MétaCan
Menu
Back to cohort
Record W2123605135 · doi:10.1242/jeb.203.6.1039

The Effects of Cell Ageing on Metabolism in Rainbow Trout (<i>Oncorhynchus Mykiss</i>) Red Blood Cells

2000· article· en· W2123605135 on OpenAlexaff
Matthew Phillips, C. D. Moyes, Bruce L. Tufts

Bibliographic record

VenueJournal of Experimental Biology · 2000
Typearticle
Languageen
FieldImmunology and Microbiology
TopicAquaculture disease management and microbiota
Canadian institutionsQueen's University
Fundersnot available
KeywordsRainbow troutAgeingMetabolismBiologyRed blood cellTroutBlood cellZoologyFish <Actinopterygii>FisheryEndocrinologyBiochemistryImmunologyGenetics

Abstract

fetched live from OpenAlex

The effects of cell age on metabolism in the nucleated red blood cells of rainbow trout (Oncorhynchus mykiss) were examined. Red blood cells were separated according to age using fixed-angle centrifugation. The mean erythrocyte haemoglobin concentration in old red blood cells was found to be 120 % of that in young red blood cells. In young red blood cells, the activities of the mitochondrial enzymes citrate synthase and cytochrome oxidase were 135-200 %, respectively, of those measured in old red blood cells. The activity of the glycolytic enzyme lactate dehydrogenase in young red blood cells was 170 % of that in old red blood cells, whereas the activity of the glycolytic enzyme pyruvate kinase was not significantly affected by cell age. In addition, young red blood cells consumed over twice as much O(2) and devoted 50 % more O(2) to protein synthesis and the activity of Na(+)/K(+)-ATPase than old red blood cells. Red blood cell age did not significantly affect the rate of lactate production. This study shows that ageing in rainbow trout nucleated red blood cells is accompanied by a significant decline in aerobic energy production and the processes it supports, as well as a corresponding increase in the glycolytic contribution to metabolism.

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 score0.039
Threshold uncertainty score0.568

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 head0.004
GPT teacher head0.223
Teacher spread0.219 · 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

Citations70
Published2000
Admission routes1
Has abstractyes

Explore more

Same venueJournal of Experimental BiologySame topicAquaculture disease management and microbiotaFrench-language works237,207