To Immunity and Beyond
To Immunity and Beyond
Podcast Description
In a series produced by the Oxford Immunology Network, Professor Paul Klenerman talks with colleagues about their recent ground-breaking publications, exploring the original scientific goals, challenges they encountered along the way, and future ambitions in the field. This series will be of interest to anyone studying or working in immunology, medical sciences, biology or related fields.
NB: To Immunity and Beyond is for educational purposes only and should not be considered professional medical advice.
Podcast Insights
Content Themes
The series centers on pivotal themes in immunology, medical sciences, and biology, exploring current research advancements. Specific episode examples include discussions on CAR-T cell sensitivity engineering, the role of ADAM10 in T cell efferocytosis, and genetic risks for multiple sclerosis in historical populations, presenting foundational studies and their implications for future research.
In a series produced by the Oxford Immunology Network, Professor Paul Klenerman talks with colleagues about their recent ground-breaking publications, exploring the original scientific goals, challenges they encountered along the way, and future ambitions in the field. This series will be of interest to anyone studying or working in immunology, medical sciences, biology or related fields.
NB: To Immunity and Beyond is for educational purposes only and should not be considered professional medical advice.
A conversation with Yang Hu about his recent article: Self-interactive learning: Fusion and evolution of multi-scale histomorphology features for molecular traits prediction in computational pathology. This episode discusses Yang Hu et al, ‘Self-interactive learning: Fusion and evolution of multi-scale histomorphology features for molecular traits prediction in computational pathology’, Medical Image Analysis, 101 (2025) https://doi.org/10.1016/j.media.2024.103437
The relevant study was supported by the PathLAKE Centre of Excellence for digital pathology and artificial intelligence which is funded by the Data to Early Diagnosis and Precision Medicine strand of the HM Government’s Industrial Strategy Challenge Fund, managed and delivered by Innovate UK on behalf of UK Research and Innovation (UKRI) (Grant ref: 104689/application number 18181), as well as NIHR Oxford Biomedical Research Centre. Views expressed are not necessarily those of the PathLAKE Consortium members, the NHS, the UKRI, the NIHR, Innovate UK or the Department of Health.
The speaker would also like to thank the support from the Quantitative Bio-Image Group, led by Professor Jens Rittscher.
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