Search for: All records

Expansion of an initial population of T cells is essential for cellular immunotherapy. In Chronic Lymphocytic Leukemia (CLL), expansion is often complicated by lack of T cell proliferation, as these cells frequently show signs of exhaustion. This report seeks to identify specific biomarkers or measures of cell function that capture the proliferative potential of a starting population of cells. Mixed CD4+/CD8+ T cells from healthy donors and individuals previously treated for CLL were characterized on the basis of proliferative potential and in vitro cellular functions. Single-factor analysis found little correlation between the number of populations doublings reached during expansion and either Rai stage (a clinical measure of CLL spread) or PD-1 expression. However, inclusion of in vitro IL-2 secretion and the propensity of cells to align onto micropatterned features of activating proteins as factors identified three distinct groups of donors. Notably, these group assignments provided an elegant separation of donors with regards to proliferative potential. Furthermore, these groups exhibited different motility characteristics, suggesting a mechanism that underlies changes in proliferative potential. This study describes a new set of functional readouts that augment surface marker panels to better predict expansion outcomes and clinical prognosis. 

Abstract Cloud condensation nuclei (CCN) are mediators of aerosol‐cloud interactions, which contribute to the largest uncertainty in climate change prediction. Here, we present a machine learning (ML)/artificial intelligence (AI) model that quantifies CCN from model‐simulated aerosol composition, atmospheric trace gas, and meteorological variables. Comprehensive multi‐campaign airborne measurements, covering varied physicochemical regimes in the troposphere, confirm the validity of and help probe the inner workings of this ML model: revealing for the first time that different ranges of atmospheric aerosol composition and mass correspond to distinct aerosol number size distributions. ML extracts this information, important for accurate quantification of CCN, additionally from both chemistry and meteorology. This can provide a physicochemically explainable, computationally efficient, robust ML pathway in global climate models that only resolve aerosol composition; potentially mitigating the uncertainty of effective radiative forcing due to aerosol‐cloud interactions (ERF_aci) and improving confidence in assessment of anthropogenic contributions and climate change projections.