Nitroglycerin prevents coagulopathies and foetal death associated with abnormal maternal inflammation in rats
Bibliographic record
Abstract
Inflammation-associated foetal loss is often linked to maternal coagulopathies. Here, we characterised the role of maternal inflammation in the development of various systemic maternal coagulopathies and foetal death during mid-to-late gestation in rats. Since nitric oxide (NO) functions as an inhibitor of platelet aggregation and anti-oxidant, we also tested whether the NO mimetic nitroglycerin (glyceryl trinitrate, GTN) prevents inflammation-associated coagulopathies and foetal death. To induce chronic inflammation, pregnant Wistar rats were injected with low-doses of lipopolysaccharide (LPS; 10-40 μg/kg) on gestational days (GD) 13.5-16.5. To determine whether the effects of inflammation are mediated by tumour necrosis factor-α (TNF-α), the TNF-α inhibitor etanercept was injected on GD 13.5 and 15.5. Controls consisted of rats injected with saline. GTN was administered to LPS-treated rats via daily application of a transdermal patch on GD 12.5-16.5. Using thromboelastography (TEG), various coagulation parameters were assessed on GD 17.5; foetal viability was determined morphologically. Reference coagulation parameters were established based on TEG results obtained from control animals. LPS-treated rats exhibited distinct systemic coagulopathies: hypercoagulability, hypocoagulability, hyperfibrinolysis, and disseminated intravascular coagulation (DIC) stages I and III. A specific foetal death coagulation phenotype was observed, implicating TEG as a potential tool to identify inflammation-induced haemostatic alterations associated with pregnancy loss. Treatment with etanercept reduced the incidence of coagulopathy by 47%, while continuous delivery of GTN prevented foetal death and the inflammation-induced coagulopathies. These findings provide a rationale for investigating the use of GTN in the prevention of maternal coagulopathies and inflammation-mediated foetal death.
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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 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".