Angiotensin-converting enzyme 2 activation attenuates inflammation and oxidative stress in brain death donor followed by rat lung transplantation
Bibliographic record
Abstract
Brain death (BD) provides most of the donor organs destined for lung transplantation (LTx). However, the organs may be affected by inflammatory and oxidative processes. Based on this, we hypothesize that the angiotensin-converting enzyme 2 (ACE2) activation can reduce the lung injury associated with LTx. 3 h after BD induction, rats were injected with saline ( BD group ) or an ACE2 activator ( ACE2a group ; 15 mg/kg -1 ) and kept on mechanical ventilation for additional 3 h. A third group included a control ventilation ( Control group ) prior to transplant. After BD protocol, left LTx were performed, followed by 2 h-reperfusion. ACE2 activation was associated with better oxygenation after BD management ( p = 0.01), attenuating edema ( p = 0.05) followed by the reduction in tissue resistance ( p = 0.01) and increase of respiratory compliance ( p = 0.02). Nrf2 expression was also upregulated in the ACE2a group ( p = 0.03). After transplantation, ACE2a group showed lower levels of TNF-α ( p = 0.02), IL-6 ( p = 0.001), IL-1β ( p = 0.01), ROS ( p = 0.004) and MDA ( p = 0.002), in addition to higher CAT activity ( p = 0.04). In conclusion, our study suggests that ACE2 activation improves anti-inflammatory and antioxidant activity in a model of LTx.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| 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".