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Record W2007143131 · doi:10.1126/science.1209985

Acetylcholine-Synthesizing T Cells Relay Neural Signals in a Vagus Nerve Circuit

2011· article· en· W2007143131 on OpenAlexaff
Mauricio Rosas‐Ballina, Peder S. Olofsson, Mahendar Ochani, Sergio Iván Valdés‐Ferrer, Yaakov A. Levine, Colin Reardon, Michael W. Tusche, Valentin A. Pavlov, Sangeeta S. Chavan, Tak W. Mak, Kevin J. Tracey

Bibliographic record

VenueScience · 2011
Typearticle
Languageen
FieldNeuroscience
TopicVagus Nerve Stimulation Research
Canadian institutionsUniversity Health NetworkOntario Institute for Cancer Research
FundersNational Institute of General Medical Sciences
KeywordsAcetylcholineCholinergicVagus nerveVagus nerve stimulationNicotinic acetylcholine receptorNeuroscienceAcetylcholine receptorBiologyInternal medicineStimulationEndocrinologyMedicineReceptor

Abstract

fetched live from OpenAlex

Neural circuits regulate cytokine production to prevent potentially damaging inflammation. A prototypical vagus nerve circuit, the inflammatory reflex, inhibits tumor necrosis factor-α production in spleen by a mechanism requiring acetylcholine signaling through the α7 nicotinic acetylcholine receptor expressed on cytokine-producing macrophages. Nerve fibers in spleen lack the enzymatic machinery necessary for acetylcholine production; therefore, how does this neural circuit terminate in cholinergic signaling? We identified an acetylcholine-producing, memory phenotype T cell population in mice that is integral to the inflammatory reflex. These acetylcholine-producing T cells are required for inhibition of cytokine production by vagus nerve stimulation. Thus, action potentials originating in the vagus nerve regulate T cells, which in turn produce the neurotransmitter, acetylcholine, required to control innate immune responses.

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 machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: metacan-v3-hybrid-931329e0061cValidation 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: Empirical · Consensus signal: Empirical
Teacher disagreement score0.001
Threshold uncertainty score0.003

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0010.001
Open science0.0000.000
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0010.000

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 head0.208
GPT teacher head0.339
Teacher spread0.131 · 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 source (direct Gemma or distilled Codex), not a consensus.

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

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

Citations1,537
Published2011
Admission routes1
Has abstractyes

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