Machine Learning-Based Prediction for In-Hospital Mortality After Acute Intracerebral Hemorrhage Using Real-World Clinical and Image Data.

Background: Machine learning (ML) techniques are widely employed across various domains to achieve accurate predictions. This study assessed the effectiveness of ML in predicting early mortality risk among patients with acute intracerebral hemorrhage (ICH) in real-world settings.

Methods And Results: ML-based models were developed to predict in-hospital mortality in 527 patients with ICH using raw brain imaging data from brain computed tomography and clinical data.

Identification of responsible sequences which mutations cause maternal H19-ICR hypermethylation with Beckwith-Wiedemann syndrome-like overgrowth.

Beckwith-Wiedemann syndrome (BWS) is caused by a gain of methylation (GOM) at the imprinting control region within the Igf2-H19 domain on the maternal allele (H19-ICR GOM). Mutations in the binding sites of several transcription factors are involved in H19-ICR GOM and BWS. However, the responsible sequence(s) for H19-ICR GOM with BWS-like overgrowth has not been identified in mice.