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Suppression of p75NTR Does Not Promote Regeneration of Injured Spinal Cord in Mice

2004· article· en· W2088216857 on OpenAlexfundno aff
Xing‐Yun Song, Jinhua Zhong, Xin Wang, Xin‐Fu Zhou

Bibliographic record

VenueJournal of Neuroscience · 2004
Typearticle
Languageen
FieldNeuroscience
TopicNerve injury and regeneration
Canadian institutionsnot available
FundersNational Health and Medical Research CouncilMedical Research CouncilFlinders UniversityYork University
KeywordsSpinal cordCorticospinal tractNeuriteRegeneration (biology)NeuroscienceGDF7Spinal cord injuryMyelinSensory systemBiologyPyramidal tractsAxonNeurotrophinLow-affinity nerve growth factor receptorCentral nervous systemCell biologyReceptorMedicineIn vitro

Abstract

fetched live from OpenAlex

The neurotrophin receptor p75NTR is the coreceptor for Nogo receptor, mediating growth cone collapse in vitro by MAG, myelin oligodendrocyte glycoprotein (Omgp), and Nogo. Whether p75NTR plays any role in the failure of nerve regeneration in vivo is not known. Immunohistochemical data showed that p75NTR was expressed in only a very small subset of ascending sensory axons but not in any corticospinal axons in the dorsal column of either normal or injured spinal cord. Using p75NTR-deficient mice, we showed that the depletion of the functional p75NTR did not promote the regeneration of the descending corticospinal tract and ascending sensory neurons in the spinal cord 2 weeks after spinal cord injury. Local administration of p75NTR-Fc fusion molecule, the dominant-negative receptor to block the function of neurite outgrowth inhibitors, did not improve regeneration of ascending sensory neurons in the injured spinal cord. Our results suggest that p75NTR may not be a critical molecule mediating the function of myelin-associated inhibitory factors in vivo.

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)0.000
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 score0.003
Threshold uncertainty score0.011

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0020.000
Science and technology studies0.0000.001
Scholarly communication0.0010.001
Open science0.0010.000
Research integrity0.0010.002
Insufficient payload (model declined to judge)0.0030.002

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 head0.042
GPT teacher head0.320
Teacher spread0.278 · 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

Citations104
Published2004
Admission routes1
Has abstractyes

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