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The cholinergic anti-inflammatory pathway (CAP) refers to the anti-inflammatory effects mediated by the parasympathetic nervous system. Existence of this pathway was first demonstrated when acetylcholinesterase inhibitors showed benefits in animal models of sepsis. CAP functions via the vagus nerve. The systemic anti-inflammatory effects of CAP converges on the α7 nicotinic acetylcholine receptor on splenic macrophages, leading to suppression of pro-inflammatory cytokines and simultaneous stimulation of anti-inflammatory cytokines, including interleukin 10. CAP offers a novel mechanism to mitigate inflammation. Electrical vagal nerve stimulation has shown benefits in patients suffering from rheumatoid arthritis. Direct agonists like nicotine and GTS-1 have also demonstrated anti-inflammatory properties in models of sepsis and acute respiratory distress syndrome, as have acetylcholinesterase inhibitors like Galantamine and Physostigmine. Experience with coronavirus disease 2019 (COVID-19) induced acute respiratory distress syndrome indicates that immunomodulators have a protective role in patient outcomes. Dexamethasone is the only medication currently in use that has shown to improve clinical outcomes. This is likely due to the suppression of what is referred to as a cytokine storm, which is implicated in the lethality of viral pneumonia. Nicotine transdermal patch activates CAP and harvests its anti-inflammatory potential by means of an easily administered depot delivery mechanism. It could prove to be a promising, safe and inexpensive additional tool in the currently limited armamentarium at our disposal for management of COVID-19 induced acute hypoxic respiratory failure.  相似文献   
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Converging evidence suggests that common complex diseases with the same or similar clinical manifestations could have different underlying genetic etiologies. While current research interests have shifted toward uncovering rare variants and structural variations predisposing to human diseases, the impact of heterogeneity in genetic studies of complex diseases has been largely overlooked. Most of the existing statistical methods assume the disease under investigation has a homogeneous genetic effect and could, therefore, have low power if the disease undergoes heterogeneous pathophysiological and etiological processes. In this paper, we propose a heterogeneity‐weighted U (HWU) method for association analyses considering genetic heterogeneity. HWU can be applied to various types of phenotypes (e.g., binary and continuous) and is computationally efficient for high‐dimensional genetic data. Through simulations, we showed the advantage of HWU when the underlying genetic etiology of a disease was heterogeneous, as well as the robustness of HWU against different model assumptions (e.g., phenotype distributions). Using HWU, we conducted a genome‐wide analysis of nicotine dependence from the Study of Addiction: Genetics and Environments dataset. The genome‐wide analysis of nearly one million genetic markers took 7h, identifying heterogeneous effects of two new genes (i.e., CYP3A5 and IKBKB) on nicotine dependence. Copyright © 2016 John Wiley & Sons, Ltd.  相似文献   
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