Moderate hyperoxia plays a protective role in lung bronchial epithelial cells
Bibliographic record
Abstract
PURPOSE: Oxygen therapy is commonly used in clinical settings, but several problems may result from improper use. Oxygen poisoning involves the initiation of a series of inflammatory reactions. In this study, we compared the effects of moderate hyperoxia (40% O2) and extreme hyperoxia (85% O2) on pulmonary bronchial epithelial cells. METHODS: Normal human tracheobronchial epithelium (NHBE) cells were exposed to hyperoxia (40% and 85%) for 24 hours, and their survival rates were determined by the colorimetic assay, MTT (3-(4,5-dimethylthiazol-2-yl)-2,5- diphenyltetrazolium bromide). The protein and mRNA levels of RelA, RelB, ASK1, TNF-α and secretory component (SC) were detected by immunohistochemical staining, western blot, and real-time polymerase chain reaction. RESULTS: The NHBE cell survival increased in the presence of moderate hyperoxia. RelA, RelB, ASK1, TNF-α and SC expressions were significantly higher in the 85% O2 group in comparison with the control group and the 40% O2 group. In the 40% O2 group, RelA, RelB, ASK1 and TNF-α were upregulated, but SC expression was not significantly different than that of the control group. However, compared with the 85% O2 group, SC expression was significantly lower in the 40% O2 group. CONCLUSION: These results suggest that moderate hyperoxia promotes proliferation in NHBE cells and activates TNF-α and downstream ASK1. Then TNF-α activates NF-κB and SC to play a protective role.
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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) | 0.001 | 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".