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Record W2899872974 · doi:10.1139/bcb-2018-0248

How alcohol drinking affects our genes: an epigenetic point of view

2018· review· en· W2899872974 on OpenAlexvenueno aff
Stefania Ciafrè, Valentina Carito, Giampiero Ferraguti, Antonio Greco, George N. Chaldakov, Marco Fiore, Mauro Ceccanti

Bibliographic record

VenueBiochemistry and Cell Biology · 2018
Typereview
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicEpigenetics and DNA Methylation
Canadian institutionsnot available
FundersSapienza Università di Roma
KeywordsEpigeneticsHistoneDNA methylationChromatin remodelingChromatinAddictionmicroRNABiologyAlcohol dependenceAcetylationGeneticsHistone methylationGeneGene expressionAlcoholNeuroscienceBiochemistry

Abstract

fetched live from OpenAlex

This work highlights recent studies in epigenetic mechanisms that play a role in alcoholism, which is a complex multifactorial disorder. There is a large body of evidence showing that alcohol can modify gene expression through epigenetic processes, namely DNA methylation and nucleosomal remodeling via histone modifications. In that regard, chronic exposure to ethanol modifies DNA and histone methylation, histone acetylation, and microRNA expression. The alcohol-mediated chromatin remodeling in the brain promotes the transition from use to abuse and addiction. Unravelling the multiplex pattern of molecular modifications induced by ethanol could support the development of new therapies for alcoholism and drug addiction targeting epigenetic processes.

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 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), Research integrity
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Bench or experimental · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.586
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0010.001
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 head0.031
GPT teacher head0.310
Teacher spread0.279 · 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 teacher head, not a consensus.

Study designBench or experimental
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

Citations60
Published2018
Admission routes1
Has abstractyes

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