M-DDC: MRI based demyelinative diseases classification with U-Net segmentation and convolutional network.

Childhood demyelinative diseases classification (DDC) with brain magnetic resonance imaging (MRI) is crucial to clinical diagnosis. But few attentions have been paid to DDC in the past. How to accurately differentiate pediatric-onset neuromyelitis optica spectrum disorder (NMOSD) from acute disseminated encephalomyelitis (ADEM) based on MRI is challenging in DDC.

Differentiation of MOGAD in ADEM-like presentation children based on FLAIR MRI features.

Objective: The differences in magnetic resonance imaging (MRI) between children with classic acute disseminated encephalomyelitis (ADEM) and myelinal oligodendrocyte glycoprotein antibody associated disease (MOGAD) with ADEM-like presentation are controversial. The purpose of this study was to investigate whether the radiological characteristics of the MRI-FLAIR sequence can predict MOGAD in children with ADEM-like presentation and to further explore its imaging differences.

Methods: We extracted 1041 radiomics features from MRI-FLAIR lesions.

Determination of significant parameters in remote ischemic postconditioning for ischemic stroke in experimental models: A systematic review and meta-analysis study.

Objectives: To systematically review studies using remote ischemia postconditioning (RIPostC) for ischemic stroke in experimental models and obtain factors that significantly influence treatment outcomes.

Materials And Methods: Peer-reviewed studies were identified and selected based on the eligibility criteria, followed by extraction of data on potentially influential factors related to model preparation, postconditioning, and measure time based on outcome measures including infarct size, neurological scales, and cell tests with autophagy, apoptosis, normal-neuron, and damaged-neuron counting. Then, all data were preprocessed, grouped, and meta-analyzed with the indicator of the standardized mean difference.