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Record W2181276919 · doi:10.1111/pcmr.12442

Pharmacological induction of skin pigmentation unveils the neuroendocrine circuit regulated by light

2015· article· en· W2181276919 on OpenAlexafffund
Gabriel E. Bertolesi, Sherene T. Vazhappilly, Carrie L. Hehr, Sarah McFarlane

Bibliographic record

VenuePigment Cell & Melanoma Research · 2015
Typearticle
Languageen
FieldBiochemistry, Genetics and Molecular Biology
Topicmelanin and skin pigmentation
Canadian institutionsAlberta Children's HospitalUniversity of Calgary
FundersNatural Sciences and Engineering Research Council of CanadaAlberta InnovatesMelanoma Research Alliance
KeywordsMelanopsinBiologyXenopusCell biologyIntrinsically photosensitive retinal ganglion cellsMelaninReceptorNeuroscienceEndocrinologyInternal medicinePhotopigmentRetinaRetinal ganglion cellMedicineBiochemistry

Abstract

fetched live from OpenAlex

Light-regulated skin colour change is an important physiological process in invertebrates and lower vertebrates, and includes daily circadian variation and camouflage (i.e. background adaptation). The photoactivation of melanopsin-expressing retinal ganglion cells (mRGCs) in the eye initiates an uncharacterized neuroendocrine circuit that regulates melanin dispersion/aggregation through the secretion of alpha-melanocyte-stimulating hormone (α-MSH). We developed experimental models of normal or enucleated Xenopus embryos, as well as in situ cultures of skin of isolated dorsal head and tails, to analyse pharmacological induction of skin pigmentation and α-MSH synthesis. Both processes are triggered by a melanopsin inhibitor, AA92593, as well as chloride channel modulators. The AA9253 effect is eye-dependent, while functional data in vivo point to GABAA receptors expressed on pituitary melanotrope cells as the chloride channel blocker target. Based on the pharmacological data, we suggest a neuroendocrine circuit linking mRGCs with α-MSH secretion, which is used normally during background adaptation.

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)0.001
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 score0.038
Threshold uncertainty score0.471

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
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 head0.071
GPT teacher head0.351
Teacher spread0.281 · 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

Citations16
Published2015
Admission routes2
Has abstractyes

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