This study aims to characterize the expression pattern and biological functions of miR-433-3p in endometriosis. Eutopic and ectopic endometrial tissues were collected from endometriosis patients, and the expression level of …
Background: Endometriosis affects 10% of reproductive-aged women globally and is one of the major causes of infertility. It is a debilitating, chronic condition with an average diagnostic delay of up …
This study aims to explore the application value of stromal cell-derived factor (CXCL12), vascular endothelial growth factor (VEGF) and carbohydrate antigen 125 (CA125) in the diagnosis, staging, and clinical manifestations …
Predictive tools are lacking for pain-related outcomes after endometriosis surgery. The objective of this study was to develop and validate a machine learning-based clinical model to predict poor pain-related quality …
This study aimed to construct and validate a nomogram for predicting natural pregnancy after hysteroscopy and laparoscopy in patients with tubal infertility and pelvic endometriosis, providing a basis for precise …
Diagnosing ovarian lesions is challenging because of their heterogeneous clinical presentations. Some benign ovarian conditions, such as endometriosis, can have features that mimic cancer. We use optical-resolution photoacoustic microscopy (OR-PAM) …
Endometrial receptivity is essential for successful pregnancy, and endometriosis is widely recognized as a disruptor of this process. Poor endometrial receptivity is also a key factor contributing to recurrent implantation …
Endometriosis is a long-term health problem that affects a significant number of women globally. Among the various forms of endometriosis, ovarian endometriosis (OEM) is the most prevalent. This research aimed …
This study aims to identify angiogenesis-associated genes (AAGs) in endometriosis (EM) by integrating bioinformatics analysis with machine learning, and to investigate their underlying mechanisms. Differentially expressed genes (DEGs) were screened …
Dual-omics, by integrating molecular information from two distinct dimensions, can offer more comprehensive perspective for complex disease. Herein, we developed an efficient functionalized mesoporous nanoparticle-coupled laser desorption/ionization mass spectrometry (fMNPLDI-MS) …