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Record W3123262423 · doi:10.1038/s41598-021-95143-y

Elimination of glutamatergic transmission from Hb9 interneurons does not impact treadmill locomotion

2021· article· en· W3123262423 on OpenAlexafffund
Lina M. Koronfel, Kevin C. Kanning, Angelita Alcos, Christopher E. Henderson, Robert M. Brownstone

Bibliographic record

VenueScientific Reports · 2021
Typearticle
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicZebrafish Biomedical Research Applications
Canadian institutionsDalhousie University
FundersNational Institute of Neurological Disorders and StrokeCanadian Institutes of Health ResearchNational Institutes of HealthBrain Research UKTow FoundationWellcome TrustOkinawa Institute of Science and Technology Graduate UniversitySpinal Muscular Atrophy Foundation
KeywordsGlutamatergicNeuroscienceBiologyBiological neural networkGlutamate receptorInterneuronSpinal cordPopulationNeurotransmissionMedicineInhibitory postsynaptic potentialGenetics

Abstract

fetched live from OpenAlex

The spinal cord contains neural circuits that can produce the rhythm and pattern of locomotor activity. It has previously been postulated that a population of glutamatergic neurons, termed Hb9 interneurons, contributes to locomotor rhythmogenesis. These neurons were identified by their expression of the homeobox gene, Hb9, which is also expressed in motor neurons. We developed a mouse line in which Cre recombinase activity is inducible in neurons expressing Hb9. We then used this line to eliminate vesicular glutamate transporter 2 from Hb9 interneurons, and found that there were no deficits in treadmill locomotion. We conclude that glutamatergic neurotransmission by Hb9 interneurons is not required for locomotor behaviour. The role of these neurons in neural circuits remains elusive.

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 score0.003
Threshold uncertainty score0.011

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 studies0.0000.001
Scholarly communication0.0000.000
Open science0.0010.000
Research integrity0.0010.002
Insufficient payload (model declined to judge)0.0030.001

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 head0.011
GPT teacher head0.299
Teacher spread0.288 · 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

Citations23
Published2021
Admission routes2
Has abstractyes

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