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Record W1526747027 · doi:10.3389/fnmol.2015.00027

Extrinsic and intrinsic regulation of axon regeneration at a crossroads

2015· article· en· W1526747027 on OpenAlexaff
Andrew H. Kaplan, Stephan Ong Tone, Alyson E. Fournier

Bibliographic record

VenueFrontiers in Molecular Neuroscience · 2015
Typearticle
Languageen
FieldNeuroscience
TopicNerve injury and regeneration
Canadian institutionsMontreal Neurological Institute and Hospital
Fundersnot available
KeywordsAstrogliosisNeuroscienceRegeneration (biology)Spinal cordAxonRemyelinationMyelinSpinal cord injuryBiologyCentral nervous systemNeuroregenerationCell biology

Abstract

fetched live from OpenAlex

Repair of the injured spinal cord is a major challenge in medicine. The limited intrinsic regenerative response mounted by adult central nervous system (CNS) neurons is further hampered by astrogliosis, myelin debris and scar tissue that characterize the damaged CNS. Improved axon regeneration and recovery can be elicited by targeting extrinsic factors as well as by boosting neuron-intrinsic growth regulators. Our knowledge of the molecular basis of intrinsic and extrinsic regulators of regeneration has expanded rapidly, resulting in promising new targets to promote repair. Intriguingly certain neuron-intrinsic growth regulators are emerging as promising targets to both stimulate growth and relieve extrinsic inhibition of regeneration. This crossroads between the intrinsic and extrinsic aspects of spinal cord injury is a promising target for effective therapies for this unmet need.

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: Bench or experimental · Consensus signal: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.002
Threshold uncertainty score0.008

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.000
Science and technology studies0.0000.001
Scholarly communication0.0010.001
Open science0.0010.001
Research integrity0.0010.002
Insufficient payload (model declined to judge)0.0020.001

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 head0.028
GPT teacher head0.254
Teacher spread0.226 · 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

Citations42
Published2015
Admission routes1
Has abstractyes

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