Inhibition of p53 protects placental trophoblast from hydrogen-peroxide induced apoptosis
Bibliographic record
Abstract
Introduction Pre-eclampsia is characterised by exaggerated placental apoptosis and p53 over-expression. This is hypothesised to result from the generation of reactive oxygen species (ROS) secondary to placental ischaemia-reperfusion. Hydrogen peroxide (H2O2) has been used to represent ROS and has been shown to induce apoptosis and p53 expression in placental villous explants. Might inhibition of p53 with pifithrin-α arrest placental damage due to ROS in pre-eclampsia? Methods Term villous explants were exposed to 1000 µM H2O2 and 10 µM pifithrin-α for 48 h (total culture 96 h). Tissue was wax embedded and apoptosis and trophoblast turnover assessed by M30 antibody recognition to cleaved cytokeratin-18 and syncytial knot formation, respectively. Apoptosis was also assessed by caspase-3/7 activity from protein. Real-time PCR was performed for p53, p21, Mdm2, Puma and Bax. Results Treatment with H2O2 increased apoptosis as measured by caspase-3/7 activity (p<0.01, Friedman, n=5) and M30 (p<0.01, Friedman, n=5). Syncytial knot formation was also increased by exposure to H2O2 (p<0.01, Friedman, n=5). Both apoptosis and syncytial knot formation was reduced by treatment with Pifithrin-α. Exposure to H2O2 increased p53, p21 and Puma mRNA expression (p<0.05, Friedman, n=5), but had no effect on Mdm2 or Bax. Again, this increase was reduced with Pifithrin-α. Conclusion Treatment of placental tissue with H2O2 increases apoptosis and gene expression in the p53 pathway. This effect is reduced by treatment with the p53 inhibitor pifithrin-α, suggesting that H2O2 induces p53-mediated apoptosis which is reversible with p53 inhibition. Inhibition of the p53 pathway could be a therapeutic target for placental-related pathologies.
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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 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".