MétaCan
Menu
Back to cohort

Initiating or Blocking Locomotion in Spinal Cats by Applying Noradrenergic Drugs to Restricted Lumbar Spinal Segments

2000· article· en· W1941846571 on OpenAlexaff
Judith Marcoux, Serge Rossignol

Bibliographic record

VenueJournal of Neuroscience · 2000
Typearticle
Languageen
FieldMedicine
TopicSpinal Cord Injury Research
Canadian institutionsUniversité de Montréal
FundersMedical Research Council
KeywordsCATSBlocking (statistics)NeuroscienceLumbarMedicinePsychologyAnatomyComputer scienceInternal medicine

Abstract

fetched live from OpenAlex

After an acute low thoracic spinal transection (T13), cats can be made to walk with the hindlimbs on a treadmill with clonidine, an alpha2-noradrenergic agonist. Because previous studies of neonatal rat spinal cord in vitro suggest that the most important lumbar segments for rhythmogenesis are L1-L2, we investigated the role of various lumbar segments in the initiation of walking movements on a treadmill of adult cats spinalized (T13), 5-6 d earlier. The locomotor activities were evaluated from electromyographic and video recordings. The results show that: (1) localized topical application of clonidine in restricted baths over either the L3-L4 or the L5-L7 segments was sufficient to induce walking movements. Yohimbine, an alpha2-noradrenergic antagonist, could block this locomotion when applied over L3-L4 or L5-L7; (2) microinjections of clonidine in one or two lumbar segments from L3 to L5 could also induce locomotion; (3) after an intravenous injection of clonidine, locomotion was blocked by microinjections of yohimbine in segments L3, L4, or L5 but not if the injection was in L6; (4) locomotion was also blocked in all cases by additional spinal transections at L3 or L4. These results show that it is possible to initiate walking in the adult spinal cat with a pharmacological stimulation of a restricted number of lumbar segments and also that the integrity of the L3-L4 segments is necessary to sustain the locomotor activity.

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)0.001
metaresearch head score (Gemma)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.897
Threshold uncertainty score0.572

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.002
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.001
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 head0.067
GPT teacher head0.392
Teacher spread0.325 · 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 designObservational
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

Citations96
Published2000
Admission routes1
Has abstractyes

Explore more

Same venueJournal of NeuroscienceSame topicSpinal Cord Injury ResearchFrench-language works237,207