Abnormal inflammation leads to maternal coagulopathies associated with placental haemostatic alterations in a rat model of foetal loss
Bibliographic record
Abstract
Spontaneous pregnancy loss is often associated with aberrant maternal inflammation and systemic coagulopathies. However, the role of inflammation in the development of obstetric coagulopathies is poorly understood. Further, questions remain as to whether systemic coagulopathies are linked to placental haemostatic alterations, and whether these local alterations contribute to a negative foetal outcome. Using a model of spontaneous foetal loss in which pregnant rats are given a single injection of bacterial lipopolysaccharide (LPS), we characterised the systemic maternal coagulation status following LPS administration using thromboelastography (TEG), a global haemostatic assay that measures the kinetics of clot formation. Systemic maternal coagulopathy was evident in 82% of LPS-treated rats. Specifically, we observed stage-I, -II, and -III disseminated intravascular coagulation (DIC) and hypercoagulability. Modulation of inflammation through inhibition of tumour necrosis factor α with etanercept resulted in a 62% reduction in the proportion of rats exhibiting coagulopathy. Moreover, inflammation-induced systemic coagulopathies were associated with placental haemostatic alterations, which included increased intravascular, decidual, and labyrinth fibrin deposition in cases of DIC-I and hypercoagulability, and an almost complete absence of fibrin deposition in cases of DIC-III. Furthermore, systemic and placental haemostatic alterations were associated with impaired utero-placental haemodynamics, and inhibition of these haemostatic alterations by etanercept was associated with maintenance of utero-placental haemodynamics. These findings indicate that modulation of maternal inflammation may be useful in the prevention of coagulopathies associated with complications of pregnancy.
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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".