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Record W2759661759 · doi:10.3389/fgene.2017.00138

Epigenetic Regulation of Cellular Senescence and Aging

2017· review· en· W2759661759 on OpenAlex

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.
fundA Canadian funder is recorded on the work.

Bibliographic record

VenueFrontiers in Genetics · 2017
Typereview
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicEpigenetics and DNA Methylation
Canadian institutionsUniversity of Lethbridge
FundersNatural Sciences and Engineering Research Council of CanadaCanadian Institutes of Health ResearchAlberta InnovatesAlberta Innovates - Health SolutionsAlberta Cancer Foundation
KeywordsEpigeneticsDNA methylationHeterochromatinBiologySenescenceHistoneCellular senescenceEpigenesisCellular AgingAgeingComputational biologyNeuroscienceChromatinCell biologyPhenotypeGeneticsBioinformaticsDNATelomereGeneGene expression

Abstract

fetched live from OpenAlex

Aging is characterized by functional decline of diverse organs and an increased risk for several diseases. Therefore, a high interest exists in understanding the molecular mechanisms that stimulate aging at all levels, from cells and tissues to organs and organisms, in order to develop ways to promote healthy aging. While many molecular and biochemical mechanisms are already understood in some detail, the role of changes in epigenetic regulation has only begun to be considered in recent years. The age-dependent global reduction in heterochromatin, along with site-specific changes in the patterns of DNA methylation and modification of histones, have been observed in several aging model systems. However, understanding of the precise role of such changes requires further research. In this review, we will discuss the role of epigenetic regulation in aging and indicate future research directions that will help elucidate the mechanistic details of it.

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.

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)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Other design · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.990
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0010.000
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 head0.038
GPT teacher head0.320
Teacher spread0.282 · 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