Machine learning-enhanced noninvasive prenatal testing of monogenic disorders.

Objective: Single-nucleotide variants (SNVs) are of great significance in prenatal diagnosis as they are the leading cause of inherited single-gene disorders (SGDs). Identifying SNVs in a non-invasive prenatal screening (NIPS) scenario is particularly challenging for maternally inherited SNVs. We present an improved method to predict inherited SNVs from maternal or paternal origin in a genome-wide manner.

Validating staging criteria for sonographic and physical examination for cervical changes at <24 weeks' gestation to predict preterm birth.

Background: Preterm birth represents a significant clinical challenge, and the ability to predict it effectively is a priority in obstetrics. The cervix undergoes specific changes during pregnancy, and staging these changes may offer a valuable predictive model for preterm birth, particularly among women with a midtrimester short cervical length.

Objective: The study aimed to validate a proposed staging model for asymptomatic women with midtrimester short cervical length and to evaluate its potential for predicting preterm birth.

The role of secure instant messaging applications in medical education: Evaluating student satisfaction in a case-based learning program using Siilo.

Background: Instant messaging applications for mobile phones have recently grown in popularity among medical personnel, including both physicians and medical students. During the COVID-19 pandemic, medical education was largely transferred to virtual platforms, making such applications an increasingly important tool for medical education. "Siilo" is a secure instant messaging application that was designed for medical professionals, and offers several advantages over other instant messaging services that are vital for its use in medical settings, including information security, data encryption, and a built-in blurring tool to maintain patient privacy.