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Record W2802324910 · doi:10.1083/jcb.201708007

Superoxide dismutases: Dual roles in controlling ROS damage and regulating ROS signaling

2018· review· en· W2802324910 on OpenAlexafffund
Ying Wang, Robyn Branicky, Alycia Noë, Siegfried Hekimi

Bibliographic record

VenueThe Journal of Cell Biology · 2018
Typereview
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicGlutathione Transferases and Polymorphisms
Canadian institutionsMcGill University
FundersCanadian Institutes of Health ResearchMcGill University
KeywordsReactive oxygen speciesSuperoxide dismutaseCompartmentalization (fire protection)SuperoxideCell biologyBiochemistryCell signalingEnzymeHydrogen peroxideChemistrySignal transductionMitochondrionBiology

Abstract

fetched live from OpenAlex

Superoxide dismutases (SODs) are universal enzymes of organisms that live in the presence of oxygen. They catalyze the conversion of superoxide into oxygen and hydrogen peroxide. Superoxide anions are the intended product of dedicated signaling enzymes as well as the byproduct of several metabolic processes including mitochondrial respiration. Through their activity, SOD enzymes control the levels of a variety of reactive oxygen species (ROS) and reactive nitrogen species, thus both limiting the potential toxicity of these molecules and controlling broad aspects of cellular life that are regulated by their signaling functions. All aerobic organisms have multiple SOD proteins targeted to different cellular and subcellular locations, reflecting the slow diffusion and multiple sources of their substrate superoxide. This compartmentalization also points to the need for fine local control of ROS signaling and to the possibility for ROS to signal between compartments. In this review, we discuss studies in model organisms and humans, which reveal the dual roles of SOD enzymes in controlling damage and regulating signaling.

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)0.001
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.002
Threshold uncertainty score0.008

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0020.001
Science and technology studies0.0000.001
Scholarly communication0.0010.001
Open science0.0010.001
Research integrity0.0010.002
Insufficient payload (model declined to judge)0.0020.002

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 head0.018
GPT teacher head0.280
Teacher spread0.262 · 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 designNot applicable
Domainnot available
GenreReview

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

Citations2,125
Published2018
Admission routes2
Has abstractyes

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