[Effects of peroxisome proliferator-activated receptor gamma ligands on monocrotaline-induced pulmonary hypertension in rats].
Bibliographic record
Abstract
Peroxisome proliferator-activated receptor gamma (PPARgamma) is a member of the nuclear hormone receptor superfamily, which regulates transcription of target genes in a ligand-dependent manner. Ligands for PPARgamma have been shown to attenuate proliferation of vascular smooth muscle cells, and to induce apoptosis in several cell lines in vitro. Since monocrotaline (MCT)-induced pulmonary hypertension in rats is characterized by proliferation of pulmonary vascular smooth muscle cells, we hypothesized that PPARgamma ligands may reduce MCT-induced pulmonary hypertension. To test this hypothesis, we treated MCT-injected rats with pioglitazone and troglitazone, synthetic ligands for PPARgamma, for three weeks and measured pulmonary artery pressure and pulmonary vessel wall thickness. TdT-mediated dUTP-biotin nick end labeling (TUNEL) and immunostaining for proliferating cell nuclear antigen (PCNA) were utilized to assess apoptosis and cell proliferation in the pulmonary arterial walls of pioglitazone-treated rats. MCT with pioglitazone or troglitazone treatment significantly reduced pulmonary hypertension and wall thickening of the pulmonary arteries. TUNEL-positive apoptotic cells were not seen in the pulmonary arterial walls of either MCT-injected or control rats with or without pioglitazone. PCNA-positive cells were only seen in the thickened pulmonary arterial walls of MCT rats, but not in the pulmonary arterial walls of controls and of pioglitazone-treated MCT rats. We conclude that PPARgamma ligands reduce MCT-induced pulmonary hypertension and pulmonary vascular wall thickening in rats. Inhibition of MCT-induced cell proliferation in the pulmonary arterial walls may account for this effect
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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) | 0.002 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 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) | 0.004 | 0.001 |
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".