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Record W2943537425 · doi:10.1002/dvg.23301

Pharmacological evidence for the role of RAR in axon guidance and embryonic development of a protostome species

2019· article· en· W2943537425 on OpenAlexafffund
Alysha Johnson, Eric de Hoog, Michael Tolentino, Tamara Israa-Nadine Nasser, Gaynor E. Spencer

Bibliographic record

Venuegenesis · 2019
Typearticle
Languageen
FieldNeuroscience
TopicNeurobiology and Insect Physiology Research
Canadian institutionsBrock University
FundersNatural Sciences and Engineering Research Council of Canada
KeywordsRetinoic acidBiologyLymnaea stagnalisVertebrateRetinoic acid receptorCell biologyRetinoidEcologyGeneticsSnailCell culture

Abstract

fetched live from OpenAlex

Retinoic acid (RA), the active metabolite of vitamin A, functions through nuclear receptors, one of which is the retinoic acid receptor (RAR). Though the RAR is essential for various aspects of vertebrate development, little is known about the role of RAR in nonchordate invertebrates. Here, we examined the potential role of an invertebrate RAR in mediating chemotropic effects of retinoic acid. The RAR of the protostome Lymnaea stagnalis is present in the growth cones of regenerating cultured motorneurons, and a synthetic RAR agonist (EC23), was able to mimic the effects of retinoic acid in inducing growth cone turning. We also examined the ability of the natural retinoids, all-trans RA and 9-cis RA, as well as the synthetic RAR agonists, to disrupt embryonic development in Lymnaea. Developmental defects included delays in embryo hatching, arrested eye, and shell development, as well as more severe abnormalities such as halted development. Developmental defects induced by some (but not all) synthetic RAR agonists were found to mimic those induced by addition of high concentrations of the natural retinoid isomers. These pharmacological data support a possible physiological role for the RAR in axon guidance and embryonic development of an invertebrate protostome species.

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 score0.168
Threshold uncertainty score0.167

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 head0.102
GPT teacher head0.354
Teacher spread0.252 · 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

Citations6
Published2019
Admission routes2
Has abstractyes

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