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Spontaneous locomotor recovery in spinal cord injured rats is accompanied by anatomical plasticity of reticulospinal fibers

2006· article· en· W1973726191 on OpenAlexaff
Mark Ballermann, Karim Fouad

Bibliographic record

VenueEuropean Journal of Neuroscience · 2006
Typearticle
Languageen
FieldMedicine
TopicSpinal Cord Injury Research
Canadian institutionsUniversity of Alberta
Fundersnot available
KeywordsSpinal cord injurySpontaneous recoverySproutingSpinal cordHindlimbLesionMedicineAnatomyCorticospinal tractCordPyramidal tractsNeuroscienceBiologyAnesthesiaSurgeryDiffusion MRIMagnetic resonance imaging

Abstract

fetched live from OpenAlex

Although injured axons in mammalian spinal cords do not regenerate, some recovery of locomotor function following incomplete injury can be observed in patients and animal models. Following a lateral hemisection injury of the thoracic spinal cord, rats spontaneously recover weight-bearing stepping in the hind limb ipsilateral to the injury. The mechanisms behind this recovery are not completely understood. Plasticity in the reticulospinal tract (RtST), the tract responsible for the initiation of walking, has not been studied. In this study, rats received lateral thoracic hemisection of the spinal cord, and RtST projections were compared in two groups of rats, one early in recovery (7 days) and the other at a time point when weight-bearing stepping was fully regained (42 days). We found that this recovery occurs in parallel with increased numbers of collaterals of spared RtST fibers entering the intermediate lamina below the injury at L2. Sprouting of injured RtST fibers above the lesion was not found. In conclusion, our study suggests that sprouting of spared RtST fibers might be involved in the recovery of locomotion following incomplete 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 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 score0.002
Threshold uncertainty score0.005

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0010.000
Science and technology studies0.0000.001
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0020.000

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 head0.033
GPT teacher head0.326
Teacher spread0.293 · 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

Citations274
Published2006
Admission routes1
Has abstractyes

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