An Examination of the Effect of Aspirin and Salicylic Acid on Soluble Fms-like Tyrosine Kinase-1 Release from Human Placental Trophoblasts
Bibliographic record
Abstract
Low-dose aspirin (LDA) is efficacious in preventing preeclampsia, but its mechanism of action is unclear. Conflicting evidence suggests that it may inhibit placental trophoblast release of soluble fms-like tyrosine kinase-1 (sFlt1), a key mediator of preeclampsia. We examined whether, and at what concentrations, aspirin and its principal metabolite, salicylic acid, modulate sFlt1 release and/or expression in trophoblasts. Human trophoblast lines BeWo and HTR-8/SVneo were cultured; BeWo cells were also treated with 1% oxygen vs. normoxia to mimic hypoxia in preeclamptic placentas. Cells were treated with aspirin or salicylic acid vs. vehicle for 24 h at concentrations relevant to LDA and at higher concentrations. Protein concentrations (ELISA) and mRNA expression (RT-PCR) of sFlt1 were determined. Under normoxia, LDA-relevant concentrations of aspirin (10-50 µmol/L) or salicylic acid (20-100 µmol/L) had no significant effect on sFlt1 protein release or mRNA expression in BeWo cells. However, inhibition was observed at higher concentrations (1 mmol/L for aspirin and ≥200 μmol/L for salicylic acid). Hypoxia enhanced sFlt1 protein release and mRNA expression in BeWo cells, but these responses were not significantly affected by either aspirin or salicylic acid at LDA concentrations. Similarly, neither drug altered sFlt1 protein secretion or mRNA expression in normoxic HTR-8/SVneo cells at LDA concentrations. We suggest that direct modulation of trophoblast release or expression of sFlt1 is unlikely to be a mechanism underlying the clinical efficacy of LDA in preeclampsia.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 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 | 0.000 |
| 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".