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Axon and Schwann Cell Partnership During Nerve Regrowth

2005· article· en· W2036950807 on OpenAlexaff
Yuan Yuan Chen, David S. McDonald, Chu Cheng, Brooks Magnowski, Douglas W. Zochodne

Bibliographic record

VenueJournal of Neuropathology & Experimental Neurology · 2005
Typearticle
Languageen
FieldNeuroscience
TopicNerve injury and regeneration
Canadian institutionsUniversity of Calgary
Fundersnot available
KeywordsLamininAxonRegeneration (biology)Cell biologySchwann cellNerve guidance conduitBiologyNeuroscienceSciatic nerveAnatomyNetrinAxon guidanceExtracellular matrix

Abstract

fetched live from OpenAlex

Regeneration of peripheral nerve involves an essential contribution by Schwann cells (SCs) in collaboration with regrowing axons. We examined such collaboration between new axons and Schwann cells destined to reform peripheral nerve trucks in a regeneration chamber bridging transected rat sciatic nerves. There was a highly intimate "dance" between axons that followed outgrowing and proliferating SCs. Axons without SCs only grew short distances and almost all axon processes had associated SC processes. When regeneration chambers were infused through an external access port with local mitomycin, a mitosis inhibitor, SC proliferation, migration and subsequent axon regrowth were dramatically reduced. Adding laminin to mitomycin did not reverse this regenerative lag and indicated that SCs provide more than laminin synthesis alone. Laminin infused alone supplemented endogenous laminin and facilitated first SC then axon regrowth. "Wrong way" misdirected axons were associated with misdirected SC processes and were more numerous in bridges exposed to mitomycin, but were fewer in laminin supplemented bridges. Later, by 21 days, there was myelinated axon repopulation of regenerative bridges but those exposed to mitomycin alone at early time points had substantial impairments in axon investment. Reforming peripheral nerve trucks involves a very close and intimate relationship between axons and SCs that must proliferate and migrate, facilitated by laminin.

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 score0.021
Threshold uncertainty score0.851

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
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 head0.023
GPT teacher head0.273
Teacher spread0.250 · 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

Citations166
Published2005
Admission routes1
Has abstractyes

Explore more

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