunity to become biological fathers.

Humans↗