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Record W2791471426 · doi:10.1002/glia.23327

<scp>A</scp> predominantly glial origin of axonal ribosomes after nerve injury

2018· article· en· W2791471426 on OpenAlexaff
Kerstin Müller, Andrea Schnatz, Miriam Schillner, Simone Woertge, Christina Müller, Ilse von Graevenitz, Ari Waisman, Jan van Minnen, Christina Francisca Vogelaar

Bibliographic record

VenueGlia · 2018
Typearticle
Languageen
FieldNeuroscience
TopicNerve injury and regeneration
Canadian institutionsUniversity of Calgary
FundersDeutsche Forschungsgemeinschaft
KeywordsBiologyRibosomeAxonCell biologyNerve injurySciatic nerveSciatic nerve injuryMessenger RNASchwann cellNeuroscienceRegeneration (biology)AnatomyBiochemistryRNAGene

Abstract

fetched live from OpenAlex

Axonal mRNA transport and local protein synthesis are crucial for peripheral axon regeneration. To date, it remains unclear how ribosomes localize to axons. They may be co-transported with mRNAs or, as suggested by recent studies, transferred from Schwann cells (SC). Here, we generated transgenic "RiboTracker" mice expressing tdTomato-tagged ribosomal protein L4 in specific cell types when crossed with Cre lines. Two neuronal RiboTracker-Cre lines displayed extremely low levels of axonal L4-tdTomato-positive ribosomes. In contrast, two glial RiboTracker-Cre lines revealed tagged ribosomes in sciatic nerve (SN) axons with increasing amounts after injury. Furthermore, non-RiboTracker dorsal root ganglia co-cultured with L4-tdTomato-expressing SCs displayed tagged ribosomes in axons. These data provide unequivocal evidence that SN axons receive ribosomes from SCs upon injury and indicate that glial cells are the main source of axonal ribosomes.

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 score0.009

Distilled classifier scores by category (both heads)

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 integrity0.0000.001
Insufficient payload (model declined to judge)0.0030.001

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 head0.021
GPT teacher head0.278
Teacher spread0.257 · 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

Citations32
Published2018
Admission routes1
Has abstractyes

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