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Record W2009717608 · doi:10.1097/mco.0b013e3280110171

Amino acids and the regulation of methyl balance in humans

2006· review· en· W2009717608 on OpenAlexaff
John T. Brosnan, Robin P. da Silva, Margaret E. Brosnan

Bibliographic record

VenueCurrent Opinion in Clinical Nutrition & Metabolic Care · 2006
Typereview
Languageen
FieldMedicine
TopicFolate and B Vitamins Research
Canadian institutionsMemorial University of Newfoundland
Fundersnot available
KeywordsTransmethylationEpigeneticsDNA methylationMethionineMethylationSteatohepatitisBiologyTranssulfurationPathogenesisBioinformaticsAmino acidBiochemistryMedicineDiseaseFatty liverGeneCystathionine beta synthaseInternal medicineGene expression

Abstract

fetched live from OpenAlex

PURPOSE OF REVIEW: To outline recent advances in our understanding of the metabolic basis for the maintenance of cellular S-adenosylmethionine levels and, thus, for facilitating the many crucial methylation reactions in the body. Amino acids are intimately involved in these processes. RECENT FINDINGS: The application of stable-isotope methodology has permitted accurate estimation of the total transmethylation flux in humans. Chemical balance studies have identified the quantitatively major transmethylation reactions. New evidence points to a key role for deranged S-adenosylmethionine metabolism in the pathogenesis of liver disease. Mutations in key enzymes point to the importance of methyl metabolism in closure of the neural tube, synthesis of creatine and metabolic clearance of methionine. Dietary interventions designed to affect S-adenosylmethionine availability to pregnant mice have been shown to modulate the epigenetic DNA methylation of specific genes. SUMMARY: These findings are of relevance to the pathogenesis of neural tube defects as well as the interaction between a genetic polymorphism and nutritional status. They also address the issue of methyl group availability and epigenetic regulation. Finally, they are also relevant to the etiology of cirrhosis and steatohepatitis.

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)0.002
metaresearch head score (Gemma)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Other design · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.947
Threshold uncertainty score0.868

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0030.001
Bibliometrics0.0010.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.001
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 head0.186
GPT teacher head0.509
Teacher spread0.323 · 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 designOther design
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

Citations36
Published2006
Admission routes1
Has abstractyes

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