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Record W2523988913 · doi:10.1111/acel.12528

Antioxidants reveal an inverted U‐shaped dose‐response relationship between reactive oxygen species levels and the rate of aging in <i>Caenorhabditis elegans</i>

2016· article· en· W2523988913 on OpenAlexaff
David Desjardins, Briseida Cacho‐Valadez, Ju‐Ling Liu, Ying Wang, Callista Yee, Kristine Bernard, Arman Khaki, Lionel Breton, Siegfried Hekimi

Bibliographic record

VenueAging Cell · 2016
Typearticle
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicGenetics, Aging, and Longevity in Model Organisms
Canadian institutionsMcGill University
Fundersnot available
KeywordsReactive oxygen speciesCaenorhabditis elegansParaquatBiologyResveratrolAntioxidantMitochondrial ROSLongevityCell biologyMitochondrionMutantBiochemistryGeneticsGene

Abstract

fetched live from OpenAlex

Reactive oxygen species (ROS) are potentially toxic, but they are also signaling molecules that modulate aging. Recent observations that ROS can promote longevity have to be reconciled with the numerous claims about the benefits of antioxidants on lifespan. Here, three antioxidants [N-acetylcysteine (NAC), vitamin C, and resveratrol (RSV)] were tested on Caenorhabditis elegans mutants that alter drug uptake, mitochondrial function, and ROS metabolism. We observed that like pro-oxidants, antioxidants can both lengthen and shorten lifespan, dependent on concentration, genotypes, and conditions. The effects of antioxidants thus reveal an inverted U-shaped dose-response relationship between ROS levels and lifespan. In addition, we observed that RSV can act additively to both NAC and paraquat, to dramatically increase lifespan. This suggests that the effect of compounds that modulate ROS levels can be additive when their loci of action or mechanisms of action are sufficiently distinct.

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: Observational · Consensus signal: none
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)0.0010.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.001
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 head0.026
GPT teacher head0.248
Teacher spread0.223 · 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 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

Citations93
Published2016
Admission routes1
Has abstractyes

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