Hyperoxia regulates leukotriene (LT) receptor mRNA expression in the developing rat lung
Bibliographic record
Abstract
Hyperoxia in newborn rats inhibits alveolarization and replicates many of the features of human newborn chronic lung disease (NCLD). Hyperoxia is known to cause an increase in LT expression in the lung and previous studies have demonstrated that LT administration during alveolarization can either arrest (LTB 4 ) or accelerate (LTD 4 ) lung development, suggesting they may be key mediators of this process. Our aim was to assess whether LTB 4 receptor (BLT 1 & BLT 2 ) and LTD 4 receptor (CysLT 1 & CysLT 2 ) mRNA expression is altered by hyperoxia. Methods Rat pups were placed in a normoxic (21% O 2 ) or hyperoxic (>95% O 2 ) environment from postnatal days 4 to 14. Pups were euthanized and lungs harvested on days 4, 6, 9, 12 and 14. Lung total RNA was extracted and analyzed using Real‐Time RT‐PCR. Data were analyzed by two‐way ANOVA. Results The mRNA abundance of BLT 1 and CysLT 2 increased with age (p < 0.001) whilst BLT 2 mRNA abundance decreased (p = 0.001). Each of these effects was accelerated by hyperoxia (p < 0.001). CysLT 1 mRNA abundance also increased with time (p < 0.001) but hyperoxia generally decreased mRNA expression (p < 0.005). Conclusions All four LT receptors demonstrate clear changes in expression during alveolarization supporting the findings of previous research showing that LTs are involved in this process. The present study is the first to show that hyperoxia affects pulmonary LT receptor levels, suggesting that LTs are involved in the disruption of alveolarization caused by high oxygen concentrations and could be linked to NCLD. Supported by the Canadian Institutes of Health Research and the Hospital for Sick Children Foundation.
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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 | 0.001 | 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) | 0.001 | 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".