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Record W2174158226 · doi:10.1139/z10-107

Maturation increases superoxide radical production without increasing oxidative damage in the skeletal muscle of hooded seals (Cystophora cristata)

2011· article· en· W2174158226 on OpenAlexvenueno aff
José Pablo Vázquez‐Medina, Norma Olimpia Olguín-Monroy, Perla D. Maldonado, Abel Santamarı́a, Mina Königsberg, Robert Elsner, Michael O. Hammill, Jennifer M. Burns, Tania Zenteno‐Savín

Bibliographic record

VenueCanadian Journal of Zoology · 2011
Typearticle
Languageen
FieldEarth and Planetary Sciences
TopicOcean Acidification Effects and Responses
Canadian institutionsnot available
Fundersnot available
KeywordsBiologyOxidative stressReactive oxygen speciesSkeletal muscleOxidative phosphorylationOxidative damageSuperoxide dismutaseBiochemistryCell biologyInternal medicineEndocrinology

Abstract

fetched live from OpenAlex

Diving vertebrates represent unique models for the study of the physiological responses to reactive oxygen species (ROS) production and oxidative stress because of their adaptability to cope with dive-derived ROS production. We hypothesized that in the skeletal muscle of a diving mammal, the hooded seal ( Cystophora cristata (Erxleben, 1777)), ROS production increases with maturation but the accumulation of oxidative damage does not. To test this, we analyzed the tissue capacity to produce ROS, the accumulation of oxidative damage, and the activity and protein content of the cooper, zinc, and manganese dependent superoxide dismutases (Cu,ZnSOD, MnSOD) in skeletal muscle from neonates, weaned pups, and adult hooded seals. Our results showed higher tissue capacity to produce ROS, higher Cu,ZnSOD and MnSOD activities, and higher MnSOD protein content in adult seals than in pups. No differences in oxidative damage to lipids, proteins, or DNA were detected among groups. Results suggest that increased SOD activity likely counters the oxidative damage commonly associated with increased ROS production. These findings highlight the unusual tolerance of skeletal muscle of seals to increased ROS production.

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)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.082
Threshold uncertainty score0.962

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.001
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 head0.023
GPT teacher head0.223
Teacher spread0.200 · 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 designObservational
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

Citations26
Published2011
Admission routes1
Has abstractyes

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