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Record W4221122007 · doi:10.1111/acel.13597

Müller Glia maintain their regenerative potential despite degeneration in the aged zebrafish retina

2022· article· en· W4221122007 on OpenAlexfundno aff
Raquel R. Martins, Mazen Zamzam, Dhani Tracey‐White, Mariya Moosajee, Ryan Thummel, Catarina M. Henriques, Ryan B. MacDonald

Bibliographic record

VenueAging Cell · 2022
Typearticle
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicRetinal Development and Disorders
Canadian institutionsnot available
FundersOffice of Extramural Research, National Institutes of HealthBiotechnology and Biological Sciences Research CouncilUniversity of SheffieldMedical Research CouncilRoyal Society of CanadaNational Eye InstituteWellcome Trust
KeywordsZebrafishBiologyRetinaMuller gliaDegeneration (medical)Regeneration (biology)Cell biologyNeuroscienceRetinal degenerationAnatomyPhysiologyPathologyStem cellGeneticsProgenitor cell

Abstract

fetched live from OpenAlex

Ageing is a significant risk factor for degeneration of the retina. Müller glia cells (MG) are key for neuronal regeneration, so harnessing the regenerative capacity of MG in the retina offers great promise for the treatment of age-associated blinding conditions. Yet, the impact of ageing on MG regenerative capacity is unclear. Here, we show that the zebrafish retina undergoes telomerase-independent, age-related neurodegeneration but that this is insufficient to stimulate MG proliferation and regeneration. Instead, age-related neurodegeneration is accompanied by MG morphological aberrations and loss of vision. Mechanistically, yes-associated protein (Yap), part of the Hippo signalling, has been shown to be critical for the regenerative response in the damaged retina, and we show that Yap expression levels decline with ageing. Despite this, morphologically and molecularly altered aged MG retain the capacity to regenerate neurons after acute light damage, therefore, highlighting key differences in the MG response to high-intensity acute damage versus chronic neuronal loss in the zebrafish retina.

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 score0.073
Threshold uncertainty score0.391

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 head0.006
GPT teacher head0.202
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

Citations33
Published2022
Admission routes1
Has abstractyes

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