Amit Bar‐Or

The paper develops MAESTRO, a self-supervised deep learning framework to transform immune profiles into embeddings capturing immune health, enabling clinical prediction and precision immunotherapy.

The paper quantifies bias in proportionality-based association metrics for count data and proposes a logit-normal multinomial model to improve accuracy.

The paper identifies a pro-inflammatory immune cell signature in pregnant women before the onset of hypertensive disorders of pregnancy.

This study identifies immune profiles in pediatric cerebrospinal fluid to differentiate pediatric-onset multiple sclerosis from other inflammatory neurological disorders.

The paper examines immunosenescence trajectories in diverse populations, finding associations between immunological age and brain atrophy, while highlighting CMV status and genetic ancestry as key factors.

The paper isolates and characterizes neutralizing anti-interferon-alpha and anti-interferon-beta antibodies from human patients, showing their specificity and potential therapeutic applications.

This study shows that brain MRI volumetric biases persist across different scanners in a harmonized MS study, with site explaining over 50% of volume variation.

The paper identifies miR-223-3p and miR-27a-3p as upregulated in MS models and shows they protect neurons by downregulating glutamate receptor signaling.