Pregnancy affects almost every organ and system, including the microorganisms that reside in the gastrointestinal tract, the gut microbiota. Pregnancy hormones, such as progesterone and estradiol, alter the composition of the maternal gut microbiota, both directly and indirectly; in turn, gastrointestinal microorganisms can produce and modulate the activity of reproductive hormones. While the levels of pregnancy hormones depend on the fetal sex, there is limited research on the role of fetal sex in the maternal gut microbiota composition and function. Using publicly available microbial sequencing data from a cohort of 485 pregnant women in Guangzhou, China, we identified taxonomical and predicted metabolic signatures associated with fetal sex and gestational age using zero-inflated negative binomial models and linear models, respectively, correcting for pre-pregnancy BMI, maternal age, parity, and residency status. We trained and tested three different machine learning models, XGBoost, Random Forest, and Elastic Net-Logistic Regression, to predict fetal sex based on maternal microbial characteristics. Further, we generated sex-specific microbial co-abundance networks using SPIEC-EASI. We identified multiple microbial species and predicted microbial enzymes that were significantly differentially abundant between the fetal sex groups, including increased levels of Bifidobacterium breve and the predicted microbial enzyme β-glucuronidase in female pregnancies, but these sex-associated microbial features represented only a weak signal to predict fetal sex (AUC < 0.6 for all explored machine learning models). Maternal microbial co-abundance networks from pregnancies carrying male or female fetuses exhibited a similar core community, yet those from female pregnancies were more densely connected than those from male pregnancies. Several of the highly connected species of the maternal microbiota from individuals carrying female pregnancies were microorganisms with previously reported β-glucuronidase activity. Our study demonstrated that the maternal gut microbiota differs between female and male pregnancies and that these differences depend on gestational age. Our results not only point to the need to include fetal sex when studying the perinatal period but also suggest a novel, data-driven mechanistic hypothesis whereby fetal sex-specific maternal-placental-fetal interactions influence hormonal regulation, which may in turn, interact bidirectionally with maternal gut microbiota composition. Given the essential role of the gut microbiota in maternal health and infant development, a better understanding of how maternal microbial communities vary in response to fetal sex during pregnancy may provide important insights into maternal health and fetal outcomes.
Neurofibromatosis-Noonan syndrome (NFNS) is an ultra-rare phenotype associated predominantly with pathogenic variants in the NF1 gene. Male reproductive complications in affected individuals have long been largely overlooked, and no systematic review has yet focused specifically on male reproductive impairment in this population. This systematic review was performed in accordance with PRISMA 2020 guidelines, and the review protocol was prospectively registered in PROSPERO (CRD420261465574). PubMed and Web of Science were searched from inception to March 2026, and 26 eligible original clinical studies were included after screening 219 records. Twenty-six eligible clinical studies were included. Cryptorchidism was the most consistently reported male reproductive abnormality, although reporting of testicular status was incomplete across studies. In the primary descriptive analysis, 5 of 26 male records with explicitly documented testicular status had cryptorchidism (19.2
To determine whether sibling blastulation efficiency calculated after excluding the transferred blastocyst was associated with live birth after the first frozen-thawed single-blastocyst transfer. This retrospective cohort study included 431 transfer cycles performed between 2022 and 2024. Sibling blastulation efficiency was calculated as the number of blastocysts formed minus one divided by the number of embryos cultured for blastocyst formation minus one. The primary outcome was live birth. Adjusted risk ratios were estimated using modified Poisson regression with robust variance. Missing covariate data were handled by multiple imputation. Live birth occurred in 192 cycles. Each 1-standard-deviation increase in sibling blastulation efficiency was associated with a higher probability of live birth after adjustment for patient characteristics, ovarian response, embryo cohort size, transferred-blastocyst morphology and transfer-related factors (adjusted risk ratio 1.14, 95
Artificial intelligence (AI) is increasingly being investigated across reproductive medicine, including semen analysis, sperm detection and selection, oocyte and embryo assessment, prediction of assisted reproductive technology outcomes, clinical decision support, and integration of multi-omics data. Large language models and generative AI are also emerging as tools for scientific writing, education, documentation, and research support. This review critically examines the current applications, clinical readiness, and research-integrity implications of AI in reproductive medicine. Although many models demonstrate promising performance in classification, ranking, or outcome prediction, the available evidence remains predominantly retrospective, task-specific, and derived from single-center or internally validated datasets. High technical performance does not necessarily translate into improved cumulative live birth, patient safety, cost-effectiveness, workflow efficiency, or equitable clinical outcomes. Important barriers include dataset heterogeneity, limited external and prospective validation, inadequate calibration assessment, data leakage, algorithmic bias, limited interpretability, privacy and cybersecurity risks, and vulnerability to performance drift. Generative AI introduces additional concerns, including fabricated references, inaccurate scientific summaries, undisclosed contributions, confidentiality breaches, and the generation or alteration of images that may be misrepresented as empirical evidence. Responsible translation therefore requires transparent reporting, independent temporal and geographic validation, subgroup and fairness analyses, prospective clinical-impact evaluation, preservation of data and model provenance, and continuous post-deployment surveillance. Current international guidance further emphasizes that AI systems cannot qualify as authors and that accountable human researchers must verify generated content and disclose material AI use. AI should presently be regarded as an assistive technology that augments, rather than replaces, embryologists, clinicians, and researchers. Future progress will depend on multicenter prospective studies using patient-important outcomes, design-appropriate reporting frameworks, risk-based regulatory oversight, and clearly defined human accountability throughout the AI lifecycle.
