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Noradrenaline Triggers Multivesicular Release at Glutamatergic Synapses in the Hypothalamus

2005· article· en· W2035066810 on OpenAlexafffund
Grant R. Gordon, Jaideep S. Bains

Bibliographic record

VenueJournal of Neuroscience · 2005
Typearticle
Languageen
FieldNeuroscience
TopicNeuroscience and Neuropharmacology Research
Canadian institutionsAlberta Children's HospitalUniversity of Calgary
FundersCanadian Institutes of Health ResearchFondation pour la Recherche Médicale
KeywordsExcitatory postsynaptic potentialGlutamatergicNeuroscienceAMPA receptorPostsynaptic CurrentPostsynaptic potentialSynaptic vesicleGlutamate receptorBiophysicsExcitatory synapseBiologySynapseChemistryCell biologyVesicleReceptorInhibitory postsynaptic potentialBiochemistry

Abstract

fetched live from OpenAlex

The origin of large-amplitude miniature EPSCs (mEPSCs) at central synapses remains to be firmly established. Here, we show that at excitatory synapses onto magnocellular neurosecretory cells in the hypothalamus, noradrenaline induces a rapid and robust increase in mEPSC amplitude that requires alpha1-adrenoceptor activation but is impervious to postsynaptic manipulations that block the putative insertion of AMPA receptors. In response to noradrenaline, mEPSCs exhibit a putative multimodal amplitude histogram distribution that is not attributable to random temporal summation, the unveiling of a quiescent synapse, or the release of large vesicles. Large-amplitude mEPSCs are sensitive to a high dose of ryanodine and are associated with an enhanced glutamate cleft concentration. Together, these data are consistent with the hypothesis that large-amplitude mEPSCs result from the synchronous release of multiple vesicles via rapid presynaptic calcium expulsion from intracellular stores.

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 score0.002

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 communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
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 head0.060
GPT teacher head0.340
Teacher spread0.280 · 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

Citations48
Published2005
Admission routes2
Has abstractyes

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