Long–term lung inflammation is reduced by estradiol treatment in brain dead female rats
Bibliographic record
Abstract
OBJECTIVES: Lung transplantation is limited by the systemic repercussions of brain death (BD). Studies have shown the potential protective role of 17β-estradiol on the lungs. Here, we aimed to investigate the effect of estradiol on the long-lasting lung inflammatory state to understand a possible therapeutic application in lung donors with BD. METHODS: Female Wistar rats were separated into 3 groups: BD, subjected to brain death (6h); E2-T0, treated with 17β-estradiol (50 μg/mL, 2 mL/h) immediately after brain death; and E2-T3, treated with 17β-estradiol (50 μg/ml, 2 ml/h) after 3h of BD. Complement system activity and macrophage presence were analyzed. TNF-α, IL-1β, IL-10, and IL-6 gene expression (RT-PCR) and levels in 24h lung culture medium were quantified. Finally, analysis of caspase-3 gene and protein expression in the lung was performed. RESULTS: Estradiol reduced complement C3 protein and gene expression. The presence of lung macrophages was not modified by estradiol, but the release of inflammatory mediators was reduced and TNF-α and IL-1β gene expression were reduced in the E2-T3 group. In addition, caspase-3 protein expression was reduced by estradiol in the same group. CONCLUSIONS: Brain death-induced lung inflammation in females is modulated by estradiol treatment. Study data suggest that estradiol can control the inflammatory response by modulating the release of mediators after brain death in the long term. These results strengthen the idea of estradiol as a therapy for donor lungs and improving transplant outcomes.
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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) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 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 | 0.001 |
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".