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IGF‐1 and BDNF promote chick bulbospinal neurite outgrowth in vitro

2005· article· en· W2073428193 on OpenAlexafffund
Rishard Salie, John D. Steeves

Bibliographic record

VenueInternational Journal of Developmental Neuroscience · 2005
Typearticle
Languageen
FieldNeuroscience
TopicNerve injury and regeneration
Canadian institutionsInternational Collaboration On Repair DiscoveriesUniversity of British Columbia
FundersMedical Research CouncilMedical Research Council Canada
KeywordsNeuriteIn vitroNeuroscienceCell biologyBiologyAnatomyBiochemistry

Abstract

fetched live from OpenAlex

Injured neurons in the CNS do not experience significant functional regeneration and so spinal cord insult often results in permanently compromised locomotor ability. The capability of a severed axon to re-grow is thought to depend on numerous factors, one of which is the decreased availability of neurotrophic factors. Application of trophic factors to axotomized neurons has been shown to enhance survival and neurite outgrowth. Although brainstem-spinal connections play a pivotal role in motor dysfunction after spinal cord injury, relatively little is known about the trophic sensitivity of these populations. This study explores the response of bulbospinal populations to various trophic factors. Several growth factors were initially examined for potential trophic effects on the projection neurons of the brainstem. Brain derived neurotrophic factor (BDNF) and insulin-like growth factor (IGF-1) significantly enhance mean process length in both the vestibulospinal neurons and spinal projection neurons from the raphe nuclei. Nerve growth factor (NGF), neurotrophin-4 (NT-4) and glial derived neurotrophic factor (GDNF) did not effect process outgrowth in vestibulospinal neurons. At the developmental stages used in this study, it was determined that receptors for BDNF and IGF-1 were present both on bulbospinal neurons and on surrounding cells with a non-neuronal morphology.

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 score0.017
Threshold uncertainty score0.483

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 communication0.0000.001
Open science0.0010.000
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 head0.023
GPT teacher head0.282
Teacher spread0.259 · 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

Citations34
Published2005
Admission routes2
Has abstractyes

Explore more

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