Effect of sodium tanshinone IIA sulfonate treatment in a rat model of preeclampsia
Bibliographic record
Abstract
Preeclampsia is a disorder of pregnancy with a significant impact on maternal and fetal health. The complexity of this multifactorial condition has precluded development of effective therapies and, although many potential pathways have been investigated, the etiology still requires clarification. Our group has investigated the scavenger lectin-like oxidized LDL (LOX-1) receptor, which may respond to factors released from the distressed placenta that contribute to the vascular pathologies observed in preeclampsia. Given the known beneficial effects of sodium tanshinone IIA sulfonate (STS; a component of Salvia miltiorrhiza) on vasodilation, reduction of oxidative stress, and lipid profiles, we have investigated its role as a potential treatment strategy. We hypothesized that STS would improve vascular endothelial function and, combined with a reduction in oxidative stress, would improve pregnancy outcomes in a rat model of preeclampsia (reduced uteroplacental perfusion pressure, RUPP). We further hypothesized this may occur via the action of STS on the LOX-1 and/or platelet-activating factor (PAF) receptor axes. The RUPP model increased maternal blood pressure, vascular oxidative stress, and involvement of the vascular PAF receptor. Treatment with STS during pregnancy decreased both oxidative stress and involvement of the PAF receptor; however, it also increased involvement of the LOX-1 receptor, which is in line with the concept that scavenger receptors, such as LOX-1 and PAF, are upregulated in response to ligand binding and/or under pathological conditions. In this model of preeclampsia, however, the vascular actions of STS did not lead to improvements in pregnancy outcome such as fetal biometrics or maternal blood pressure.
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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 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 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".