Maternal dietary deficiencies in folic acid or choline worsen stroke outcomes in adult male and female mouse offspring
Bibliographic record
Abstract
Maternal one-carbon metabolism plays an important role in early life programming. There is a well-established connection between the fetal environment and the health status of the offspring. However, there is a knowledge gap on how maternal nutrition impacts stroke outcomes in offspring. The aim of our study was to investigate the role of maternal dietary deficiencies in folic acid or choline on stroke outcomes in 3-month-old offspring. Adult female mice were fed a folic acid-deficient diet, choline-deficient diet, or control diet 4 weeks before pregnancy. They were continued on diets during pregnancy and lactation. Male and female offspring were weaned onto a control diet and at 2 months of age were subjected to ischemic stroke within the sensorimotor cortex via photothrombotic damage. Mothers maintained on either a folic acid-deficient diet or choline-deficient diet had reduced levels of S-adenosylmethionine in the liver and S-adenosylhomocysteine in the plasma. After ischemic stroke, motor function was impaired in 3-month-old offspring from mothers receiving either a folic acid-deficient diet or choline-deficient diet compared to the animals receiving a control diet. In brain tissue, there was no difference in ischemic damage volume. When protein levels were assessed in ischemic brain tissue, there were lower levels of active caspase-3 and hypoxia-inducible factor 1α in males compared to females and betaine levels were reduced in offspring from the mothers receiving a choline-deficient diet. Our results demonstrate that a deficient maternal diet at critical time points in neurodevelopment results in worse stroke outcomes. This study emphasizes the importance of maternal diet and the impact it can have on offspring health.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 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 | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".