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Record W2955329307 · doi:10.1073/pnas.1821752116

EBV infection is associated with histone bivalent switch modifications in squamous epithelial cells

2019· article· en· W2955329307 on OpenAlexaff
Merrin Man‐Long Leong, Arthur Kwok Leung Cheung, Wei Dai, Sai Wah Tsao, Chi Man Tsang, William O. Dawson, Josephine Mun Yee Ko, Maria Li Lung

Bibliographic record

VenueProceedings of the National Academy of Sciences · 2019
Typearticle
Languageen
FieldMedicine
TopicViral-associated cancers and disorders
Canadian institutionsUniversity Health Network
FundersUniversity of Hong KongResearch Grants Council, University Grants Committee
KeywordsBiologyHistoneDNA repairBivalent (engine)DNA mismatch repairHistone methylationHistone H2AMolecular biologyDNA damageHistone methyltransferaseCancer researchCell biologyDNA methylationDNAChemistryGeneticsGeneGene expression

Abstract

fetched live from OpenAlex

Significance Epstein−Barr virus (EBV) infection is implicated in the development of certain cancers; however, the mechanisms by which EBV contributes to the pathogenesis of epithelial cell malignancies remains unclear. This study highlights the association of EBV infection with aberrant modifications in histone bivalent marks, H3K4me3 and H3K27me3, in nasopharyngeal epithelial cells. Down-regulation of the DNA damage repair pathway genes, including MLH1 , associated with aberrant histone bivalent marks in a promoter hypermethylation-independent manner, was observed after EBV infection. This finding provides strong evidence linking EBV infection to epigenetic modifications in epithelial cells. This study also suggests that the level of MLH1 may be useful as a potential biomarker to evaluate the responsiveness of nasopharyngeal carcinoma patients to cisplatin-based therapies.

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: Bench or experimental · Consensus signal: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.002
Threshold uncertainty score0.007

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.000
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)0.0020.000

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 head0.028
GPT teacher head0.303
Teacher spread0.274 · 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 designBench or experimental
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

Citations34
Published2019
Admission routes1
Has abstractyes

Explore more

Same venueProceedings of the National Academy of SciencesSame topicViral-associated cancers and disordersFrench-language works237,207