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Mucosal basophils, eosinophils, and mast cells
Published in Phillip D. Smith, Richard S. Blumberg, Thomas T. MacDonald, Principles of Mucosal Immunology, 2020
Edda Fiebiger, Stephan C. Bischoff
Human mast cells also likely participate in regulating lymphocyte functions during the course of allergic inflammation. Upon IgE cross-linking, mast cells produce IL-13, a cytokine that supports the production of allergen-specific IgE by B cells. The release of IL-13 can be further increased by the presence of IL-4, which is known to shift the cytokine profile produced by human mast cells away from pro-inflammatory cytokines—such as TNF-α, IL-1β, and IL-6—to TH2 cytokines, including IL-13. Human mast cells can also regulate T-cell functions through other mediators, such as PGD2 that almost exclusively derives from activated mast cells, is released during allergic reactions, and is particularly important at the onset and in the perpetuation of asthma in young adults. This lipid mediator evokes airway hypersensitivity and chemotaxis of T cells, basophils, and eosinophils through interaction with two receptors: the prostanoid DP receptor (PTGDR) on granulocytes and smooth muscle cells; and CRTH2 (chemoattractant receptor-homologous molecule expressed on TH2 cells) on TH2 cells. Furthermore, PTGDR has been identified as an asthma-susceptibility gene. Apart from PGD2, other human mast-cell mediators, such as LTB4, CCL3, and CCL4; OX40 ligand; and TNF-α, are involved in recruiting T cells and triggering T-cell–mediated adaptive immune responses, including memory induction, that enhance and perpetuate allergic reactions.
A preliminary study on topical cetirizine in the therapeutic management of androgenetic alopecia
Published in Journal of Dermatological Treatment, 2018
A. Rossi, D. Campo, M. C. Fortuna, V. Garelli, G. Pranteda, G. De Vita, L. Sorriso-Valvo, D. Di Nunno, M. Carlesimo
Garza et al. found elevated levels of prostaglandin D2 synthase (PTGDS) at the mRNA and protein levels in bald scalp versus haired scalp of men with AGA; as well as the enzymatic product of PTGDS, prostaglandin D2 (PGD2), is generally elevated in bald human scalp tissue. Furthermore, Garza et al. provided functional data indicating that PGD2 and its no enzymatic metabolites, 15-deoxy-Δ12,14-prostaglandin J2 (15-dPGJ2), inhibit hair growth in both mouse and human hair follicles. In addition, in mice and human models, hair growth inhibition requires the PGD(2) receptor G protein-coupled receptor 44 (GPR44), but not the PGD(2) receptor 1 (PTGDR) (6–8).
Liquid biopsy in newly diagnosed patients with locoregional (I-IIIA) non-small cell lung cancer
Published in Expert Review of Molecular Diagnostics, 2019
Kezhong Chen, Guannan Kang, Heng Zhao, Kai Zhang, Jian Zhang, Fan Yang, Jun Wang
In addition to prognosis prediction, MRD detection by ctDNA can differentiate high-risk populations who may benefit from adjuvant therapy. Considering the heterogeneity of patients with the same TNM stage, some patients may receive unnecessary adjuvant therapy, while some high-risk patients may be delayed for indispensable treatment. Therefore, MRD detection by liquid biopsy may be a useful adjunct to the TNM staging system. Some researchers expect to predict prognosis accurately in the early postoperative period or even before surgical treatment. Ooki et al. established a panel of cancer-specific methylated genes including CDO1, HOXA9, PTGDR, and AJAP1. A prognostic risk category based on this panel could refine current stratification for outcomes as independent prognostic factor for early-stage lung cancer. The prediction of prognosis could be made at the time of diagnosis [41]. Note that this study also illustrated the complexity of relationship between prognosis and promotor methylation status. Individual promoter methylation is only a part of the epigenome-wide methylation status, and the impact of one methylated promoter can be neutralized by compensatory expression of others [41]. More studies are needed to illuminate the network of tumor epigenetics. Meanwhile, current research is still insufficient to incorporate liquid biopsy into routine application. First, the sensitivity of MRD detection varies among detection methods, and some detection platforms cannot meet clinical requirements. In addition, some patients may have detectable ctDNA after a relatively long time, and these patients may be classified into a low-risk population and have treatment delays. To date, there are no data from large-scale prospective clinical trials to explore the feasibility of ctDNA-based adjuvant therapy.
Post-viral atopic airway disease: pathogenesis and potential avenues for intervention
Published in Expert Review of Clinical Immunology, 2019
Syed-Rehan A. Hussain, Asuncion Mejias, Octavio Ramilo, Mark. E. Peeples, Mitchell H. Grayson
Atopy is a very complex disease involving multiple environmental, pharmacogenetic, physiologic, biochemical, and microbiological factors that result in asthma and other atopic diseases. Advanced statistical modeling, such as Bayesian Network Analysis, a machine learning statistical analysis process, can serve as a tool to identify risk factors that may contribute to the development of the asthma phenotype. Through correlations, Bayesian Network Analysis determines a hierarchical network that specifies which variables associated with the outcome are actually contributing to the disease phenotype. For example, GWAS or RNAseq coupled with Bayesian network based multilevel analysis of relevance (BN-BMLA) can identify genes that may be used as targets for personalized therapy. There are few studies that have used BN-BMLA for evaluation of asthma coupled with genome screening. One such study identified a SNP (rs3751464) in the FRMD6 gene that was associated with asthma. Further, a BN-BMLA approach identified an additional 5 SNPs in four genes on Chr 11 (PRPF19) and Chr 14 (FRMD6, PTGER2, and PTGDR) that were linked to asthma phenotypes [57]. A more recent study found an association between a SNP in FRMD6 and exercise-induced asthma [58]. Further, machine learning processes have been shown to predict acute asthma exacerbations with high sensitivity and accuracy using a 7-day observation window to predict the patient outcome on day 8 [59]. However, it is important to note that there are no studies that have yet used this statistical tool to examine post viral atopic diseases. The strength of including BN-BMLA in post-viral atopic disease research is that it seeks to model causal relationships between multiple variables and can have a dynamic state analysis at given time points. Perhaps in the future, BN-BMLA will be used clinically to predict those at risk for developing viral induced asthma/atopy, as well as providing insights into the most important pathways driving disease.