AIMC Topic: Immunotherapy, Adoptive

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Unsupervised Machine Learning-Based Process Analytical Tools for Near Real-Time Cell Morphology Analysis During CAR-T Cell Manufacturing.

Biotechnology and bioengineering
Cell therapies like Chimeric Antigen Receptor (CAR)-T cell therapy deliver living cells to patients as active pharmaceutical ingredients. Manufacturing of these cells is complex, often yielding, heterogeneous products and high failure rates. Quality ...

Advancing T-cell immunotherapy for cellular senescence and disease: Mechanisms, challenges, and clinical prospects.

Ageing research reviews
Cellular senescence is a complex biological process with a dual role in tissue homeostasis and aging-related pathologies. Accumulation of senescent cells promotes chronic inflammation, tissue dysfunction, age-related diseases, and tumor suppression. ...

Advancing CAR T-cell Therapies with Artificial Intelligence: Opportunities and Challenges.

Blood cancer discovery
Artificial intelligence could enhance chimeric antigen receptor T-cell therapy outcomes through optimization of all steps, from target identification, vector design, and manufacturing to personalized data-driven clinical decisions. In this report, we...

New tools for MHC research from machine learning and predictive algorithms to the tumour immunopeptidome.

Immunology
At a time when immunology seeks to progress ever more rapidly from characterization of a microbial or tumour antigen to the immune correlates that may define protective T-cell immunity, there is a need for robust tools to enable accurate predictions ...