Spontaneous Pregnancy Loss Mediated by Abnormal Maternal Inflammation in Rats Is Linked to Deficient Uteroplacental Perfusion
Bibliographic record
Abstract
Abnormal maternal inflammation during pregnancy is associated with spontaneous pregnancy loss and intrauterine fetal growth restriction. However, the mechanisms responsible for these pregnancy outcomes are not well understood. In this study, we used a rat model to demonstrate that pregnancy loss resulting from aberrant maternal inflammation is closely linked to deficient placental perfusion. Administration of LPS to pregnant Wistar rats on gestational day 14.5, to induce maternal inflammation, caused fetal loss in a dose-dependent manner 3-4 h later, and surviving fetuses were significantly growth restricted. Pregnancy loss was associated with coagulopathy, structural abnormalities in the uteroplacental vasculature, decreased placental blood flow, and placental and fetal hypoxia within 3 h of LPS administration. This impairment in uteroplacental hemodynamics in LPS-treated rats was linked to increased uterine artery resistance and reduced spiral arteriole flow velocity. Pregnancy loss induced by LPS was prevented by maternal administration of the immunoregulatory cytokine IL-10 or by blocking TNF-α activity after treatment with etanercept (Enbrel). These results indicate that alterations in placental perfusion are responsible for fetal morbidities associated with aberrant maternal inflammation and support a rationale for investigating a potential use of immunomodulatory agents in the prevention of spontaneous pregnancy loss.
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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) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".