MétaCan
Menu
Back to cohort
Record W2948875581 · doi:10.1111/odi.13136

LINE‐1 methylation in cleft lip tissues: Influence of infant <i>MTHFR</i> c.677C&gt;T genotype

2019· article· en· W2948875581 on OpenAlexaff
Mohammad Faisal J. Khan, Julian Little, Valentina Aleotti, Peter Mossey, Régine P.M. Steegers‐Theunissen, Luca Autelitano, Maria Costanza Meazzini, Amin Ravaei, Michele Rubini

Bibliographic record

VenueOral Diseases · 2019
Typearticle
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicCleft Lip and Palate Research
Canadian institutionsUniversity of Ottawa
FundersUniversità degli Studi di Ferrara
KeywordsMethylenetetrahydrofolate reductaseGenotypingGenotypeMethylationDNA methylationgenomic DNAAlleleMolecular biologyGeneticsMedicineBiologyDNAGeneGene expression

Abstract

fetched live from OpenAlex

OBJECTIVE: To investigate the influence of MTHFR c.677C>T genotype on LINE-1 methylation in lateral and medial tissues from cleft lip (CL). METHODS: Forty-five consecutive non-syndromic cleft lip with or without cleft palate (nsCL/P) cases were included in the study. Genomic DNA was extracted from tissues at both sides of cleft lip, and LINE-1 methylation was detected by bisulfite conversion and pyrosequencing. MTHFR c.677C>T genotyping was carried out using the TaqMan genotyping assay. RESULTS: LINE-1 methylation level was significantly higher on medial side of cleft lip compared with lateral side (p = 0.001). This difference was not significantly influenced by the case's sex or cleft type. However, MTHFR c.677C>T genotyping revealed that the difference in LINE-1 methylation across cleft lip was restricted to carriers of C allele of MTHFR c.677C>T and was not apparent in TT homozygous cases (p = 0.027). CONCLUSION: This integrated analysis supports the previous finding of differences in DNA methylation across the two sides of cleft lip and further suggests a possible role of MTHFR c.677C>T genotype in establishing this difference.

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 categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.337
Threshold uncertainty score0.394

Codex and Gemma teacher scores by category

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)0.0000.000

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 head0.012
GPT teacher head0.298
Teacher spread0.286 · 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.

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

Citations11
Published2019
Admission routes1
Has abstractyes

Explore more

Same venueOral DiseasesSame topicCleft Lip and Palate ResearchFrench-language works237,207