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Record W4401380649 · doi:10.47611/jsrhs.v13i1.6459

Exploring the Interconnected Mechanisms of Transgenerational Epigenetic Inheritance

2024· article· en· W4401380649 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.

Bibliographic record

VenueJournal of Student Research · 2024
Typearticle
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicEpigenetics and DNA Methylation
Canadian institutionsImpact
Fundersnot available
KeywordsEpigeneticsTransgenerational epigeneticsDNA methylationBiologyGenomic imprintingmicroRNAGeneticsInheritance (genetic algorithm)HistoneEpigenesisGeneComputational biologyGene expression

Abstract

fetched live from OpenAlex

It is estimated that around 70% of all adults around the world have faced trauma in their lives (Benjet et al., 2016). Trauma can cause individuals to undergo epigenetic changes which can lead to health complications in the future (Alegría-Torres et al., 2011). Epigenetics is defined as the study of molecular modifications to DNA through DNA methylation, histone modifications, and non-coding RNAs that can regulate gene expression independent of DNA sequences (Li, 2021). New evidence suggests that epigenetic changes may be passed down to offspring. However, the exact pathway for transgenerational epigenetic inheritance to occur is unknown. While existing theories, including intrauterine programming, miRNA-mediated pathways, and genomic imprinting, offer possible pathways, none can fully account for the spectrum of transgenerational inheritance. This paper will review the three models proposed and will explore the possibility of their combined influence on transgenerational epigenetic inheritance. Exploring epigenetic mechanisms can help offer potential intervention points to relieve the negative impact of trauma on several generations.

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)0.002
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation 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 score0.025
Threshold uncertainty score0.180

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.000
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)0.0000.000

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 head0.281
GPT teacher head0.429
Teacher spread0.149 · 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