In the pig model of coronary ISR, using a prototype of PAP-1-eluting stent, no differences were observed regarding % of stenosis compared to control stents (31 ± 13 % vs 37 ± 18%, respectively; P = 0.372) at 28-days follow-up. PAP-1 treatment was safe and did not impair vascular healing in terms of delayed endothelialization, inflammation or thrombosis. However, an incomplete release of PAP-1 from stents was documented. We conclude that the use of selective Kv1.3 blockers represents a promising therapeutic approach for the prevention of intimal hyperplasia in AV, although further studies to improve their delivery method are needed to elucidate its potential in ISR.Allergic asthma, which is the most common type of asthma, is mediated by the IgE response, and B cells are key drivers of allergic inflammation in the lungs. B cell activation factor (BAFF) and proliferation inducing ligand (APRIL) are members of the TNF superfamily. BAFF and APRIL interact with three receptors, namely the B cell activation factor receptor (BAFF-r), B cell maturation antigen (BCMA), and transmembrane activator; calcium modulator; and cyclophilin ligand interactor (TACI). The interaction of BAFF and APRIL with their receptors induces B cell activation, differentiation, and antibody production. BAFF and APRIL are produced by airway epithelial cells during the response to allergens or infectious agents, and have shown to induce local IgE production, thus establishing allergic inflammation in the airways. BAFF can maintain in inflamed airways during infection and can inhibit regulatory T cells (Tregs), thereby promoting allergic inflammation in the airways. This review aims to outline current knowledge about BAFF/APRIL systems in humans as well as in murine models of allergic asthma. The precise role of BAFF and APRIL and their receptors in allergic asthma remains unclear. Therefore, further studies are required to identify and elucidate their roles in enhancing IgE production and activating immune cells that drive the Th2 effector response and initiate allergic inflammation in asthma. Targeting BAFF/APRIL or their cognate receptors may offer a novel therapeutic approach in asthma treatment.Statistical surveys of COVID-19 patients indicate, against all common logic, that people who smoke are less prone to the infection and/or exhibit less severe respiratory symptoms than non-smokers. This suggests that nicotine may have some preventive or modulatory effect on the inflammatory response in the lungs. Because it is known that the response to, and resolution of the SARS-CoV-2 infection depends mainly on the lung macrophages, we discuss the recent scientific findings, which may explain why and how nicotine may modulate lung macrophage response during COVID-19 infection.Whole brain segmentation of fine-grained structures using deep learning (DL) is a very challenging task since the number of anatomical labels is very high compared to the number of available training images. To address this problem, previous DL methods proposed to use a single convolution neural network (CNN) or few independent CNNs. In this paper, we present a novel ensemble method based on a large number of CNNs processing different overlapping brain areas. Inspired by parliamentary decision-making systems, we propose a framework called AssemblyNet, made of two "assemblies" of U-Nets. Such a parliamentary system is capable of dealing with complex decisions, unseen problem and reaching a relevant consensus. AssemblyNet introduces sharing of knowledge among neighboring U-Nets, an "amendment" procedure made by the second assembly at higher-resolution to refine the decision taken by the first one, and a final decision obtained by majority voting. During our validation, AssemblyNet showed competitive performance compared to state-of-the-art methods such as U-Net, Joint label fusion and SLANT. Moreover, we investigated the scan-rescan consistency and the robustness to disease effects of our method. These experiences demonstrated the reliability of AssemblyNet. Finally, we showed the interest of using semi-supervised learning to improve the performance of our method.Recent accounts of large-scale cortical organisation suggest that the default mode network (DMN) is positioned at the top of a principal gradient, reflecting the separation between heteromodal and unimodal sensory-motor regions in patterns of connectivity and in geodesic distance along the cortical surface (Margulies et al., 2016). This isolation of DMN from external inputs might allow the integration of disparate sources of information that can constrain subsequent cognition. We tested this hypothesis by manipulating the degree to which semantic decisions for ambiguous words (e.g. jam) were constrained by preceding visual cues depicting relevant spatial contexts (e.g. supermarket or road) and/or facial emotions (e.g. https://www.selleckchem.com/products/ipi-145-ink1197.html happy vs. frustrated). We contrasted (i) the effects of a single preceding cue with a no-cue condition employing scrambled images, and (ii) convergent spatial and emotion cues with single cues. Single cues elicited stronger activation in the multiple demand network relative to no cues, consistent with the requirement to maintain information in working memory. The availability of two convergent cues elicited stronger activation within DMN regions (bilateral angular gyrus, middle temporal gyrus, medial prefrontal cortex, and posterior cingulate), even though behavioural performance was unchanged by cueing - consequently task difficulty is unlikely to account for the observed differences in brain activation. A regions-of-interest analysis along the unimodal-to-heteromodal principal gradient revealed maximal activation for the convergent cue condition at the heteromodal end, corresponding to the DMN. Our findings are consistent with the view that regions of DMN support states of information integration that constrain ongoing cognition and provide a framework for understanding the location of these effects at the heteromodal end of the principal gradient.Resting-state functional MRI activity is organized as a complex network. However, this coordinated brain activity changes with time, raising questions about its evolving temporal arrangement. Does the brain visit different configurations through time in a random or ordered way? Advances in this area depend on developing novel paradigms that would allow us to shed light on these issues. We here propose to study the temporal changes in the functional connectome by looking at transition graphs of network activity. Nodes of these graphs correspond to brief whole-brain connectivity patterns (or meta-states), and directed links to the temporal transition between consecutive meta-states. We applied this method to two datasets of healthy subjects (160 subjects and a replication sample of 54), and found that transition networks had several non-trivial properties, such as a heavy-tailed degree distribution, high clustering, and a modular organization. This organization was implemented at a low biological cost with a high cost-efficiency of the dynamics.