Influenza Virus-Specific Immunological Memory Is Enhanced by Repeated Social Defeat

Immunological memory (MEM) development is affected by stress-induced neuroendocrine mediators. Current knowledge about how a behavioral interaction, such as social defeat, alters the development of adaptive immunity, and MEM is incomplete. In this study, the experience of social disruption stress (SDR) prior to a primary influenza viral infection enhanced the frequency and function of the T cell memory pool. Socially stressed mice had a significantly enlarged population of CD8+ T cells specific for the immunodominant NP366–74 epitope of A/PR/8/34 virus in lung and spleen tissues at 6–12 wk after primary infection (resting memory). Moreover, during resting memory, SDR-MEM mice responded with an enhanced footpad delayed-type hypersensitivity response, and more IFN-γ–producing CD4+ T cells were detected after ex vivo stimulation. When mice were rechallenged with A/PR/8/34 virus, SDR-MEM mice terminated viral gene expression significantly earlier than MEM mice and generated a greater DbNP366–74CD8+ T cell response in the lung parenchyma and airways. This enhancement was specific to the T cell response. SDR-MEM mice had significantly attenuated anti-influenza IgG titers during resting memory. Similar experiments in which mice were primed with X-31 influenza and challenged with A/PR/8/34 virus elicited similar enhancements in the splenic and lung airway Db NP366–74CD8+ T cell populations in SDR-MEM mice. This study demonstrates that the experience of repeated social defeat prior to a primary viral infection significantly enhances virus-specific memory via augmentation of memory T cell populations and suggests that social stressors should be carefully considered in the design and analysis of future studies on antiviral immunity

Military maladaptation : counterinsurgency and the politics of failure

Tactical learning is critical to battlefield success, especially in a counterinsurgency. This article tests the existing model of military adaption against a ‘most-likely’ case: the British Army’s counterinsurgency in the Southern Cameroons (1960–61). Despite meeting all preconditions thought to enable adaptation – decentralization, leadership turnover, supportive leadership, poor organizational memory, feedback loops, and a clear threat – the British still failed to adapt. Archival evidence suggests politicians subverted bottom-up adaptation, because winning came at too high a price in terms of Britain’s broader strategic imperatives. Our finding identifies an important gap in the extant adaptation literature: it ignores politics.PostprintPeer reviewe

Pathogenic Connexin-31 Forms Constitutively Active Hemichannels to Promote Necrotic Cell Death

Mutations in Connexin-31 (Cx31) are associated with multiple human diseases including erythrokeratodermia variabilis (EKV). The molecular action of Cx31 pathogenic mutants remains largely elusive. We report here that expression of EKV pathogenic mutant Cx31R42P induces cell death with necrotic characteristics. Inhibition of hemichannel activity by a connexin hemichannel inhibitor or high extracellular calcium suppresses Cx31R42P-induced cell death. Expression of Cx31R42P induces ER stress resulting in reactive oxygen species (ROS) production, in turn, to regulate gating of Cx31R42P hemichannels and Cx31R42P induced cell death. Moreover, Cx31R42P hemichannels play an important role in mediating ATP release from the cell. In contrast, no hemichannel activity was detected with cells expressing wildtype Cx31. Together, the results suggest that Cx31R42P forms constitutively active hemichannels to promote necrotic cell death. The Cx31R42P active hemichannels are likely resulted by an ER stress mediated ROS overproduction. The study identifies a mechanism of EKV pathogenesis induced by a Cx31 mutant and provides a new avenue for potential treatment strategy of the disease

Validation of clinical acceptability of deep-learning-based automated segmentation of organs-at-risk for head-and-neck radiotherapy treatment planning

IntroductionOrgan-at-risk segmentation for head and neck cancer radiation therapy is a complex and time-consuming process (requiring up to 42 individual structure, and may delay start of treatment or even limit access to function-preserving care. Feasibility of using a deep learning (DL) based autosegmentation model to reduce contouring time without compromising contour accuracy is assessed through a blinded randomized trial of radiation oncologists (ROs) using retrospective, de-identified patient data.MethodsTwo head and neck expert ROs used dedicated time to create gold standard (GS) contours on computed tomography (CT) images. 445 CTs were used to train a custom 3D U-Net DL model covering 42 organs-at-risk, with an additional 20 CTs were held out for the randomized trial. For each held-out patient dataset, one of the eight participant ROs was randomly allocated to review and revise the contours produced by the DL model, while another reviewed contours produced by a medical dosimetry assistant (MDA), both blinded to their origin. Time required for MDAs and ROs to contour was recorded, and the unrevised DL contours, as well as the RO-revised contours by the MDAs and DL model were compared to the GS for that patient.ResultsMean time for initial MDA contouring was 2.3 hours (range 1.6-3.8 hours) and RO-revision took 1.1 hours (range, 0.4-4.4 hours), compared to 0.7 hours (range 0.1-2.0 hours) for the RO-revisions to DL contours. Total time reduced by 76% (95%-Confidence Interval: 65%-88%) and RO-revision time reduced by 35% (95%-CI,-39%-91%). All geometric and dosimetric metrics computed, agreement with GS was equivalent or significantly greater (p<0.05) for RO-revised DL contours compared to the RO-revised MDA contours, including volumetric Dice similarity coefficient (VDSC), surface DSC, added path length, and the 95%-Hausdorff distance. 32 OARs (76%) had mean VDSC greater than 0.8 for the RO-revised DL contours, compared to 20 (48%) for RO-revised MDA contours, and 34 (81%) for the unrevised DL OARs.ConclusionDL autosegmentation demonstrated significant time-savings for organ-at-risk contouring while improving agreement with the institutional GS, indicating comparable accuracy of DL model. Integration into the clinical practice with a prospective evaluation is currently underway