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Record W2889071181 · doi:10.3389/fendo.2018.00484

Nesfatin-1 Regulates Feeding, Glucosensing and Lipid Metabolism in Rainbow Trout

2018· article· en· W2889071181 on OpenAlexafffund
Ayelén Melisa Blanco, Cristina Velasco, Juan Ignacio Bertucci, José L. Soengas, Suraj Unniappan

Bibliographic record

VenueFrontiers in Endocrinology · 2018
Typearticle
Languageen
FieldAgricultural and Biological Sciences
TopicAquaculture Nutrition and Growth
Canadian institutionsUniversity of Saskatchewan
FundersAgencia Estatal de InvestigaciónXunta de GaliciaNatural Sciences and Engineering Research Council of CanadaSaskatchewan Health Research Foundation
KeywordsRainbow troutLipid metabolismEnergy metabolismMetabolismBiologyZoologyChemistryFisheryEndocrinologyFish <Actinopterygii>

Abstract

fetched live from OpenAlex

Nesfatin-1 is an 82 amino acid peptide which has been involved in a wide variety of physiological functions in both mammals and fish. This study aimed to elucidate the role of nesfatin-1 on rainbow trout food intake, and its putative effects on glucose and fatty acid sensing systems. Intracerebroventricular administration of 25 ng/g nesfatin-1 resulted in a significant inhibition of appetite, likely mediated by the activation of central POMC and CART. Nesfatin-1 stimulated the glucosensing machinery (changes in sglt1, g6pase, gsase, and gnat3 mRNA expression) in the hindbrain and hypothalamus. Central fatty acid sensing mechanisms were unaltered by nesfatin-1, but this peptide altered the expression of mRNAs encoding factors regulating lipid metabolism (fat/cd36, acly, mcd, fas, lpl, pparα and pparγ), suggesting that nesfatin-1 promotes lipid accumulation in the neurons. In the liver, intracerebroventricular nesfatin-1 treatment resulted in decreased capacity for glucose use and lipogenesis and increased the potential of fatty acid oxidation. Altogether, the present results demonstrate that nesfatin-1 is involved in the homeostatic regulation of food intake and metabolism in fish.

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 score0.404
Threshold uncertainty score0.258

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 head0.216
Teacher spread0.205 · 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

Citations27
Published2018
Admission routes2
Has abstractyes

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