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Record W2153029275 · doi:10.1126/science.1112125

Mitochondrial DNA Mutations, Oxidative Stress, and Apoptosis in Mammalian Aging

2005· article· en· W2153029275 on OpenAlexaff
Gregory C. Kujoth, Asimina Hiona, Thomas D. Pugh, Shinichi Someya, K. Panzer, Stephanie E. Wohlgemuth, Tim Hofer, Arnold Y. Seo, Rachel Sullivan, Wendy A. Jobling, Jason D. Morrow, Holly Van Remmen, John M. Sedivy, Tatsuya Yamasoba, Masaru Tanokura, Richard Weindruch, Christiaan Leeuwenburgh, Tomas A. Prolla

Bibliographic record

VenueScience · 2005
Typearticle
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicMitochondrial Function and Pathology
Canadian institutionsInstitute of Aging
FundersNational Institute of Diabetes and Digestive and Kidney DiseasesNational Center for Research ResourcesNational Institute of General Medical SciencesNational Institute on Aging
KeywordsMitochondrial DNABiologyOxidative stressMitochondrionMutationApoptosisGeneticsDNA damageCell biologyDNA repairMolecular biologyDNAOxidative phosphorylationPolymeraseGeneBiochemistry

Abstract

fetched live from OpenAlex

Mutations in mitochondrial DNA (mtDNA) accumulate in tissues of mammalian species and have been hypothesized to contribute to aging. We show that mice expressing a proofreading-deficient version of the mitochondrial DNA polymerase g (POLG) accumulate mtDNA mutations and display features of accelerated aging. Accumulation of mtDNA mutations was not associated with increased markers of oxidative stress or a defect in cellular proliferation, but was correlated with the induction of apoptotic markers, particularly in tissues characterized by rapid cellular turnover. The levels of apoptotic markers were also found to increase during aging in normal mice. Thus, accumulation of mtDNA mutations that promote apoptosis may be a central mechanism driving mammalian aging.

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 score0.002

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
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 head0.010
GPT teacher head0.263
Teacher spread0.252 · 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

Citations2,155
Published2005
Admission routes1
Has abstractyes

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