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Record W2105023809 · doi:10.1242/jeb.089730

Neuromodulators, stress and plasticity: a role for endocannabinoid signalling

2013· review· en· W2105023809 on OpenAlexafffund
Laura Senst, Jaideep S. Bains

Bibliographic record

VenueJournal of Experimental Biology · 2013
Typereview
Languageen
FieldMedicine
TopicCannabis and Cannabinoid Research
Canadian institutionsUniversity of Calgary
FundersCanadian Institutes of Health Research
KeywordsNeuroscienceBiological neural networkNeuroplasticityEndocannabinoid systemSignallingNeuronal circuitsPsychologyStress (linguistics)Structural plasticityBiologyCell biology

Abstract

fetched live from OpenAlex

Any unanticipated threat to survival triggers an immediate sequence of events in the brain that culminate in a coordinated neural, endocrine and behavioural response. There is increasing evidence that stress itself modifies neural circuits. In other words, neural stress circuits learn from stress. This self-teaching is surprising as one might expect these essential circuits to be hard-wired. Our recent findings, however, indicate that repeated homotypic stress in rats causes functional changes in neural circuitry in the hypothalamus. In particular, we focus on signalling via endocannabinoids and describe plasticity in this system that impacts fast retrograde signalling at synapses on to the stress command neurons in the brain. Interestingly, this plasticity appears to be limited to early adolescence, hinting at unique modes of control of neural circuits by stress during different developmental stages.

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: Not applicable · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.003
Threshold uncertainty score0.011

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0020.002
Science and technology studies0.0000.001
Scholarly communication0.0010.001
Open science0.0010.001
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0030.002

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 head0.043
GPT teacher head0.369
Teacher spread0.326 · 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 designNot applicable
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

Citations18
Published2013
Admission routes2
Has abstractyes

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