Plumbagin reverses proliferation and resistance to apoptosis in experimental PAH
Bibliographic record
Abstract
Like cancer, pulmonary arterial hypertension (PAH) is characterised by a pro-proliferative and anti-apoptotic phenotype. In PAH, pulmonary artery smooth muscle cell (PASMC) proliferation is enhanced and apoptosis suppressed. The sustainability of this phenotype requires the activation of pro-survival transcription factors, such as signal transducer and activator of transcription (STAT)3 and nuclear factor of activated T-cells (NFAT). There are no drugs currently available that are able to efficiently and safely inhibit this axis. We hypothesised that plumbagin (PLB), a natural organic compound known to block STAT3 in cancer cells, would reverse experimental pulmonary hypertension. Using human PAH-PASMC, we demonstrated in vitro that PLB inhibits the activation of the STAT3/NFAT axis, increasing the voltage-gated K(+) current bone morphogenetic protein receptor type II (BMPR2), and decreasing intracellular Ca(2+) concentration ([Ca(2+)](i)), rho-associated coiled-coil containing protein kinase (ROCK)1 and interleukin (IL)-6, contributing to the inhibition of PAH-PASMC proliferation and resistance to apoptosis (proliferating cell nuclear antigen (PCNA), TUNEL, Ki67 and anexine V). In vivo, PLB oral administration decreases distal pulmonary artery remodelling, mean pulmonary artery pressure and right ventricular hypertrophy without affecting systemic circulation in both monocrotaline- and suden/chronic hypoxia-induced PAH in rats. This study demonstrates that the STAT3/NFAT axis can be therapeutically targeted by PLB in human PAH-PASMC and experimental PAH rat models. Thus, PLB could be considered a specific and attractive future therapeutic strategy for PAH.
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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) | 0.001 | 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".