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Record W2983653863 · doi:10.1111/jne.12811

Glucocorticoid and β‐adrenergic regulation of hippocampal dendritic spines

2019· article· en· W2983653863 on OpenAlexaff
Sylvie L. Lesuis, Wendy Timmermans, Paul J. Lucassen, Casper C. Hoogenraad, Harm J. Krugers

Bibliographic record

VenueJournal of Neuroendocrinology · 2019
Typearticle
Languageen
FieldNeuroscience
TopicStress Responses and Cortisol
Canadian institutionsSickKids FoundationMental Health Research CanadaHospital for Sick ChildrenUniversity of Toronto
FundersNederlandse Organisatie voor Wetenschappelijk Onderzoek
KeywordsCorticosteroneInternal medicineEndocrinologyGlucocorticoidAgonistHippocampal formationNorepinephrineHormoneGlucocorticoid receptorAntagonistExcitatory postsynaptic potentialDendritic spinePostsynaptic potentialReceptorChemistryBiologyMedicineDopamine

Abstract

fetched live from OpenAlex

Glucocorticoid hormones are particularly potent with respect to enhancing memory formation. Notably, this occurs in close synergy with arousal (i.e., when norepinephrine levels are enhanced). In the present study, we examined whether glucocorticoid and norepinephrine hormones regulate the number of spines in hippocampal primary neurons. We report that brief administration of corticosterone or the β-adrenergic receptor agonist isoproterenol alone increases spine number. This effect becomes particularly prominent when corticosterone and isoproterenol are administered together. In parallel, corticosterone and isoproterenol alone increased the amplitude of miniature excitatory postsynaptic currents, an effect that is not amplified when both hormones are administered together. The effects of co-application of corticosterone and isoproterenol on spines could be prevented by blocking the glucocorticoid receptor antagonist RU486. Taken together, both corticosterone and β-adrenergic receptor activation increase spine number, and they exert additive effects on spine number for which activation of glucocorticoid receptors is permissive.

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: Empirical · Consensus signal: Empirical
Teacher disagreement score0.471
Threshold uncertainty score0.366

Codex and Gemma teacher scores by category

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)0.0000.000

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 head0.019
GPT teacher head0.261
Teacher spread0.242 · 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
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

Citations13
Published2019
Admission routes1
Has abstractyes

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