Publications by authors named "Ameya Jijo"
Conventional Insemination (CI) and Intra-Cytoplasmic Sperm Injection (ICSI) are routinely used insemination methods in clinical Assisted Reproductive Technologies (ART) settings. However, the existing data on the developmental competence and implantation potential of CI and ICSI derived embryos are not unequivocal. This prospective study on 23 patients undergoing ART treatment explored whether the secretomes of CI- and ICSI-derived embryo differentially alter the expression of integrins (α and β integrin) and MUCIN-1 (MUC-1) in a human endometrial epithelial cell line (Ishikawa).
View Article and Find Full Text PDFBackground: The unique epigenetic architecture that sperm cells acquire during spermiogenesis by retaining <15% of either canonical or variant histone proteins in their genome is essential for normal embryogenesis. Whilst heterogeneous levels of retained histones are found in morphologically normal spermatozoa, their effect on reproductive outcomes is not fully understood.
Methods: Processed spermatozoa (n = 62) were tested for DNA integrity by sperm chromatin dispersion assay, and retained histones were extracted and subjected to dot-blot analysis.
Context: The clinical value of human sperm metabolites has not been established due to the technical complexity in detecting these metabolites when sperm numbers are low.
Aims: To detect endogenous intracellular metabolites in fresh and post-thaw human spermatozoa using 800MHz nuclear magnetic resonance (NMR) spectroscopy equipped with a 1.7-mm cryo-probe.
Article Synopsis
- ICSI is a special method used to help men who have trouble having babies, but now it's being used a lot even when men aren't the problem, which is a bit too much.
- This study looked at embryos created by both IVF and ICSI from couples who didn’t have male fertility issues to see if they grow differently.
- The researchers found that after 96 hours, the embryos didn't show big differences in their chemical makeup, meaning ICSI doesn't change how the embryos develop compared to IVF in these cases.
Article Synopsis
- This study explored the integration of metabolomic and embryologic data with machine learning (ML) to enhance the prediction of embryo implantation success in infertile couples.
- The research involved 56 couples undergoing single blastocyst transfer, analyzing spent culture medium (SCM) to measure metabolite levels using NMR spectroscopy.
- Findings showed that lower levels of certain metabolites like pyruvate and threonine were linked to successful implantations, and a custom ML model achieved 100% accuracy in predicting embryo implantation potential, suggesting practical benefits for patient outcomes.