Au-modified ceria nanozyme prevents and treats hypoxia-induced pulmonary hypertension with greatly improved enzymatic activity and safety
Bibliographic record
Abstract
Despite recent advances the prognosis of pulmonary hypertension remains poor and warrants novel therapeutic options. Extensive studies, including ours, have revealed that hypoxia-induced pulmonary hypertension is associated with high oxidative stress. Cerium oxide nanozyme or nanoparticles (CeNPs) have displayed catalytic activity mimicking both catalase and superoxide dismutase functions and have been widely used as an anti-oxidative stress approach. However, whether CeNPs can attenuate hypoxia-induced pulmonary vascular oxidative stress and pulmonary hypertension is unknown. In this study, we designed a new ceria nanozyme or nanoparticle (AuCeNPs) exhibiting enhanced enzyme activity. The AuCeNPs significantly blunted the increase of reactive oxygen species and intracellular calcium concentration while limiting proliferation of pulmonary artery smooth muscle cells and pulmonary vasoconstriction in a model of hypoxia-induced pulmonary hypertension. In addition, the inhalation of nebulized AuCeNPs, but not CeNPs, not only prevented but also blunted hypoxia-induced pulmonary hypertension in rats. The benefits of AuCeNPs were associated with limited increase of intracellular calcium concentration as well as enhancement of extracellular calcium-sensing receptor (CaSR) activity and expression in rat pulmonary artery smooth muscle cells. Nebulised AuCeNPs showed a favorable safety profile, systemic arterial pressure, liver and kidney function, plasma Ca2+ level, and blood biochemical parameters were not affected. We conclude that AuCeNPs is an improved reactive oxygen species scavenger that effectively prevents and treats hypoxia-induced pulmonary hypertension.
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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) | 0.000 | 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".