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Record W2153779542 · doi:10.3109/10715762.2012.698009

Oxidative stress in growth hormone transgenic coho salmon with compressed lifespan – a model for addressing aging

2012· article· en· W2153779542 on OpenAlexafffund
Bethanie Carney Almroth, Jörgen I. Johnsson, Robert H. Devlin, Joachim Sturve

Bibliographic record

VenueFree Radical Research · 2012
Typearticle
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicAnimal Genetics and Reproduction
Canadian institutionsFisheries and Oceans Canada
FundersSkogs- och Jordbrukets ForskningsrådAGE-WELL
KeywordsOxidative stressGlutathioneAntioxidantReactive oxygen speciesTransgeneEndocrinologyInternal medicineEnzyme assayHormoneAgeingEnzymeGenetically modified mouseBiologyOncorhynchusChemistryFish <Actinopterygii>BiochemistryFisheryGeneMedicine

Abstract

fetched live from OpenAlex

Growth hormone (GH) transgenic fish have dramatically enhanced growth rates, increased oxygen demands and reactive oxygen species production. GH-transgenic coho salmon provide an opportunity to address effects of increased metabolism on physiological aging. The objective of this study was to compare oxidative stress in wild-type (WT) and GH-transgenic (T) coho salmon (Oncorhynchus kisutch) of different ages (1 and 2 years). Antioxidant enzyme activity, protein carbonyls (PC) and glutathione (GSH, GSSG) were measured. PC correlated to growth rates in individual fish. T fish exhibited lower antioxidant enzyme activities and GSH levels compared to the WT, while levels of PC and GSSG were higher. Age affects were observed in both WT and T fish; enzyme activities and GSH decreased while PC and GSSG increased. Our results support the metabolic rate theory of aging. This study aims to be a platform for continued studies of the theories of aging using fish as model organisms.

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 machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: metacan-v3-hybrid-931329e0061cValidation 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 score0.003
Threshold uncertainty score0.007

Distilled classifier scores by category (both heads)

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 integrity0.0000.001
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 head0.094
GPT teacher head0.365
Teacher spread0.271 · 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 source (direct Gemma or distilled Codex), 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

Citations28
Published2012
Admission routes2
Has abstractyes

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