Ameliorative effects of aspirin against lipopolysaccharide-induced preeclampsia-like symptoms in rats by inhibiting the pro-inflammatory pathway
Bibliographic record
Abstract
Preeclampsia is an inflammatory disease and has connection with increased pro-inflammatory cytokines. Aspirin reduces the incidence of preeclampsia complications. However, the effects of aspirin on lipopolysaccharide-induced preeclampsia-like symptoms in rats have not been reported and the underlying molecular mechanism has not been illuminated. Hence, we investigated the anti-inflammatory effects of aspirin on lipopolysaccharide-induced preeclampsia-like phenotypes in pregnant rats and elucidated the potential molecular mechanism. Preeclampsia-like phenotypes were induced by tail vein injection of lipopolysaccharide (1 μg/kg) on gestational day 14. Aspirin (2 mg/kg per day) were administered from gestational day 14 to 19. Clinical phenotypes were recorded. Placenta tissues and serum were obtained to measure inflammatory cytokines levels using ELISA kit on gestational day 20. The mRNA expressions of IL-6, IL-1β, and MCP-1 were measured by real-time PCR. Protein expressions including TLR4, MyD88, NF-κBp65, and TLR2 were determined by Western blot analysis in the rat placentas of each group. Aspirin obviously assuaged lipopolysaccharide-induced preeclampsia-like phenotypes in pregnant rats. Aspirin treatment significantly decreased the levels of pro-inflammatory cytokines in serum and placenta tissues of preeclampsia rats. Aspirin also obviously downregulated the mRNA expressions of IL-6, IL-1β, and MCP-1 and assuaged the activation of TLR4, MyD88, NF-κBp65, and TLR2 in the placental tissue. Our results indicated that aspirin could assuage preeclampsia-like phenotypes, and this improvement effect is possibly the result of the suppression of pro-inflammatory cytokines via the TLR4, MyD88, NF-κBp65, and TLR2 signaling pathway.
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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) | 0.001 | 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".