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Hoffmann, C. J.

Publications and source records attributed to Hoffmann, C. J..

2 recordsLinked to original sources

Proviruses in CD4+ T cells reactive to autologous antigens contribute to nonsuppressible HIV-1 viremia

Antiretroviral therapy (ART) halts HIV-1 replication, reducing plasma virus levels to below the limit of detection, but it is not curative due to a reservoir of latently infected CD4+ T cells. In some people living with HIV-1 (PLWH), plasma HIV-1 RNA becomes persistently detectable despite optimal ART. This nonsuppressible viremia (NSV) is characterized by identical, non-evolving HIV-1 RNA variants expressed from infected CD4+ T cell clones. The mechanisms driving persistent virus production from a specific population of infected cells are poorly understood. We hypothesized that proviruses in cells responding to chronic immunologic stimuli, including self-associated antigens, may drive viral gene expression and NSV. Here, we demonstrate that stimulation of CD4+ T cells with autologous cell lysates induces virus production in an MHC-II-dependent manner. In 7 of 8 participants with NSV, we recovered viral RNA released ex vivo in response to autologous cell lysates that matched plasma virus. This process involves both defective and replication-competent proviruses residing in conventional T cells, and is also observed in PLWH with undetectable viremia. These findings suggest that recognition of self-associated antigens is an important cause of HIV-1 reservoir expression, which can contribute to persistent systemic inflammation and potential rebound upon ART interruption. One sentence summaryHIV-1 viremia not suppressed by effective ART can be caused by proviruses in CD4+ T cells reactive to autologous antigens.

immunology↗

Deep learning-based automated lesion segmentation on mouse stroke magnetic resonance images

Magnetic resonance imaging (MRI) is widely used for ischemic stroke lesion detection in mice. A challenge is that lesion segmentation often relies on manual tracing by trained experts, which is labor-intensive, time-consuming, and prone to inter- and intra-rater variability. Here, we present a fully automated ischemic stroke lesion segmentation method for mouse T2-weighted MRI data. As an end-to-end deep learning approach, the automated lesion segmentation requires very little preprocessing and works directly on the raw MRI scans. We randomly split a large dataset of 382 MRI scans into a subset (n = 293) to train the automated lesion segmentation and a subset (n = 89) to evaluate its performance. We compared Dice coefficients and accuracy of lesion volume against manual segmentation, as well as its performance on an independent dataset from an open repository with different imaging characteristics. The automated lesion segmentation produced segmentation masks with a smooth, compact, and realistic appearance that are in high agreement with manual segmentation.

neuroscience↗