The female reproductive system represents a highly complex regulatory network governing critical physiological functions, encompassing reproductive capacity and endocrine regulation that maintains female physiological homeostasis. The in vitro simulation system provides a novel tool for biomedical research and can be used as physiological and pathological models to study the female reproductive system. Recent advances in this technology have evolved from 2D and 3D printing to organ-on-a-chip (OOC) and microfluidic systems, which has emerged as a transformative platform for modeling the female reproductive system. These microphysiological systems integrate microfluidics, 3D cell culture, and biomimetic scaffolds to replicate key functional aspects of reproductive organs and tissues. They have enabled precise simulation of hormonal regulation, embryo-endometrium interactions, and disease mechanisms such as endometriosis and gynecologic cancers. This review highlights the current state of female reproductive OOCs, including ovary-, uterus-, and fallopian tube-on-a-chip system, their applications in assisted reproduction and disease modeling, and the technological hurdles to their widespread application. Though significant barriers remain in scaling OOCs for high-throughput drug screening, standardizing protocols for clinical applications, and validating their predictive value against human patient outcomes, OOCs have emerged as a transformative platform to model complex pathologies, offering unprecedented insights into disease mechanisms and personalized therapeutic interventions. Future directions, including multi-organ integration for systemic reproductive modeling, incorporation of microbiome interactions, and clinical translation for mechanisms of drug action, will facilitate unprecedented insights into reproductive physiology and pathology.

This study examines the noise and biases introduced by technical factors in single-cell RNA sequencing (scRNA-seq) data, presenting a thorough benchmarking analysis of six widely utilized normalization methods. The evaluation of these methods is conducted from three perspectives: cell clustering, differential expression analysis, and computational resource requirements, utilizing seven real datasets alongside four simulated datasets. The findings indicate that Dino excels in clustering 10 × datasets and those with a substantial number of cells, while scTransform demonstrates strong performance with datasets produced through full-length library preparation protocols. Additionally, SCnorm is identified as suitable for small-scale datasets. This research serves as a significant reference for scholars in selecting appropriate normalization tools, thereby enhancing the accuracy and reliability of subsequent analyses of scRNA-seq data.

The important clinical significance of cfDNA: as a non-invasive biomarker, cfDNA is used in cancer detection, prenatal screening, and transplant monitoring, providing insights for precision medicine.