Machine learning tool decodes immune receptor sequences to diagnose multiple diseases

Stanford University researchers developed a machine learning-based method capable of diagnosing multiple diseases using B cell and T cell receptor sequences. The model, called Machine learning for Immunological Diagnosis (Mal-ID), distinguished between COVID-19, HIV, lupus, type 1 diabetes, influenza vaccination response, and healthy states, achieving near-perfect classification.
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Study finds stem-like T cells key to lasting immune response in cancer, chronic diseases

Prolonged illnesses like cancer and chronic infections often leave the immune system in a state of exhaustion, where its frontline defenders—T cells—lose their ability to function effectively. Research, led by the Peter Doherty Institute for Infection and Immunity (Doherty Institute) and the Peter MacCallum Cancer Center (Peter Mac), have identified a rare type of immune cells, called stem-like T cells, that holds the key to maintaining powerful, long-term immune responses.
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