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Shenoi, S.

Publications and source records attributed to Shenoi, S..

2 recordsLinked to original sources

Type I Interferon-Driven Monocyte Dysregulation and MAS-associated CD8+ T cells During Macrophage Activation Syndrome

Macrophage activation syndrome (MAS) is driven by a hyperinflammatory response characterized by aberrant activation of lymphocytes and phagocytes. While monocytes and macrophages are thought to be important in MAS pathogenesis, their role remains poorly understood. We used bulk and single-cell RNA sequencing (RNA-Seq) on sorted monocytes from children with MAS and healthy controls to identify transcriptional changes during MAS. We defined a MAS signature in classical monocytes that correlated with ferritin and was elevated in monocytes from systemic lupus erythematosus and COVID-19 patients. We also identified a subset of classical monocytes with high levels of interferon-stimulated genes (ISGs) that expanded during MAS. Surprisingly, the transcriptional signature of these cells was driven by type I IFNs, rather than IFN{gamma}. Consistent with this finding, we detected increased levels of circulating IFN{beta} during MAS, suggesting that IFN{beta} plays an unrecognized role in driving MAS monocyte responses. We also identified a MAS-associated CD8+ T cell population with a distinctive transcriptional signature. We used cell-cell communication algorithms to predict increased immunoregulatory interactions between monocytes and T cells during MAS. Together, these results provide new evidence for a role for type I IFN during MAS and identify a unique CD8+ T cell population that may contribute to MAS pathophysiology.

immunology↗

Characterizing brain imaging features associated with ADAS-Cog13 sub-scores with 3D convolutional neural networks

To date, few advanced machine learning models have been developed for investigating the associations between features from brain imaging and individual Alzheimers disease (AD) related cognitive functional changes. Additionally, how these associations differ among different imaging modalities is unclear. Here we investigated 3D convolutional neural network (CNN) models which were trained to predict sub-scores in 13-item Alzheimers Disease Assessment Scale - Cognitive Subscale (ADAS-Cog13) based on MRI and FDG-PET brain imaging data obtained from the ADNI database. We found that each key ADAS-Cog13 sub-score was associated with a specific set of brain features within an imaging modality. Overall, sub-scores were strongly associated with structural changes of subcortical regions including amygdala, hippocampus, and putamen, and were associated with metabolic changes of cortical regions including the cingulated gyrus, occipital cortex, middle front gyrus, precuneus cortex, and the cerebellum. Our findings provided insights into complex AD etiology. Our analytical pipeline can also be utilized to study other brain diseases.

neuroscience↗