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Record W2984697931 · doi:10.1097/mco.0000000000000611

Formate and its role in amino acid metabolism

2019· review· en· W2984697931 on OpenAlexafffund
John T. Brosnan, Margaret E. Brosnan

Bibliographic record

VenueCurrent Opinion in Clinical Nutrition & Metabolic Care · 2019
Typereview
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicAmino Acid Enzymes and Metabolism
Canadian institutionsMemorial University of Newfoundland
FundersCanadian Institutes of Health Research
KeywordsFormateMetabolismSerineAmino acidGlycineBiochemistryFormate dehydrogenaseChemistryEnzymeCatalysis

Abstract

fetched live from OpenAlex

PURPOSE OF REVIEW: The aim of this report is to examine critical relationships between amino acid and formate metabolism with particular reference to the production of formate, and to review novel functions of formate. RECENT FINDINGS: In addition to well established mechanisms in one-carbon metabolism, formate may play an important role in early pregnancy by preventing the onset of neural tube defects in sensitive strains of mice, including mice with deficiencies in MTHFD1L, the glycine cleavage system and the mitochondrial folate transporter. Markedly elevated, circulating levels of formate are found in late pregnancy, including in cord blood, as well as elevated levels of amino acid precursors. These are consistent with specific roles for formate in late pregnancy. Serine metabolism may reduce NADP to NADPH and permit the use of NADPH in reductive reactions. Novel, noncanonical functions of formate include high rates of formate production from serine in cells and in cancers. SUMMARY: Novel, noncanonical functions of formate continue to be discovered. Integrating their functions with well established elements of one-carbon metabolism remains an important future objective.

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)0.001
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.004
Threshold uncertainty score0.013

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0010.002
Science and technology studies0.0000.000
Scholarly communication0.0010.001
Open science0.0010.001
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0040.003

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 head0.101
GPT teacher head0.433
Teacher spread0.331 · 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 designNot applicable
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

Citations7
Published2019
Admission routes2
Has abstractyes

Explore more

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