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Record W2110585132 · doi:10.1017/s0317167100009331

Peripheral Sensory Axon Growth: From Receptor Binding to Cellular Signaling

2008· review· en· W2110585132 on OpenAlexafffundvenue
Budd A. Tucker, Karen M. Mearow

Bibliographic record

VenueCanadian Journal of Neurological Sciences / Journal Canadien des Sciences Neurologiques · 2008
Typereview
Languageen
FieldNeuroscience
TopicNerve injury and regeneration
Canadian institutionsMemorial University of Newfoundland
FundersNatural Sciences and Engineering Research Council of CanadaCanadian Institutes of Health Research
KeywordsDorsal root ganglionNeurotrophinNeuroscienceSensory neuronAxonNerve growth factorSensory systemRegeneration (biology)Peripheral nervous systemExtracellularBiologyNeurotrophic factorsExtracellular matrixNervous systemPeripheral nerve injuryCentral nervous systemCell biologyReceptor

Abstract

fetched live from OpenAlex

Regeneration following axonal injury of the adult peripheral sensory nervous system is heavily influenced by factors located in a neuron's extracellular environment. These factors include neurotrophins, such as Nerve Growth Factor (NGF) and the extracellular matrix, such as laminin. The presence of these molecules in the peripheral nervous system (PNS) is a major contributing factor for the dichotomy between regenerative capacities of central vs. peripheral neurons. Although PNS neurons are capable of spontaneous regeneration, this response is critically dependent on many different factors including the type, location and severity of the injury. In this article, we will focus on the plasticity of adult dorsal root ganglion (DRG) sensory neurons and how trophic factors and the extracellular environment stimulate the activation of intracellular signaling cascades that promote axonal growth in adult dorsal root ganglion neurons.

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)0.001
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: Not applicable · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.003
Threshold uncertainty score0.011

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0020.000
Bibliometrics0.0020.002
Science and technology studies0.0000.001
Scholarly communication0.0020.002
Open science0.0010.001
Research integrity0.0020.002
Insufficient payload (model declined to judge)0.0030.005

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 head0.103
GPT teacher head0.304
Teacher spread0.201 · 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 designNot applicable
Domainnot available
GenreReview

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

Citations52
Published2008
Admission routes3
Has abstractyes

Explore more

Same venueCanadian Journal of Neurological Sciences / Journal Canadien des Sciences NeurologiquesSame topicNerve injury and regenerationFrench-language works237,207