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Record W2103045057 · doi:10.1016/j.celrep.2014.06.014

Modifying Lipid Rafts Promotes Regeneration and Functional Recovery

2014· article· en· W2103045057 on OpenAlexafffund
Nardos G. Tassew, Andrea J. Mothe, Alireza P. Shabanzadeh, Paromita Banerjee, Paulo D. Koeberle, Rod Bremner, Charles H. Tator, Philippe P. Monnier

Bibliographic record

VenueCell Reports · 2014
Typearticle
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicRNA Interference and Gene Delivery
Canadian institutionsLunenfeld-Tanenbaum Research InstituteMount Sinai HospitalCanada Research ChairsUniversity of Toronto
FundersCanadian Institutes of Health Research
KeywordsLipid raftRegeneration (biology)Cell biologyChemistryBiologyComputational biologySignal transduction

Abstract

fetched live from OpenAlex

Ideal strategies to ameliorate CNS damage should promote both neuronal survival and axon regeneration. The receptor Neogenin promotes neuronal apoptosis. Its ligand prevents death, but the resulting repulsive guidance molecule a (RGMa)-Neogenin interaction also inhibits axonal growth, countering any prosurvival benefits. Here, we explore strategies to inhibit Neogenin, thus simultaneously enhancing survival and regeneration. We show that bone morphogenetic protein (BMP) and RGMa-dependent recruitment of Neogenin into lipid rafts requires an interaction between RGMa and Neogenin subdomains. RGMa or Neogenin peptides that prevent this interaction, BMP inhibition by Noggin, or reduction of membrane cholesterol all block Neogenin raft localization, promote axon outgrowth, and prevent neuronal apoptosis. Blocking Neogenin raft association influences axonal pathfinding, enhances survival in the developing CNS, and promotes survival and regeneration in the injured adult optic nerve and spinal cord. Moreover, lowering cholesterol disrupts rafts and restores locomotor function after spinal cord injury. These data reveal a unified strategy to promote both survival and regeneration in the CNS.

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 score0.020
Threshold uncertainty score0.326

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 integrity0.0000.000
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 head0.011
GPT teacher head0.208
Teacher spread0.197 · 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

Citations62
Published2014
Admission routes2
Has abstractyes

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