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Record W2043425697 · doi:10.5402/2012/257841

Characterization of Stanniocalcin-1 Receptors in the Rainbow Trout

2012· article· en· W2043425697 on OpenAlexafffund
Timothy D. J. Richards, Amanda L. Fenton, Rahma Syed, Graham F. Wagner

Bibliographic record

VenueISRN Endocrinology · 2012
Typearticle
Languageen
FieldEnvironmental Science
TopicPhysiological and biochemical adaptations
Canadian institutionsWestern University
FundersNatural Sciences and Engineering Research Council of CanadaUniversidade de Santiago de Compostela
KeywordsRainbow troutReceptorZoologyBiologyChemistryFisheryFish <Actinopterygii>Biochemistry

Abstract

fetched live from OpenAlex

Mammalian stanniocalcin-1 (STC-1) is one of several ligands targeted to mitochondria. High affinity STC-1 receptors are present on the mitochondrial membranes of nephron cells, myocytes, and hepatocytes, to enable ligand sequestration within the matrix. However, STC-1 receptors have not been characterized in fish. Nor is it known if mitochondrial targeting occurs in fish. The aim of the study was to address these questions. Saturation binding assays were carried out to obtain estimates of K(D) and B(max). They revealed the presence of saturable, high-affinity receptors on both membranes and mitochondria of liver, muscle, and gill filament. In situ ligand binding (ISLB) was used to localize receptors at the histological level and revealed some unexpected findings. In cranium, for instance, receptors were found mainly in the cartilage matrix, as opposed to the chondrocytes. In brain, the majority of receptors were located on neuropil areas as opposed to neuronal cell bodies. In skeletal muscle, receptors were confined to periodic striations, tentatively identified as the Z lines. Receptors were even found on STC-1 producing corpuscles of Stannius cells, raising the possibility of there being an autocrine feedback loop or, perhaps, a soluble binding protein that is released with the ligand to regulate its bioavailability.

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: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.841
Threshold uncertainty score0.724

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)0.0010.000

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 head0.022
GPT teacher head0.245
Teacher spread0.223 · 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

Citations8
Published2012
Admission routes2
Has abstractyes

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