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Record W2127752236 · doi:10.1242/jeb.203.14.2219

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

2000· article· en· W2127752236 on OpenAlexaff
Susan G. Lund, Matthew Phillips, Christopher D. Moyes, Bruce L. Tufts

Bibliographic record

VenueJournal of Experimental Biology · 2000
Typearticle
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicHeat shock proteins research
Canadian institutionsQueen's University
Fundersnot available
KeywordsRainbow troutRed blood cellBiologyHsp70Heat shock proteinAgeingTroutRed CellBlood cellShock (circulatory)Molecular biologyBiochemistryChemistryInternal medicineImmunologyFish <Actinopterygii>MedicineFisheryGeneGenetics

Abstract

fetched live from OpenAlex

The effects of cell age on protein synthesis were examined in the nucleated red blood cells of rainbow trout (Oncorhynchus mykiss). Total DNA content was unaffected by cell age, whereas total RNA content in young red blood cells was roughly ten times as high as that in old red blood cells. The mRNA levels for haemoglobin, carbonic anhydrase and the chloride/bicarbonate (Cl(-)/HCO(3)(-)) exchanger were also approximately tenfold higher in young red blood cells. Although young red blood cells synthesized roughly five times more protein under steady-state conditions, total protein concentration was not affected by cell age. Despite large reductions in mRNA levels with red blood cell ageing, the concentrations and/or activities of the respiratory proteins were largely preserved. In contrast, the ability to mount a heat shock response was greatly reduced in older red blood cells. Young red blood cells produced 13 times more heat shock protein 70 mRNA following heat shock and four times more 70 kDa protein after recovery. They also transcribed much more heat shock cognate 71 and heat shock factor mRNA than did older red blood cells under steady-state conditions.

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 score0.004
Threshold uncertainty score0.499

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 head0.007
GPT teacher head0.267
Teacher spread0.260 · 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

Citations55
Published2000
Admission routes1
Has abstractyes

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