Obstetrics & Gynecology

Lipid digestion- driven drug fate as a key determinant of SNEDDS performance: Mechanistic basis of absorption and in vitro- in vivo disconnect.

Self-nanoemulsifying drug delivery systems (SNEDDS) represent a major class of lipid-based nanomedicines that are primarily utilised for the enhancement of oral delivery of poorly water-soluble drugs. However, their clinical translation remains inconsistent due to a sustained divergence between in vitro performance and in vivo bioavailability. Although substantial literature exists, a unified mech...

Detection of cancer recurrence from Thai-English electronic medical records using sentence embeddings.

OBJECTIVE: This study developed and validated monolingual and bilingual sentence-bidirectional encoder representations from transformers (SBERT) models for detecting cancer recurrence within Thai-English electronic medical records (EMRs) from Thai cancer hospitals. METHOD: A multicentre dataset of 32 436 documents from 1250 patients was used for model development. External validation involved an i...

Research on the impact of artificial intelligence on the export technological complexity of chinese manufacturing enterprises: An analysis based on mediating effects.

Technological innovation drives high-quality economic development, and artificial intelligence (AI) represents a new impetus for developing productive...

Expatriate health care workers in Saudi Arabia: Exploring workforce integration, advantages, and challenges in a globalized health care system.

BACKGROUND: Effective expatriate management has become crucial in the health care sector, driven by the growing number of globally mobile professional...

An easier government? The impact of digital government platform on government employee overtime.

BackgroundOvertime serves as an indicator of bureaucrats' efficiency. If excessive, it may undermine the country's long-term capacity to fulfill its r...

Enhancing the positive predictive value of early-stage ovarian cancer detection using a two-step machine learning framework.

BACKGROUND: Early detection of epithelial ovarian cancer (EOC) remains a major clinical challenge. Although serum tumor markers are widely used for de...

Deep learning-based dose prediction to enhance planning efficiency in cervical brachytherapy with hybrid applicators.

Objective.This study aimed to develop a dose prediction model for hybrid applicators in cervical cancer brachytherapy.Approach.A total of 216 previous...

Prioritising parameters to minimise blood transfusions for postpartum haemorrhage: A machine learning study using national population data.

BACKGROUND: Postpartum haemorrhage (PPH) accounts for approximately 27% of maternal deaths worldwide and often requires blood transfusion, along with ...

Physics-constrained dual-domain network for CBCT reconstruction from orthogonal X-rays in gynecologic radiotherapy.

BACKGROUND: Daily cone-beam computed tomography (CBCT) is widely used for image-guided radiotherapy (IGRT) in gynecologic (GYN) cancer to verify patie...

The Development Path of Gynecological Cytology after Papanicolaou: from Bethesda to Artificial Intelligence and Personalized Medicine.

BACKGROUND: Cervical cancer is the fourth most common cancer whose incidence was significantly reduced with the help of Pap smear and organized cervic...

Aspartame exposure promotes endometriosis progression through PTGS2-mediated oxidative stress and mitochondrial dysfunction.

BACKGROUND: Aspartame, a widely used artificial sweetener, has been implicated in multiple toxicities. However, its potential reproductive toxicity me...

A novel cross-attentive network for classifying cervical metastatic lymph nodes on B- and D-mode ultrasound images in oral squamous cell carcinoma.

OBJECTIVES: This study proposes a deep convolutional neural network model that integrates B-mode and D-mode ultrasound images to classify metastatic l...

Computerized Assessment of Motor Imitation for Distinguishing Autism in Video (CAMI-2DNet).

Motor imitation impairments are commonly reported in individuals with autism spectrum conditions (ASCs), suggesting that motor imitation could be used...

End-to-End Autonomous Quantification of Brain Aneurysm and Parent Artery Morphology at CT Angiography.

Purpose To develop and validate an end-to-end autonomous platform for the quantification and visualization of brain aneurysm and parent artery morphol...

SELFIE: Self-Supervised Learning for Fast Dynamic Golden-Angle Radial MRI.

Accelerated golden-angle radial acquisitions are widely used for dynamic MRI, but compressed sensing (CS)-based reconstruction presents residual artif...

Enhancing Generalizability and Clinical Integration of CT-based Deep Learning for Ovarian Tumor Classification.

Explainable machine learning for patient-specific quality assurance in intensity-modulated radiotherapy based on anatomical structures.

BACKGROUND: Patient-specific quality assurance (PSQA) plays a pivotal role in intensity-modulated radiotherapy (IMRT) to ensure accurate dose delivery...

AI-assisted Radiomic Model for Cervical Cancer Recurrence Prediction: A Multicenter Retrospective Study with Experimental Validation.

Purpose To evaluate the value of a nomogram model incorporating clinical parameters, hematologic inflammatory biomarkers, and MRI radiomic features in...

Identification of preeclampsia-associated immune-related risk loci in the Chinese population.

OBJECTIVE: Preeclampsia, the most severe hypertensive disorder of pregnancy, is the leading cause of maternal death. While genetic factors are recogni...

Real-time intraoperative identification of malignant ovarian tumors using deep learning of clinical gross specimen images.

OBJECTIVE: Ovarian cancer is one of the most common gynecologic malignancies with high mortalities. A proper operation strategy is key to improve pati...