Maternal Dietary Deficiencies in Folic Acid and Choline Result in Larger Damage Volume, Reduced Neuro-Degeneration and -Inflammation and Changes in Choline Metabolites after Ischemic Stroke in Middle-Aged Offspring
Bibliographic record
Abstract
Maternal dietary levels of one-carbon (1C) metabolites (folic acid and choline) during pregnancy play a vital role in neurodevelopment. However, the impact of maternal dietary deficiencies on offspring stroke outcomes later in life remains undefined. The aim of this study was to investigate the role of maternal dietary deficiencies in folic acid and choline on ischemic stroke outcomes in middle-aged offspring. Female mice were maintained on either a control or deficient diet prior to and during pregnancy and lactation. At 10 months of age ischemic stroke was induced in male and female offspring. Stroke outcome was assessed by measuring motor function and brain tissue. There was no difference in offspring motor function; however, sex differences were present. In brain tissue, maternal dietary deficiency increased ischemic damage volume and offspring from deficient mothers had reduced neurodegeneration and neuroinflammation within the ischemic region. Furthermore, there were changes in plasma 1C metabolites as a result of maternal diet and sex. Our data indicate that maternal dietary deficiencies do not impact offspring behavior after ischemic stroke but do play a role in brain histology and one-carbon metabolite levels in plasma. Additionally, this study demonstrates that the sex of mice plays an important role in stroke outcomes.
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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) | 0.002 | 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".