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Record W2076150419 · doi:10.1086/425205

Cross‐Tolerance in the Tidepool Sculpin: The Role of Heat Shock Proteins

2005· article· en· W2076150419 on OpenAlexafffund
Anne E. Todgham, Patricia M. Schulte, George K. Iwama

Bibliographic record

VenuePhysiological and Biochemical Zoology · 2005
Typearticle
Languageen
FieldEnvironmental Science
TopicPhysiological and biochemical adaptations
Canadian institutionsUniversity of British Columbia
FundersNatural Sciences and Engineering Research Council of CanadaKillam Trusts
KeywordsCross-toleranceBiologyHeat shock proteinStressorShock (circulatory)Hsp70Heat shockEcologyZoologyInternal medicineGeneticsMedicine

Abstract

fetched live from OpenAlex

Cross-tolerance, or the ability of one stressor to transiently increase tolerance to a second heterologous stressor, is thought to involve the induction of heat shock proteins (Hsp). We thus investigated the boundaries of cross-tolerance in tidepool sculpins (Oligocottus maculosus) and their relationship to Hsp70 levels. Survival of sculpins exposed to severe osmotic (90 ppt, 2 h) and hypoxic (0.33 mg O(2)/L, 2 h) stressors increased from 68% to 96%, and from 47% to 76%, respectively, following a +12 degrees C heat shock. The magnitude of this heat shock was critical for protection. A +10 degrees C heat shock did not confer cross-tolerance, while a +15 degrees C heat shock was deleterious. Sculpins required between 8 and 48 h of recovery following the +12 degrees C heat shock to develop cross-tolerance. There was no association between Hsp70 levels before the onset of the secondary stressor and cross-tolerance. However, branchial Hsp70 levels following osmotic shock were highly correlated with the time frame of cross-tolerance. Thus, Hsp70 induction by the priming stressor may be less important than the ability of the cell to mount an Hsp response to subsequent stressors. The time frame of cross-tolerance is similar to the interval between low tides, suggesting the possible relevance of this response in nature.

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 score0.726
Threshold uncertainty score0.516

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 studies0.0000.001
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 head0.013
GPT teacher head0.233
Teacher spread0.220 · 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

Citations147
Published2005
Admission routes2
Has abstractyes

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