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Record W2772426563 · doi:10.1038/s41419-017-0074-8

In retinitis pigmentosa TrkC.T1-dependent vectorial Erk activity upregulates glial TNF-α, causing selective neuronal death

2017· article· en· W2772426563 on OpenAlexafffund
Alba Galán, Sean Jmaeff, Pablo F. Barcelona, Fouad Brahimi, Marinko V. Šarunic, H. Uri Saragovi

Bibliographic record

VenueCell Death and Disease · 2017
Typearticle
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicRetinal Development and Disorders
Canadian institutionsMcGill University Health CentreSimon Fraser UniversityMcGill UniversityJewish General Hospital
FundersCanadian Institutes of Health Research
KeywordsRetinitis pigmentosaMAPK/ERK pathwayTumor necrosis factor alphaMedicineCell biologyBiologyNeuroscienceSignal transductionImmunologyRetina

Abstract

fetched live from OpenAlex

In some diseases the TrkC.T1 isoform is upregulated in glia, associated with glial TNF-α production and neuronal death. What remains unknown are the activating signals in glia, and how paracrine signals may be selective for a targeted neuron while sparing other proximate neurons. We studied these questions in the retina, where Müller glia contacts photoreceptors on one side and retinal ganglion cells on the other. In a mutant Rhodopsin mouse model of retinitis pigmentosa (RP) causing progressive photoreceptor death-but sparing retinal ganglion cells-TrkC.T1 and NT-3 ligand are upregulated in Müller glia. TrkC.T1 activity generates p-Erk, which causes increased TNF-α. These sequential events take place predominantly in Müller fibers contacting stressed photoreceptors, and culminate in selective death. Each event and photoreceptor death can be prevented by reduction of TrkC.T1 expression, by pharmacological antagonism of TrkC or by pharmacological inhibition Erk. Unmasking the sequence of non-cell autologous events and mechanisms causing selective neuronal death may help rationalize therapies.

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: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.155
Threshold uncertainty score0.895

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 head0.249
Teacher spread0.238 · 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 designObservational
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

Citations15
Published2017
Admission routes2
Has abstractyes

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