The mechanism by which inflammation influences the adaptive response to vaccines is not fully understood. Here, we examine the role of lymph node macrophages (LNMs) in the induction of the cytokine storm triggered by inactivated influenza virus vaccine. Following vaccination, LNMs undergo inflammasome-independent necrosis-like death that is reliant on MyD88 and Toll-like receptor 7 (TLR7) expression and releases pre-stored interleukin-1α (IL-1α). Furthermore, activated medullary macrophages produce interferon-β (IFN-β) that induces the autocrine secretion of IL-1α. We also found that macrophage depletion promotes lymph node-resident dendritic cell (LNDC) relocation and affects the capacity of CD11b+ LNDCs to capture virus and express co-stimulatory molecules. Inhibition of the IL-1α-induced inflammatory cascade reduced B cell responses, while co-administration of recombinant IL-1α increased the humoral response. Stimulation of the IL-1α inflammatory pathway might therefore represent a strategy to enhance antigen presentation by LNDCs and improve the humoral response against influenza vaccines.

The role of natural killer (NK) cells in the immune response against vaccines is not fully understood. Here, we examine the function of infiltrated NK cells in the initiation of the inflammatory response triggered by inactivated influenza virus vaccine in the draining lymph node (LN). We observed that, following vaccination, NK cells are recruited to the interfollicular and medullary areas of the LN and become activated by type I interferons (IFNs) produced by LN macrophages. The activation of NK cells leads to their early production of IFNγ, which in turn regulates the recruitment of IL-6+ CD11b+ dendritic cells. Finally, we demonstrate that the interleukin-6 (IL-6)-mediated inflammation is important for the development of an effective humoral response against influenza virus in the draining LN.

By considering knowledge as a path, machines and humans can collaboratively analyze complex biomedical datasets.

Recent advances in intravital video microscopy have allowed the visualization of leukocyte behavior in vivo, revealing unprecedented spatiotemporal dynamics of immune cell interaction. However, state-of-the-art software and methods for automatically measuring cell migration exhibit limitations in tracking the position of leukocytes over time. Challenges arise both from the complex migration patterns of these cells and from the experimental artifacts introduced during image acquisition. Additionally, the development of novel tracking tools is hampered by the lack of a sound ground truth for algorithm validation and benchmarking. Therefore, the objective of this work was to create a database, namely LTDB, with a significant number of manually tracked leukocytes. Broad experimental conditions, sites of imaging, types of immune cells and challenging case studies were included to foster the development of robust computer vision techniques for imaging-based immunological research. Lastly, LTDB represents a step towards the unravelling of biological mechanisms by video data mining in systems biology.

We propose a deep learning (DL) model and a hyperparameter optimization strategy to reconstruct T₁ and T₂ maps acquired with the magnetic resonance fingerprinting (MRF) methodology. We applied two different MRF sequence routines to acquire images of ex vivo rat brain phantoms using a 7‐T preclinical scanner. Subsequently, the DL model was trained using experimental data, completely excluding the use of any theoretical MRI signal simulator. The best combination of the DL parameters was implemented by an automatic hyperparameter optimization strategy, whose key aspect is to include all the parameters to the fit, allowing the simultaneous optimization of the neural network architecture, the structure of the DL model, and the supervised learning algorithm. By comparing the reconstruction performances of the DL technique with those achieved from the traditional dictionary‐based method on an independent dataset, the DL approach was shown to reduce the mean percentage relative error by a factor of 3 for T₁ and by a factor of 2 for T₂, and to improve the computational time by at least a factor of 37. Furthermore, the proposed DL method enables maintaining comparable reconstruction performance, even with a lower number of MRF images and a reduced k‐space sampling percentage, with respect to the dictionary‐based method. Our results suggest that the proposed DL methodology may offer an improvement in reconstruction accuracy, as well as speeding up MRF for preclinical, and in prospective clinical, investigations.