Insights into prevention of human neural tube defects by folic acid arising from consideration of mouse mutants
Bibliographic record
Abstract
Almost 30 years after the initial study by Richard W. Smithells and coworkers, it is still unknown how maternal periconceptional folic acid supplementation prevents human neural tube defects (NTDs). In this article, questions about human NTD prevention are considered in relation to three groups of mouse models: NTD mutants that respond to folate, NTD mutants and strains that do not respond to folate, and mutants involving folate-pathway genes. Of the 200 mouse NTD mutants, only a few have been tested with folate; half respond and half do not. Among responsive mutants, folic acid supplementation reduces exencephaly and/or spina bifida aperta frequency in the Sp(2H), Sp, Cd, Cited2, Cart1, and Gcn5 mutants. Prevention ranges from 35 to 85%. The responsive Sp(2H) (Pax3) mutant has abnormal folate metabolism, but the responsive Cited2 mutant does not. Neither folic nor folinic acid reduces NTD frequency in Axd, Grhl3, Fkbp8, Map3k4, or Nog mutants or in the curly tail or SELH/Bc strains. Spina bifida frequency is reduced in Axd by methionine and in curly tail by inositol. Exencephaly frequency is reduced in SELH/Bc by an alternative commercial ration. Mutations in folate-pathway genes do not cause NTDs, except for 30% exencephaly in folate-treated Folr1. Among folate-pathway mutants, neural tube closure is normal in Cbs, Folr2, Mthfd1, Mthfd2, Mthfr, and Shmt1 mutants. Embryos die by midgestation in Folr1, Mtr, Mtrr, and RFC1 mutants. The mouse models point to genetic heterogeneity in the ability to respond to folic acid and also to heterogeneity in genetic cause of NTDs that can be prevented by folic acid.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot 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.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".