Obstetrics & Gynecology

Race/ethnicity, disability, and antenatal depression in the United States: population-level insights from machine learning.

OBJECTIVE: Women with intersecting identities, such as being both Black and disabled, face heightened risk of antenatal depression, yet few studies examine its nuanced mechanisms. To capture complex, interactive associations among risk factors, we applied explainable machine learning to predict antenatal depression and identify key predictors among non-Hispanic Black (NHB) and non-Hispanic White (...

Precise oxygen therapy to emphysema patients by fuzzy-based gain tuning control of set-point regulated MRAC.

Emphysema, a primary component of chronic obstructive pulmonary disease (COPD), causes progressive dyspnea through the destruction of alveolar membranes. This structural degradation reduces the available surface area for gas exchange, significantly impairing oxygen delivery to the bloodstream. While oxygen therapy is a critical intervention, the inherent physiological complexities, specifically tr...

6PPD-quinone promotes ovarian cancer progression: Insights from network toxicology, machine learning, and in vitro validation.

6PPD-quinone (6PPD-Q), a toxic tire-derived antioxidant transformation product, is a pervasive environmental contaminant linked to reproductive toxici...

Treatment of pulmonary fibrosis: From disease mechanisms to future novel therapies (Review).

Pulmonary fibrosis (PF) is a progressive and fatal interstitial lung disease characterized by irreversible lung scarring and frequently associated wit...

Prediction of surgery type for uterine fibroids using machine learning algorithms and hormone values.

This study aimed to develop and externally validate machine learning (ML)-based models to characterize surgical classification patterns between hyster...

Smartphone addiction and temporomandibular disorders among university students: A machine learning based multiple regression analysis study.

Temporomandibular disorders (TMD) involve complex interactions among behavioral and musculoskeletal factors, and machine learning (ML) has increasingl...

Radiomics-based ultrasOund Model for differentiating Uterine Sarcomas from leiomyomas (ROMUS): a retrospective pilot Multicenter Italian Trials in Ovarian Cancer (MITO) study.

OBJECTIVE: To develop machine-learning models that incorporate clinical information and radiomics features extracted from ultrasound images to disting...

Integrated weighted gene co-expression network analysis and machine learning analysis identifies SEC14L5 as a potential biomarker for polycystic ovary syndrome.

BACKGROUND AND AIMS: To identify novel biomarkers and therapeutic targets for polycystic ovary syndrome (PCOS) using integrated bioinformatics approac...

Imidazolium Lipid-based Nanoparticles Enable Effective mRNA Delivery and Cellular Immune Response.

Imidazolium LipidBrick® cationic lipid nanoparticles (LNPs) provide a pH-independent alternative to conventional ionizable systems for nucleic acid de...

From Antigen to Atlas: A Multi-omics, Single-Cell Pipeline for Discovering Safe and Effective CAR Targets in Ovarian Cancer.

Chimeric antigen receptor (CAR) T-cell therapy has achieved remarkable success in hematologic malignancies but continues to face significant barriers ...

Image segmentation of cervical grainy sandy patches lesions associated with female genital schistosomiasis using deep convolutional neural network with U-NET architecture.

Female genital schistosomiasis (FGS) is a neglected but highly prevalent disease in sub-Saharan Africa, caused by Schistosoma haematobium egg-induced ...

Machine Learning-Enhanced Microfluidic Impedance Platform for Rare Cell Analysis.

Rare cells, despite constituting only a small fraction of the population, play a critical role in health and disease. For instance, a minor subset of ...

Multivariable AI-based analysis of immune-lifestyle patterns associated with recurrent pregnancy loss: an exploratory retrospective study.

Introduction Approximately 1-5% of pregnant women experience recurrent pregnancy loss (RPL). Early detection and evaluation of high-risk variables all...

Machine-learning with multiomics for radiotherapy-induced acute rectal toxicities and survival outcome in patients with cervical cancer.

Differentiation of Benign and Malignant Cervical Lymph Nodes Using a Multi-Modal Ultrasound-Based Machine Learning Model with SHAP Interpretability.

OBJECTIVES: To evaluate the diagnostic value of a machine learning (ML) model based on multi-modal ultrasound features in differentiating benign from ...

Corrigendum to 'Machine-learning survival models for predicting time to recurrence in epithelial ovarian cancer' [Gynecologic Oncology 204 (2026) 184-193].

Women's reproductive factors predict local brain aging profiles mapped using deep neural networks.

Deep neural networks (DNNs) trained on magnetic resonance images can estimate global brain age (GBA), which reflects women's neurological disease risk...

Hierarchical machine learning models for predicting antenatal care utilisation among Nigerian women: Identifying actionable insights for health policy.

Harnessing lipid-driven immunometabolic pathways in omental metastases to enhance immunotherapy in patients with ovarian cancer.

Immunotherapy with immune checkpoint blockade (ICB) in epithelial ovarian carcinoma (EOC) shows limited clinical benefit only for a small subset of pa...

RNA therapeutics 2.0: Expanding the landscape from mRNA vaccines to splicing modulators and beyond.

RNA therapeutics have progressed into a disruptive drug class quickly, replacing a variety of primary experimental agents which included vaccines, ant...