Detrimental effects of high levels of antioxidant vitamins C and E on placental function: Considerations for the Vitamins in Preeclampsia (VIP) trial
Bibliographic record
Abstract
AIM: Supplementation with antioxidant vitamins has been proposed to reduce the risk of preeclampsia and perinatal complications. In a recent study, it has been shown that this supplementation to above physiological doses does not reduce the risk of preeclampsia, but increases the rate of low birthweight babies, suggesting a detrimental effect on placental function, given the lower birthweight. The aim of the present study was to investigate the effects of high levels of antioxidants vitamins C and E on placental cells in vitro. METHODS: Isolated fresh human cytotrophoblasts were exposed to high concentrations of vitamins C and E for 48 h. Then the secretion of human chorionic gonadotropin (hCG) and the production of tumor necrosis factor-alpha (TNF-alpha) were assessed. RESULTS: High levels of vitamins C and E, separately or combined, decrease the secretion of hCG by cytotrophoblasts and increase their production of TNF-alpha. CONCLUSION: Exposure of cytotrophoblasts to high levels of antioxidant vitamins C and E may affect placental function, as reflected by decreased secretion of hCG; and placental immunity, as reflected by increased production of TNF-alpha. Such alterations are known to lead to endothelial dysfunction and adverse pregnancy outcomes, such as fetal growth restriction (FGR).
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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 | 0.002 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.002 |
| 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".