AIMC Topic: Endometriosis

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Integrating inflammatory biomarkers and demographic variables with machine learning to predict endometriosis risk.

Scientific reports
This study explores the relationship between inflammatory biomarkers and the risk of endometriosis, aiming to develop a predictive model using National Health and Nutrition Examination Survey (1999-2006) data. The dataset included 4,089 females with ...

Identified endoplasmic reticulum stress-related molecular cluster and immune characterization in endometriosis.

Scientific reports
Endometriosis is a common disease among women of childbearing age, and endoplasmic reticulum stress (ERS), a response involved in regulating protein homeostasis, has been linked to its pathogenesis. To identify ERS-related hub genes, this study seque...

Integrative transcriptomic analysis identifies shared EndMT-related gene signatures in endometriosis and recurrent miscarriage.

Scientific reports
Endometriosis (EMs) and recurrent miscarriage (RM) represent major reproductive health challenges. This study investigates the involvement of endothelial-mesenchymal transition (EndMT) in these conditions through integrative bioinformatics analysis, ...

Serum Fingerprinting-Based Integrative Dual-Omics Machine Learning for Endometriosis-Associated Ovarian Cancer.

Analytical chemistry
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 ...

SPP1 as a key modulator of M2 macrophage polarization promotes endometriosis progression via activation of the FAK/PI3K/AKT pathway: A bioinformatics and experimental study.

International immunopharmacology
Endometriosis (EMs) is a gynecological disorder characterized by chronic inflammation and an aberrant immune microenvironment. In this study, we integrated the GSE6364 dataset from the GEO database to identify differentially expressed genes, and appl...

Machine learning prediction of clinical pregnancy in endometriosis patients following fresh IVF/ICSI-ET.

European journal of medical research
BACKGROUND: Fresh embryo transfer reduces waiting time and minimizes embryo cryodamage for endometriosis (EM) patients. The current prediction models for fresh embryo transfer outcomes in EM primarily rely on logistic regression, with limited applica...

A Multi-Modal Pelvic MRI Dataset for Deep Learning-Based Pelvic Organ Segmentation in Endometriosis.

Scientific data
Endometriosis affects approximately 190 million females of reproductive age worldwide. Magnetic Resonance Imaging (MRI) has been recommended as the primary non-invasive diagnostic method for endometriosis. This study presents new female pelvic MRI mu...

Screening for endometriosis: A scoping review of screening measures that could support early diagnosis.

BMC women's health
BACKGROUND: Endometriosis is prevalent in approximately 6-10% of all women of reproductive age and is associated with pelvic pain, heavy menstrual bleeding, infertility, and pain during intercourse. Despite reporting symptoms, women wait around 11 ye...

WISP2/CCN5 revealed as a potential diagnostic biomarker for endometriosis based on machine learning and single-cell transcriptomic analysis.

Functional & integrative genomics
OBJECTIVE: Endometriosis is a prevalent gynecological disease characterized by the ectopic growth of functional endometrial tissue outside the uterine cavity, affecting millions of women worldwide. Currently, the definitive diagnosis relies on invasi...

Hybrid Neural network and machine learning models with improved optimization method for gut microbiome effects on the sleep quality in patients with endometriosis.

Computer methods and programs in biomedicine
BACKGROUND AND OBJECTIVE: Endometriosis is a chronic gynecological condition known to affect the quality of life of millions of women globally, often manifesting with symptoms that impact sleep quality. Emerging evidence suggests a crucial role of th...