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Record W2210116731 · doi:10.1152/jn.2001.86.3.1131

Voltage-Sensitivity of Motoneuron NMDA Receptor Channels Is Modulated by Serotonin in the Neonatal Rat Spinal Cord

2001· article· en· W2210116731 on OpenAlexaff
Jason N. MacLean, Brian J. Schmidt

Bibliographic record

VenueJournal of Neurophysiology · 2001
Typearticle
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicIon channel regulation and function
Canadian institutionsUniversity of Manitoba
Fundersnot available
KeywordsNMDA receptorNeuroscienceSpinal cordNeurochemicalChemistrySerotonin5-HT receptorTetrodotoxinGlutamate receptorBiophysicsReceptorBiologyBiochemistry

Abstract

fetched live from OpenAlex

Both N-methyl-D-aspartate (NMDA) and serotonin (5-HT) receptors contribute to the generation of rhythmic motor patterns in the rat spinal cord. Co-application of these chemicals is more effective at producing locomotor-like activity than either neurochemical alone. In addition, NMDA application to rat spinal motoneurons, synaptically isolated in tetrodotoxin, induces nonlinear membrane behavior that results in voltage oscillations which can be blocked by 5-HT antagonists. However, the mechanisms underlying NMDA and 5-HT receptor interactions pertinent to motor rhythm production remain to be determined. In the present study, an in vitro neonatal rat spinal cord preparation was used to examine whether NMDA receptor-mediated nonlinear membrane voltage is modulated by 5-HT. Whole-cell recordings of spinal motoneurons demonstrated that 5-HT shifts the region of NMDA receptor-dependent negative slope conductance (RNSC) of the current-voltage relationship to more hyperpolarized potentials and enhances whole-cell inward current. The influence of 5-HT on the RNSC was similar to the effect on the RNSC of decreasing the extracellular Mg(2+)concentration. The results suggest that 5-HT may modulate this form of membrane voltage nonlinearity by regulating Mg(2+) blockade of the NMDA ionophore.

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 score0.120
Threshold uncertainty score0.386

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 head0.016
GPT teacher head0.257
Teacher spread0.241 · 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

Citations35
Published2001
Admission routes1
Has abstractyes

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