Impact of excessive hypercholesterolemia in pregnancy on the placentas of the male and female offspring
Bibliographic record
Abstract
Excessive hypercholesterolemia (eHC) in pregnancy reduces placental efficiency in both fetal sexes, but the mechanisms are not known. In this study, Sprague Dawley rats received a control or high cholesterol diet (to model eHC) during pregnancy, after which various markers of placental function were assessed. Lipid levels, but not reactive oxygen species levels, were increased in both the male and female eHC placentas vs. controls. However, compared to control placentas, eHC reduced cholesterol receptors, increased cholesterol transporters, lowered fetal cholesterol levels, and altered the unfolded protein response in a sex-specific manner. Moreover, NOD-, LRR-, and pyrin domain-containing protein 3 (NLRP3) levels were increased in only the male eHC placentas, and associated with reduced interleukin 1β levels, likely due to its rapid secretion into the circulation. The levels of caspase 8, but not caspase 1, were increased in only the male eHC placentas vs. controls, suggesting that the processing of interleukin 1β may have happened via a non-canonical pathway. In conclusion, eHC in pregnancy impacts the placentas of both the male and female offspring, but the activation of the NLRP3 inflammasome in only the male placentas suggests that the male offspring may be more susceptible to excessive increases in maternal cholesterol levels.
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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 | 0.000 | 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 | 0.000 |
| 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".