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Record W3033281992 · doi:10.3390/ijms21114100

Tau Gene Deletion Does Not Influence Axonal Regeneration and Retinal Neuron Survival in the Injured Mouse Visual System

2020· article· en· W3033281992 on OpenAlexafffund
Léa Rodriguez, Sandrine Joly, Julius Baya Mdzomba, Vincent Pernet

Bibliographic record

VenueInternational Journal of Molecular Sciences · 2020
Typearticle
Languageen
FieldNeuroscience
TopicNerve injury and regeneration
Canadian institutionsUniversité Laval
FundersFonds de Recherche du Québec - SantéNatural Sciences and Engineering Research Council of Canada
KeywordsCiliary neurotrophic factorAxonBiologyRetinalOptic nerveRegeneration (biology)Retinal ganglion cellRetinaNeuronCell biologyNeuroscienceNeuriteAxoplasmic transportNeurotrophic factorsAnatomyGenetics

Abstract

fetched live from OpenAlex

In the present study, we hypothesized that the microtubule-associated protein Tau may influence retinal neuron survival and axonal regeneration after optic nerve injury. To test this hypothesis, the density of retinal ganglion cells was evaluated by immunostaining retinal flat-mounts for RNA-binding protein with multiple splicing (RBPMS) two weeks after optic nerve micro-crush lesion in Tau-deprived (Tau knock-out (KO)) and wild-type (WT) mice. Axon growth was determined on longitudinal sections of optic nerves after anterograde tracing. Our results showed that the number of surviving retinal ganglion cells and growing axons did not significantly vary between WT and Tau KO animals. Moreover, sustained activation of the neuronal growth program with ciliary neurotrophic factor (CNTF) resulted in a similar increase in surviving neurons and in growing axons in WT and Tau KO mice. Taken together, our data suggest that Tau does not influence axonal regeneration or neuronal survival.

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 score0.001
Threshold uncertainty score0.005

Distilled classifier scores by category (both heads)

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

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 head0.025
GPT teacher head0.289
Teacher spread0.265 · 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

Citations7
Published2020
Admission routes2
Has abstractyes

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Same venueInternational Journal of Molecular Sciences→Same topicNerve injury and regeneration→French-language works237,207→