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Application of Rho Antagonist to Neuronal Cell Bodies Promotes Neurite Growth in Compartmented Cultures and Regeneration of Retinal Ganglion Cell Axons in the Optic Nerve of Adult Rats

2005· article· en· W2084733055 on OpenAlexafffund
Johanne Bertrand, Matthew J. Winton, Nieves Rodriguez-Hernandez, Robert B. Campenot, Lisa McKerracher

Bibliographic record

VenueJournal of Neuroscience · 2005
Typearticle
Languageen
FieldNeuroscience
TopicNerve injury and regeneration
Canadian institutionsUniversity of AlbertaUniversité de Montréal
FundersCanadian Institutes of Health Research
KeywordsAxotomyRetinal ganglion cellNeuriteAxonOptic nerveCell biologyBiologyGrowth coneAxoplasmic transportRegeneration (biology)NeuroprotectionRetinaNeuroscienceIn vitroBiochemistry

Abstract

fetched live from OpenAlex

Inactivation of Rho promotes neurite growth on inhibitory substrates and axon regeneration in vivo. Here, we compared axon growth when neuronal cell bodies or injured axons were treated with a cell-permeable Rho antagonist (C3-07) in vitro and in vivo. In neurons plated in compartmented cultures, application of C3-07 to either cell bodies or distal axons promoted axonal growth on myelin-associated glycoprotein substrates. In vivo, an injection of C3-07 into the eye promoted regeneration of retinal ganglion cell (RGC) axons in the optic nerve after microcrush lesion. Delayed application of C3-07 promoted RGC growth across the lesion scar. Application of C3-07 completely prevented RGC cell death for 1 week after axotomy. To investigate the mechanism by which Rho inactivation promotes RGC growth, we studied slow axonal transport. Reduction in slow transport of cytoskeletal proteins was observed after axotomy, but inactivation of Rho did not increase slow axonal transport rates. Together, our results indicate that application of a Rho antagonist at the cell body is neuroprotective and overcomes growth inhibition but does not fully prime RGCs for active growth.

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 score0.028
Threshold uncertainty score0.355

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
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 head0.019
GPT teacher head0.274
Teacher spread0.255 · 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

Citations181
Published2005
Admission routes2
Has abstractyes

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