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Record W2762030311 · doi:10.1139/bcb-2017-0206

Maternal iron nutriture as a critical modulator of fetal alcohol spectrum disorder risk in alcohol-exposed pregnancies

2017· review· en· W2762030311 on OpenAlexvenueno aff
Kaylee K. Helfrich, Nipun Saini, Pamela J. Kling, Susan M. Smith

Bibliographic record

VenueBiochemistry and Cell Biology · 2017
Typereview
Languageen
FieldMedicine
TopicPrenatal Substance Exposure Effects
Canadian institutionsnot available
FundersNational Institute on Alcohol Abuse and AlcoholismNational Institutes of Health
KeywordsPregnancyOffspringFetusMedicineIron deficiencyPhysiologyAlcoholFetal alcohol syndromeInternal medicineAnemiaBiologyBiochemistry

Abstract

fetched live from OpenAlex

Alcohol consumption during pregnancy places the fetus at risk for permanent physical, cognitive, and behavioral impairments, collectively termed fetal alcohol spectrum disorder (FASD). However, prenatal alcohol exposure (PAE) outcomes vary widely, and growing evidence suggests that maternal nutrition is a modifying factor. Certain nutrients, such as iron, may modulate FASD outcomes. Untreated gestational iron deficiency (ID) causes persistent neurodevelopmental deficits in the offspring that affect many of the same domains damaged by PAE. Although chronic alcohol consumption enhances iron uptake and elevates liver iron stores in adult alcoholics, alcohol-abusing premenopausal women often have low iron reserves due to menstruation, childbirth, and poor diet. Recent investigations show that low iron reserves during pregnancy are strongly associated with a worsening of several hallmark features in FASD including reduced growth and impaired associative learning. This review discusses recent clinical and animal model findings that maternal ID worsens fetal outcomes in response to PAE. It also discusses underlying mechanisms by which PAE disrupts maternal and fetal iron homeostasis. We suggest that alcohol-exposed ID pregnancies contribute to the severe end of the FASD spectrum.

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 categoriesMeta-epidemiology (narrow)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Other design · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.721
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0020.000
Bibliometrics0.0000.000
Science and technology studies0.0000.001
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0010.001
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 head0.023
GPT teacher head0.318
Teacher spread0.294 · 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.

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

Citations18
Published2017
Admission routes1
Has abstractyes

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