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a(HBP:"B-973") increases act(a(MESH:"alpha7 Nicotinic Acetylcholine Receptor")) View Subject | View Object

Peak current was increased 2-fold and 6-fold relative to 3 mM acetylcholine in 300 nM and 1 μM B-973, respectively (Fig. 1C) PubMed:28132910

a(HBP:"B-973") increases act(a(MESH:"alpha7 Nicotinic Acetylcholine Receptor")) View Subject | View Object

The amplitude of currents were dose dependent, reaching levels at 30 μM B-973 larger than control currents in response to 3 mM acetylcholine (Fig. 7A) PubMed:28132910

a(HBP:"B-973") increases act(a(MESH:"alpha7 Nicotinic Acetylcholine Receptor")) View Subject | View Object

The currents induced by B-973 alone arise from the α7 receptor since methyllycaconitine blocks them nearly completely (Fig. 7C and Fig. S6) PubMed:28132910

a(HBP:"B-973") increases act(a(CHEBI:acetylcholine)) View Subject | View Object

Over the concentration range studied, B-973 increased the potency of acetylcholine at the α7 receptor 70-fold (control acetylcholine EC50=0.49 mM; acetylcholine EC50 at 1 μM B-973=0.007 mM) PubMed:28132910

a(HBP:"B-973") increases complex(a(MESH:"alpha7 Nicotinic Acetylcholine Receptor"), a(PUBCHEM:16068384)) View Subject | View Object

[3H]A-585539 saturation binding revealed that B-973 and PNU-120596 increased the affinity of the receptor for [3H]A-585539 approximately 4-fold without changing the apparent Bmax (Fig. 5D) PubMed:28132910

a(HBP:"B-973") increases complex(a(PUBCHEM:16068384), p(MGI:Chrna7)) View Subject | View Object

At the highest PAM concentration tested (10 μM), the percent increase in [3H]A-585539 binding was much greater for recombinant α7 than that observed with rat brain membranes PubMed:28132910

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BEL Commons is developed and maintained in an academic capacity by Charles Tapley Hoyt and Daniel Domingo-Fernández at the Fraunhofer SCAI Department of Bioinformatics with support from the IMI project, AETIONOMY. It is built on top of PyBEL, an open source project. Please feel free to contact us here to give us feedback or report any issues. Also, see our Publishing Notes and Data Protection information.

If you find BEL Commons useful in your work, please consider citing: Hoyt, C. T., Domingo-Fernández, D., & Hofmann-Apitius, M. (2018). BEL Commons: an environment for exploration and analysis of networks encoded in Biological Expression Language. Database, 2018(3), 1–11.