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Extracellular ADP regulates lesion‐induced<i>in vivo</i>cell proliferation and death in the zebrafish retina

2009· article· en· W2070809056 on OpenAlexfundno aff
Ariadna G. Battista, María Jimena Ricatti, Diego E. Pafundo, Marien A. Gautier, María Paula Faillace

Bibliographic record

VenueJournal of Neurochemistry · 2009
Typearticle
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicAdenosine and Purinergic Signaling
Canadian institutionsnot available
FundersCentre Hospitalier Universitaire de Québec
KeywordsPurinergic receptorCell biologyZebrafishRetinaBiologyExtracellularProgrammed cell deathPurinergic signallingParacrine signallingNeuroscienceReceptorAdenosine receptorBiochemistry

Abstract

fetched live from OpenAlex

Regeneration and growth that occur in the adult teleost retina by neurogenesis have been helpful in identifying molecular and cellular mechanisms underlying cell proliferation and differentiation. In this report, we demonstrate that endogenous purinergic signals regulate cell proliferation induced by a cytotoxic injury of the adult zebrafish retina which mainly damages inner retinal layers. Particularly, we found that ADP but not ATP or adenosine significantly enhanced cell division as assessed by 5-bromo-2'-deoxyuridine incorporation following injury, during the degenerative and proliferative phase of the regeneration process. This effect of ADP occurs via P2Y1 metabotropic receptors as shown by intra-ocular injection of selective antagonists. Additionally, we describe a role for purinergic signals in regulating cell death induced by injury. Scavenging of extracellular nucleotides significantly increased cell death principally seen in the inner retinal layers. This effect is partially reproduced by blocking P2Y1 receptors suggesting a neuroprotective function for ADP, which is derived from extracellular ATP probably released by dying cells as a consequence of the ouabain treatment. This study demonstrates a crucial role for ADP as a paracrine signal in the repair of retinal tissue following injury.

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 score0.012
Threshold uncertainty score0.371

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 head0.014
GPT teacher head0.245
Teacher spread0.230 · 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

Citations40
Published2009
Admission routes1
Has abstractyes

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