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Appears in Networks 1

In-Edges 6

bp(GO:"GO:0014047") increases bp(MESH:D017774) View Subject | View Object

Second, because of this background signal, as well as the fact that neurons are left with smaller amounts of neurotransmitter to release into the synapse during neuronal firing, the 'peak signal'—the difference between synaptic glutamate concentration during neuronal activity and synaptic glutamate concentration under resting conditions—is attenuated, leading to suboptimal neurotransmission as exemplified by a lack of long-term potentiation (LTP) PubMed:16273023

act(p(GFAM:"Glutamate ionotropic receptor NMDA type subunits")) decreases bp(MESH:D017774) View Subject | View Object

In Alzheimer's disease, however, it is believed that Mg2+ is displaced from the NMDA receptor calcium channel even under resting conditions, due to the elevated levels of glutamate that are present in the synapse at all times.13 It has been hypothesized that this constant activation of NMDA receptors leads to neuronal overactivity while also contributing to an unfavorable signal-to-noise ratio during glutamatergic neurotransmission and, hence, to the absence of LTP. PubMed:16273023

path(MESH:D000544) decreases bp(MESH:D017774) View Subject | View Object

Second, because of this background signal, as well as the fact that neurons are left with smaller amounts of neurotransmitter to release into the synapse during neuronal firing, the 'peak signal'—the difference between synaptic glutamate concentration during neuronal activity and synaptic glutamate concentration under resting conditions—is attenuated, leading to suboptimal neurotransmission as exemplified by a lack of long-term potentiation (LTP) PubMed:16273023

path(MESH:D000544) decreases bp(MESH:D017774) View Subject | View Object

It is believed that LTP, which can persist at a given synapse for periods ranging from hours to months, models the processes of learning and memory, and a number of studies have demonstrated a loss of LTP in animal models of Alzheimer's disease. PubMed:16273023

path(MESH:D007858) association bp(MESH:D017774) View Subject | View Object

It is believed that LTP, which can persist at a given synapse for periods ranging from hours to months, models the processes of learning and memory, and a number of studies have demonstrated a loss of LTP in animal models of Alzheimer's disease. PubMed:16273023

path(MESH:D008568) association bp(MESH:D017774) View Subject | View Object

It is believed that LTP, which can persist at a given synapse for periods ranging from hours to months, models the processes of learning and memory, and a number of studies have demonstrated a loss of LTP in animal models of Alzheimer's disease. PubMed:16273023

Out-Edges 2

bp(MESH:D017774) association path(MESH:D008568) View Subject | View Object

It is believed that LTP, which can persist at a given synapse for periods ranging from hours to months, models the processes of learning and memory, and a number of studies have demonstrated a loss of LTP in animal models of Alzheimer's disease. PubMed:16273023

bp(MESH:D017774) association path(MESH:D007858) View Subject | View Object

It is believed that LTP, which can persist at a given synapse for periods ranging from hours to months, models the processes of learning and memory, and a number of studies have demonstrated a loss of LTP in animal models of Alzheimer's disease. PubMed:16273023

About

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.