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Record W2417724393 · doi:10.1038/srep13620

The Synergetic Coupling among the Cellular Antioxidants Glutathione Peroxidase/Peroxiredoxin and Other Antioxidants and its Effect on the Concentration of H2O2

2015· article· en· W2417724393 on OpenAlexaff
Hamid R. Molavian, Ali Madani Tonekaboni, Mohammad Kohandel, Sivabal Sivaloganathan

Bibliographic record

VenueScientific Reports · 2015
Typearticle
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicRedox biology and oxidative stress
Canadian institutionsFields Institute for Research in Mathematical SciencesUniversity of Waterloo
Fundersnot available
KeywordsPeroxiredoxinAntioxidantCatalasePeroxidaseGlutathione peroxidaseHydrogen peroxideGlutathioneChemistryBiochemistryOxidative stressBiophysicsCell biologyBiologyEnzyme

Abstract

fetched live from OpenAlex

Abstract Glutathione peroxidase (GPx), peroxiredoxin (Prx) and catalase are the major antioxidants at the cellular level and protect cell compartments against hydrogen peroxide (H 2 O 2 ). In addition, they affect cellular processes such as cell signaling by modulating H 2 O 2 . In this paper we demonstrate that there is a synergetic coupling between GPxs, Prxs themselves and also with other antioxidants when the GPxs and Prxs are not in their saturated reduced form. This is due to a change in the activity of glutathione peroxidases and peroxiredoxins as a result of a change in the concentrations of other antioxidants. The strength of this synergy depends on the reaction rates and the concentration of these antioxidants. We use a perturbative method to calculate the concentration of H 2 O 2 as function of the production rate of H 2 O 2 and the concentration of various antioxidants. This derivation shows clearly why antioxidants behave in a correlated manner and why any change in the activity of one of them translates to a change in the activity of other antioxidants. Our results show that an increase in the activity of GPxs or Prxs might not be due to a genetic switch but due to an increase in the activity of other antioxidants.

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)0.003
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: Bench or experimental · Consensus signal: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.017
Threshold uncertainty score0.503

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0030.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0010.001
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 head0.245
Teacher spread0.232 · 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

Citations45
Published2015
Admission routes1
Has abstractyes

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