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Record W2945077451 · doi:10.22215/etd/2018-13253

Regulation of glutathione-based antioxidant defenses in response to dehydration stress in the African clawed frog, Xenopus laevis

2018· dissertation· en· W2945077451 on OpenAlexaff
Jessica J.L. Mattice

Bibliographic record

Venuenot available
Typedissertation
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicCancer, Hypoxia, and Metabolism
Canadian institutionsCarleton University
Fundersnot available
KeywordsXenopusGlutathioneAntioxidantGlutathione reductaseBiochemistryOxidative stressBiologyEnzymeDehydrationChemistryCell biologyGlutathione peroxidase

Abstract

fetched live from OpenAlex

The African clawed frog (Xenopus laevis) needs efficient antioxidant defenses to mitigate oxidative damage and endure dehydration stress under arid conditions in its natural environment.Two enzymes that aid glutathione-based antioxidant defenses, glutathione reductase (GR) and glucose-6-phosphate dehydrogenase (G6PDH), were purified from liver of control and dehydrated (~35% total body water lost) frogs.Kinetic analysis revealed that GR was positively regulated in response to dehydration to regenerate GSH, particularly when physiological urea concentrations were high.G6PDH from dehydrated frogs showed lower affinity for glucose-6-phosphate than control but was strongly activated in the presence of high ATP.Both enzymes showed regulatory modification by reversible protein phosphorylation that affected substrate affinities.Dehydration also induced other PTMs including protein sumoylation, cys-nitrosylation, and acetylation that could affect kinetic properties and protein stability.This thesis provides novel insights into the regulation of glutathione-based enzymatic defenses against dehydration-induced oxidative stress.

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 score0.001
Threshold uncertainty score0.003

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 integrity0.0000.000
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 head0.009
GPT teacher head0.267
Teacher spread0.258 · 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
Published2018
Admission routes1
Has abstractyes

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