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Record W2067522902 · doi:10.1089/neu.2012.2734

Dorsolateral Funiculus Lesioning of the Mouse Cervical Spinal Cord at C4 but Not at C6 Results in Sustained Forelimb Motor Deficits

2013· article· en· W2067522902 on OpenAlexafffund
Brett J. Hilton, Peggy Assinck, Greg J. Duncan, Daniel C. Lu, Stephanie W. Lo, Wolfram Tetzlaff

Bibliographic record

VenueJournal of Neurotrauma · 2013
Typearticle
Languageen
FieldMedicine
TopicSpinal Cord Injury Research
Canadian institutionsInternational Collaboration On Repair DiscoveriesUniversity of British Columbia
FundersCanadian Institutes of Health Research
KeywordsForelimbBiotinylated dextran amineSpinal cordSpinal cord injuryCrush injuryAnterograde tracingLesionHindlimbAnatomyCorticospinal tractNeuroscienceCentral nervous systemMedicineBiologySurgeryMagnetic resonance imaging

Abstract

fetched live from OpenAlex

Although upper extremity functional recovery is a high priority for spinal cord injured patients with cervical injuries, few injury models have been developed in mice with sustained deficits in forelimb motor function. Here, we characterize a dorsolateral funiculus (DLF) crush model in mice, which ablates the rubrospinal tract (RST) unilaterally and thus allows correlation of motor recovery to axonal regeneration in the assessment of molecular regeneration targets. We conducted unilateral DLF crush injuries at cervical levels C4 and C6 and assessed motor recovery in a battery of tests: the rearing test of forelimb asymmetry, the grooming test, staircase pellet reaching, a horizontal ladder task, and CatWalk gait analysis. All tasks revealed lesion effects on forepaw function when DLF crush was instigated at level C4, but deficits were generally only transient in mice with DLF crush performed at level C6. Anterograde tracing of the RST with biotinylated dextran amine revealed the tract's complete ablation. The characterization of a C4 DLF model in mice provides an important tool for assessing molecular regeneration targets to promote functional recovery after spinal cord injury.

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: Bench or experimental · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.850
Threshold uncertainty score0.610

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0010.000
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 head0.089
GPT teacher head0.363
Teacher spread0.273 · 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

Citations38
Published2013
Admission routes2
Has abstractyes

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