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Record W2035623767 · doi:10.1042/bj20121662

Regulation of cholinergic activity by the vesicular acetylcholine transporter

2013· review· en· W2035623767 on OpenAlexaff
Vânia F. Prado, Ashbeel Roy, Benjamin Kolisnyk, Robert Gros, Marco A. M. Prado

Bibliographic record

VenueBiochemical Journal · 2013
Typereview
Languageen
FieldNeuroscience
TopicNeuroscience and Neuropharmacology Research
Canadian institutionsWestern University
Fundersnot available
KeywordsVesicular acetylcholine transporterAcetylcholineNeurotransmitterCholinergicSynaptic vesicleCholinergic neuronNeurotransmitter AgentsBiologyCell biologyNeurotransmitter transporterSynaptic cleftNeurotransmissionTransporterNeuroscienceChemistryBiochemistryCholine acetyltransferaseVesicleEndocrinologyCentral nervous systemGene

Abstract

fetched live from OpenAlex

Acetylcholine, the first chemical to be identified as a neurotransmitter, is packed in synaptic vesicles by the activity of VAChT (vesicular acetylcholine transporter). A decrease in VAChT expression has been reported in a number of diseases, and this has consequences for the amount of acetylcholine loaded in synaptic vesicles as well as for neurotransmitter release. Several genetically modified mice targeting the VAChT gene have been generated, providing novel models to understand how changes in VAChT affect transmitter release. A surprising finding is that most cholinergic neurons in the brain also can express a second type of vesicular neurotransmitter transporter that allows these neurons to secrete two distinct neurotransmitters. Thus a given neuron can use two neurotransmitters to regulate different physiological functions. In addition, recent data indicate that non-neuronal cells can also express the machinery used to synthesize and release acetylcholine. Some of these cells rely on VAChT to secrete acetylcholine with potential physiological consequences in the periphery. Hence novel functions for the oldest neurotransmitter known are emerging with the potential to provide new targets for the treatment of several pathological conditions.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Bench or experimental · Consensus signal: Bench or experimental
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.447
Threshold uncertainty score0.739

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0000.001
Science and technology studies0.0000.001
Scholarly communication0.0000.000
Open science0.0010.000
Research integrity0.0000.002
Insufficient payload (model declined to judge)0.0000.000

Machine scores (provisional)

The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.

Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.

Opus teacher head0.107
GPT teacher head0.392
Teacher spread0.285 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designBench or experimental
Domainnot available
GenreReview

How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".

Quick stats

Citations134
Published2013
Admission routes1
Has abstractyes

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