Role of Complement Component C1q in the Onset of Preeclampsia in Mice
Bibliographic record
Abstract
Preeclampsia (PE) is a life-threatening, pregnancy-induced disease and a leading cause of maternal and fetal morbidity and mortality. Despite considerable research, the causes of PE remain unclear, and there is no effective treatment. Studies in animal models that resemble this complex pregnancy-related disorder may help to identify possible therapies for PE. Complement component C1q has an important role in trophoblast migration, spiral arteries remodeling, and normal placentation. Here we show that pregnant C1q-deficient (C1q(-/-)) mice recapitulate the key features of human PE: hypertension, albuminuria, endotheliosis, decreased placental vascular endothelial growth factor (VEGF) and elevated levels of soluble VEGF receptor 1 (sFlt-1) that correlate with increased fetal death. In addition, decreased blood flow and increased oxidative stress are observed in placentas from C1q(-/-) mice. Treatment of C1q(-/-) mice with pravastatin restored trophoblast invasiveness, placental blood flow, and angiogenic balance and, thus, prevented the onset of PE. Serum-soluble receptors for VEGF-1 levels were reduced and placental VEGF levels were significantly increased in C1q(-/-) mice treated with pravastatin compared with untreated C1q(-/-) mice (VEGF: 1067±171 versus 419±194 pg/mL; P<0.01). Pravastatin treatment reduced hypertension (change in mean arterial pressure: 1±1 versus 18±3 mm Hg in C1q(-/-) untreated mice), and albuminuria (of creatinine) was reduced from 820±175 to 117±45 μg/mg (both P<0.01). Renal damage and endothelial dysfunction were significantly attenuated with pravastatin. This model that highlights the causative role of impaired trophoblast invasion in the pathogenesis of PE allowed us to identify pravastatin as a good therapeutic option to prevent PE.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".