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Record W1553160036 · doi:10.2331/fishsci.68.sup2_1037

Effects of cold shock and cortisol on heat shock protein levels in rainbow trout

2002· article· en· W1553160036 on OpenAlexafffund
IOSHIKI NAKANO, Niladri Basu, Toshiyasu Yamaguchi, Minoru Sato, Kazumi Nakano, Jason Hicks, George K. Iwama

Bibliographic record

VenueFisheries Science · 2002
Typearticle
Languageen
FieldEnvironmental Science
TopicPhysiological and biochemical adaptations
Canadian institutionsInstitute for Marine BiosciencesUniversity of British Columbia
FundersNatural Sciences and Engineering Research Council of CanadaUniversity of British ColumbiaMinistry of Education, Culture, Sports, Science and Technology
KeywordsRainbow troutEndocrinologyHeat shock proteinInternal medicineHsp70Shock (circulatory)BiologyHydrocortisoneCold stressEndocrine systemFish <Actinopterygii>HormoneMedicineBiochemistryFishery

Abstract

fetched live from OpenAlex

The effect of cold shock and increased levels of circulating cortisol on heat shock proteins (Hsp) levels in rainbow trout (Oncorhynchus mykiss) were examined. Cold shock: When the cultured hepatocytes were exposed to cold (at 4°C for 2h), Hsp 70 level in hepatocytes were similar to that in control. However, Hsp 30 was markedly induced by cold shock. Cortisol: The high concentration of circulating cortisol (pharmacological levels) was found to reduce the Hsp 70 levels in liver and gill of cortisol implanted fish (50 μg cortisol/g body weight) exposed to heat shock (at 22°C for 2h) compared to the sham. These results suggest that the expressions of Hsp 30 and 70 in cell may be affected by cold stress response and circulating cortisol levels in fish, respectively. Furthermore, cellular stress response, such as Hsp expression, might be related with neuroendocrine/endocrine system 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 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 score0.004
Threshold uncertainty score0.008

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 head0.020
GPT teacher head0.204
Teacher spread0.184 · 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

Citations1
Published2002
Admission routes2
Has abstractyes

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